remo more files from images

fix: remove duplicate 'table' object causing clickToExpand translatio…

TRANSLATION_STATUS.md

@@ -0,0 +1,163 @@
+# Translation Status - Beyond CX Analytics
+
+## ✅ Completed Modules
+
+### Agentic Readiness Module
+- **Status:** ✅ **COMPLETED**
+- **Commit:** `b991824`
+- **Files:**
+  - ✅ `frontend/utils/agenticReadinessV2.ts` - All functions, comments, and descriptions translated
+  - ✅ `frontend/components/tabs/AgenticReadinessTab.tsx` - RED_FLAG_CONFIGS and comments translated
+  - ✅ `frontend/locales/en.json` & `es.json` - New subfactors section added
+  - ✅ `backend/beyond_flows/scorers/agentic_score.py` - All docstrings, comments, and reason codes translated
+
+---
+
+## 🔄 Modules Pending Translation
+
+### HIGH PRIORITY - Core Utils (Frontend)
+
+#### 1. **realDataAnalysis.ts**
+- **Lines of Spanish:** ~92 occurrences
+- **Scope:**
+  - Function names: `clasificarTierSimple()`, `clasificarTier()`
+  - 20+ inline comments in Spanish
+  - Function documentation
+- **Impact:** HIGH - Core analysis engine
+- **Estimated effort:** 2-3 hours
+
+#### 2. **analysisGenerator.ts**
+- **Lines of Spanish:** ~49 occurrences
+- **Scope:**
+  - Multiple inline comments
+  - References to `clasificarTierSimple()`
+  - Data transformation comments
+- **Impact:** HIGH - Main data generator
+- **Estimated effort:** 1-2 hours
+
+#### 3. **backendMapper.ts**
+- **Lines of Spanish:** ~13 occurrences
+- **Scope:**
+  - Function documentation
+  - Mapping logic comments
+- **Impact:** MEDIUM - Backend integration
+- **Estimated effort:** 30-60 minutes
+
+---
+
+### MEDIUM PRIORITY - Utilities (Frontend)
+
+#### 4. **dataTransformation.ts**
+- **Lines of Spanish:** ~8 occurrences
+- **Impact:** MEDIUM
+- **Estimated effort:** 30 minutes
+
+#### 5. **segmentClassifier.ts**
+- **Lines of Spanish:** ~3 occurrences
+- **Impact:** LOW
+- **Estimated effort:** 15 minutes
+
+#### 6. **fileParser.ts**
+- **Lines of Spanish:** ~3 occurrences
+- **Impact:** LOW
+- **Estimated effort:** 15 minutes
+
+#### 7. **apiClient.ts**
+- **Lines of Spanish:** ~2 occurrences
+- **Impact:** LOW
+- **Estimated effort:** 10 minutes
+
+#### 8. **serverCache.ts**
+- **Lines of Spanish:** ~2 occurrences
+- **Impact:** LOW
+- **Estimated effort:** 10 minutes
+
+---
+
+### MEDIUM PRIORITY - Backend Dimensions
+
+#### 9. **backend/beyond_metrics/dimensions/OperationalPerformance.py**
+- **Lines of Spanish:** ~7 occurrences
+- **Impact:** MEDIUM
+- **Estimated effort:** 30 minutes
+
+#### 10. **backend/beyond_metrics/dimensions/SatisfactionExperience.py**
+- **Lines of Spanish:** ~8 occurrences
+- **Impact:** MEDIUM
+- **Estimated effort:** 30 minutes
+
+#### 11. **backend/beyond_metrics/dimensions/EconomyCost.py**
+- **Lines of Spanish:** ~4 occurrences
+- **Impact:** MEDIUM
+- **Estimated effort:** 20 minutes
+
+---
+
+### LOW PRIORITY - API & Services
+
+#### 12. **backend/beyond_api/api/analysis.py**
+- **Lines of Spanish:** ~1 occurrence
+- **Impact:** LOW
+- **Estimated effort:** 5 minutes
+
+#### 13. **backend/beyond_api/api/auth.py**
+- **Lines of Spanish:** ~1 occurrence
+- **Impact:** LOW
+- **Estimated effort:** 5 minutes
+
+#### 14. **backend/beyond_api/services/analysis_service.py**
+- **Lines of Spanish:** ~2 occurrences
+- **Impact:** LOW
+- **Estimated effort:** 10 minutes
+
+#### 15. **backend/beyond_metrics/io/base.py**
+- **Lines of Spanish:** ~1 occurrence
+- **Impact:** LOW
+- **Estimated effort:** 5 minutes
+
+#### 16. **backend/beyond_metrics/io/google_drive.py**
+- **Lines of Spanish:** ~2 occurrences
+- **Impact:** LOW
+- **Estimated effort:** 10 minutes
+
+---
+
+## 📊 Summary Statistics
+
+| Category | Files | Total Occurrences | Estimated Time |
+|----------|-------|-------------------|----------------|
+| ✅ Completed | 4 | ~150 | 3 hours (DONE) |
+| 🔴 High Priority | 3 | 154 | 4-6 hours |
+| 🟡 Medium Priority | 8 | 35 | 2-3 hours |
+| 🟢 Low Priority | 5 | 7 | 45 minutes |
+| **TOTAL PENDING** | **16** | **196** | **~8 hours** |
+
+---
+
+## 🎯 Recommended Translation Order
+
+### Phase 1: Critical Path (High Priority)
+1. `realDataAnalysis.ts` - Core analysis engine with `clasificarTier()` functions
+2. `analysisGenerator.ts` - Main data generation orchestrator
+3. `backendMapper.ts` - Backend integration layer
+
+### Phase 2: Supporting Utils (Medium Priority)
+4. `dataTransformation.ts`
+5. Backend dimension files (`OperationalPerformance.py`, `SatisfactionExperience.py`, `EconomyCost.py`)
+
+### Phase 3: Final Cleanup (Low Priority)
+6. Remaining utility files and API services
+
+---
+
+## 📝 Notes
+
+- **Variable names** like `volumen_mes`, `escalación`, etc. in data interfaces should **remain as-is** for API compatibility
+- **Function names** that are part of the public API should be carefully reviewed before renaming
+- **i18n strings** in locales files should continue to have both EN/ES versions
+- **Reason codes** and internal enums should be in English for consistency
+
+---
+
+**Last Updated:** 2026-02-07
+**Status:** agenticReadiness module completed, 16 modules pending

backend/.dockerignore

@@ -11,3 +11,7 @@ build
 data/output
 *.zip
 .DS_Store
+Dockerfile
+docker-compose.yml
+.env
+tests

backend/beyond_api/services/analysis_service.py

@@ -17,11 +17,11 @@ from typing import Any, Mapping, Optional, Dict
 
 def _build_economy_config(economy_data: Optional[Mapping[str, Any]]) -> EconomyConfig:
     """
-    Construye EconomyConfig validando tipos y evitando que el type checker
-    mezcle floats y dicts en un solo diccionario.
+    Builds EconomyConfig validating types and preventing the type checker
+    from mixing floats and dicts in a single dictionary.
     """
 
-    # Valores por defecto
+    # Default values
     default_customer_segments: Dict[str, str] = {
         "VIP": "high",
         "Premium": "high",
@@ -45,9 +45,9 @@ def _build_economy_config(economy_data: Optional[Mapping[str, Any]]) -> EconomyC
         value = economy_data.get(field, default)
         if isinstance(value, (int, float)):
             return float(value)
-        raise ValueError(f"El campo '{field}' debe ser numérico (float). Valor recibido: {value!r}")
+        raise ValueError(f"The field '{field}' must be numeric (float). Received value: {value!r}")
 
-    # Campos escalares
+    # Scalar fields
     labor_cost_per_hour = _get_float("labor_cost_per_hour", 20.0)
     overhead_rate = _get_float("overhead_rate", 0.10)
     tech_costs_annual = _get_float("tech_costs_annual", 5000.0)
@@ -55,16 +55,16 @@ def _build_economy_config(economy_data: Optional[Mapping[str, Any]]) -> EconomyC
     automation_volume_share = _get_float("automation_volume_share", 0.5)
     automation_success_rate = _get_float("automation_success_rate", 0.6)
 
-    # customer_segments puede venir o no; si viene, validarlo
+    # customer_segments may or may not be present; if present, validate it
     customer_segments: Dict[str, str] = dict(default_customer_segments)
     if "customer_segments" in economy_data and economy_data["customer_segments"] is not None:
         cs = economy_data["customer_segments"]
         if not isinstance(cs, Mapping):
-            raise ValueError("customer_segments debe ser un diccionario {segment: level}")
+            raise ValueError("customer_segments must be a dictionary {segment: level}")
         for k, v in cs.items():
             if not isinstance(v, str):
                 raise ValueError(
-                    f"El valor de customer_segments['{k}'] debe ser str. Valor recibido: {v!r}"
+                    f"The value of customer_segments['{k}'] must be str. Received value: {v!r}"
                 )
             customer_segments[str(k)] = v
 
@@ -86,31 +86,31 @@ def run_analysis(
     company_folder: Optional[str] = None,
 ) -> tuple[Path, Optional[Path]]:
     """
-    Ejecuta el pipeline sobre un CSV y devuelve:
-    - (results_dir, None) si return_type == "path"
-    - (results_dir, zip_path) si return_type == "zip"
+    Executes the pipeline on a CSV and returns:
+    - (results_dir, None) if return_type == "path"
+    - (results_dir, zip_path) if return_type == "zip"
 
-    input_path puede ser absoluto o relativo, pero los resultados
-    se escribirán SIEMPRE en la carpeta del CSV, dentro de una
-    subcarpeta con nombre = timestamp (y opcionalmente prefijada
-    por company_folder).
+    input_path can be absolute or relative, but results
+    will ALWAYS be written to the CSV's folder, inside a
+    subfolder named timestamp (and optionally prefixed
+    by company_folder).
     """
 
     input_path = input_path.resolve()
 
     if not input_path.exists():
-        raise FileNotFoundError(f"El CSV no existe: {input_path}")
+        raise FileNotFoundError(f"CSV does not exist: {input_path}")
     if not input_path.is_file():
-        raise ValueError(f"La ruta no apunta a un fichero CSV: {input_path}")
+        raise ValueError(f"Path does not point to a CSV file: {input_path}")
 
-    # Carpeta donde está el CSV
+    # Folder where the CSV is located
     csv_dir = input_path.parent
 
-    # DataSource y ResultsSink apuntan a ESA carpeta
+    # DataSource and ResultsSink point to THAT folder
     datasource = LocalDataSource(base_dir=str(csv_dir))
     sink = LocalResultsSink(base_dir=str(csv_dir))
 
-    # Config de economía
+    # Economy config
     economy_cfg = _build_economy_config(economy_data)
 
     dimension_params: Dict[str, Mapping[str, Any]] = {
@@ -119,13 +119,13 @@ def run_analysis(
         }
     }
 
-    # Callback de scoring
+    # Scoring callback
     def agentic_post_run(results: Dict[str, Any], run_base: str, sink_: ResultsSink) -> None:
         scorer = AgenticScorer()
         try:
             agentic = scorer.compute_and_return(results)
         except Exception as e:
-            # No rompemos toda la ejecución si el scorer falla
+            # Don't break the entire execution if the scorer fails
             agentic = {
                 "error": f"{type(e).__name__}: {e}",
             }
@@ -139,45 +139,45 @@ def run_analysis(
         post_run=[agentic_post_run],
     )
 
-    # Timestamp de ejecución (nombre de la carpeta de resultados)
+    # Execution timestamp (results folder name)
     timestamp = datetime.utcnow().strftime("%Y%m%d-%H%M%S")
 
-    # Ruta lógica de resultados (RELATIVA al base_dir del sink)
+    # Logical results path (RELATIVE to sink's base_dir)
     if company_folder:
-        # Ej: "Cliente_X/20251208-153045"
+        # E.g. "Cliente_X/20251208-153045"
         run_dir_rel = f"{company_folder.rstrip('/')}/{timestamp}"
     else:
-        # Ej: "20251208-153045"
+        # E.g. "20251208-153045"
         run_dir_rel = timestamp
 
-    # Ejecutar pipeline: el CSV se pasa relativo a csv_dir
+    # Execute pipeline: CSV is passed relative to csv_dir
     pipeline.run(
         input_path=input_path.name,
         run_dir=run_dir_rel,
     )
 
-    # Carpeta real con los resultados
+    # Actual folder with results
     results_dir = csv_dir / run_dir_rel
 
     if return_type == "path":
         return results_dir, None
 
-    # --- ZIP de resultados -------------------------------------------------
-    # Creamos el ZIP en la MISMA carpeta del CSV, con nombre basado en run_dir
+    # --- ZIP results -------------------------------------------------------
+    # Create the ZIP in the SAME folder as the CSV, with name based on run_dir
     zip_name = f"{run_dir_rel.replace('/', '_')}.zip"
     zip_path = csv_dir / zip_name
 
     with zipfile.ZipFile(zip_path, "w", zipfile.ZIP_DEFLATED) as zipf:
         for file in results_dir.rglob("*"):
             if file.is_file():
-                # Lo guardamos relativo a la carpeta de resultados
+                # Store it relative to the results folder
                 arcname = file.relative_to(results_dir.parent)
                 zipf.write(file, arcname)
 
     return results_dir, zip_path
 
 
-from typing import Any, Mapping, Dict  # asegúrate de tener estos imports arriba
+from typing import Any, Mapping, Dict  # ensure these imports are at the top
 
 
 def run_analysis_collect_json(
@@ -187,33 +187,33 @@ def run_analysis_collect_json(
     company_folder: Optional[str] = None,
 ) -> Dict[str, Any]:
     """
-    Ejecuta el pipeline y devuelve un único JSON con todos los resultados.
+    Executes the pipeline and returns a single JSON with all results.
 
-    A diferencia de run_analysis:
-    - NO escribe results.json
-    - NO escribe agentic_readiness.json
-    - agentic_readiness se incrusta en el dict de resultados
+    Unlike run_analysis:
+    - Does NOT write results.json
+    - Does NOT write agentic_readiness.json
+    - agentic_readiness is embedded in the results dict
 
-    El parámetro `analysis` permite elegir el nivel de análisis:
+    The `analysis` parameter allows choosing the analysis level:
     - "basic" -> beyond_metrics/configs/basic.json
     - "premium"  -> beyond_metrics/configs/beyond_metrics_config.json
     """
 
-    # Normalizamos y validamos la ruta del CSV
+    # Normalize and validate the CSV path
     input_path = input_path.resolve()
     if not input_path.exists():
-        raise FileNotFoundError(f"El CSV no existe: {input_path}")
+        raise FileNotFoundError(f"CSV does not exist: {input_path}")
     if not input_path.is_file():
-        raise ValueError(f"La ruta no apunta a un fichero CSV: {input_path}")
+        raise ValueError(f"Path does not point to a CSV file: {input_path}")
 
-    # Carpeta donde está el CSV
+    # Folder where the CSV is located
     csv_dir = input_path.parent
 
-    # DataSource y ResultsSink apuntan a ESA carpeta
+    # DataSource and ResultsSink point to THAT folder
     datasource = LocalDataSource(base_dir=str(csv_dir))
     sink = LocalResultsSink(base_dir=str(csv_dir))
 
-    # Config de economía
+    # Economy config
     economy_cfg = _build_economy_config(economy_data)
 
     dimension_params: Dict[str, Mapping[str, Any]] = {
@@ -222,13 +222,13 @@ def run_analysis_collect_json(
         }
     }
 
-    # Elegimos el fichero de configuración de dimensiones según `analysis`
+    # Choose the dimensions config file based on `analysis`
     if analysis == "basic":
         dimensions_config_path = "beyond_metrics/configs/basic.json"
     else: 
         dimensions_config_path = "beyond_metrics/configs/beyond_metrics_config.json"
 
-    # Callback post-run: añadir agentic_readiness al JSON final (sin escribir ficheros)
+    # Post-run callback: add agentic_readiness to the final JSON (without writing files)
     def agentic_post_run(results: Dict[str, Any], run_base: str, sink_: ResultsSink) -> None:
         scorer = AgenticScorer()
         try:
@@ -245,14 +245,14 @@ def run_analysis_collect_json(
         post_run=[agentic_post_run],
     )
 
-    # Timestamp de ejecución (para separar posibles artefactos como plots)
+    # Execution timestamp (to separate possible artifacts like plots)
     timestamp = datetime.utcnow().strftime("%Y%m%d-%H%M%S")
     if company_folder:
         run_dir_rel = f"{company_folder.rstrip('/')}/{timestamp}"
     else:
         run_dir_rel = timestamp
 
-    # Ejecutar pipeline sin escribir results.json
+    # Execute pipeline without writing results.json
     results = pipeline.run(
         input_path=input_path.name,
         run_dir=run_dir_rel,

backend/beyond_flows/scorers/agentic_score.py

@@ -1,22 +1,22 @@
 """
 agentic_score.py
 
-Calcula el Agentic Readiness Score de un contact center a partir
-de un JSON con KPIs agregados (misma estructura que results.json).
+Calculates the Agentic Readiness Score of a contact center from
+a JSON file with aggregated KPIs (same structure as results.json).
 
-Diseñado como clase para integrarse fácilmente en pipelines.
+Designed as a class to integrate easily into pipelines.
 
-Características:
-- Tolerante a datos faltantes: si una dimensión no se puede calcular
-  (porque faltan KPIs), se marca como `computed = False` y no se
-  incluye en el cálculo del score global.
-- La llamada típica en un pipeline será:
+Features:
+- Tolerant to missing data: if a dimension cannot be calculated
+  (due to missing KPIs), it is marked as `computed = False` and not
+  included in the global score calculation.
+- Typical pipeline call:
     from agentic_score import AgenticScorer
     scorer = AgenticScorer()
-    result = scorer.run_on_folder("/ruta/a/carpeta")
+    result = scorer.run_on_folder("/path/to/folder")
 
-Esa carpeta debe contener un `results.json` de entrada.
-El módulo generará un `agentic_readiness.json` en la misma carpeta.
+The folder must contain a `results.json` input file.
+The module will generate an `agentic_readiness.json` in the same folder.
 """
 
 from __future__ import annotations
@@ -35,7 +35,7 @@ Number = Union[int, float]
 # =========================
 
 def _is_nan(x: Any) -> bool:
-    """Devuelve True si x es NaN, None o el string 'NaN'."""
+    """Returns True if x is NaN, None or the string 'NaN'."""
     try:
         if x is None:
             return True
@@ -60,7 +60,7 @@ def _safe_mean(values: Sequence[Optional[Number]]) -> Optional[float]:
 
 
 def _get_nested(d: Dict[str, Any], *keys: str, default: Any = None) -> Any:
-    """Acceso seguro a diccionarios anidados."""
+    """Safe access to nested dictionaries."""
     cur: Any = d
     for k in keys:
         if not isinstance(cur, dict) or k not in cur:
@@ -75,20 +75,20 @@ def _clamp(value: float, lo: float = 0.0, hi: float = 10.0) -> float:
 
 def _normalize_numeric_sequence(field: Any) -> Optional[List[Number]]:
     """
-    Normaliza un campo que representa una secuencia numérica.
+    Normalizes a field representing a numeric sequence.
 
-    Soporta:
-    - Formato antiguo del pipeline: [10, 20, 30]
-    - Formato nuevo del pipeline: {"labels": [...], "values": [10, 20, 30]}
+    Supports:
+    - Old pipeline format: [10, 20, 30]
+    - New pipeline format: {"labels": [...], "values": [10, 20, 30]}
 
-    Devuelve:
-    - lista de números, si hay datos numéricos válidos
-    - None, si el campo no tiene una secuencia numérica interpretable
+    Returns:
+    - list of numbers, if there is valid numeric data
+    - None, if the field does not have an interpretable numeric sequence
     """
     if field is None:
         return None
 
-    # Formato nuevo: {"labels": [...], "values": [...]}
+    # New format: {"labels": [...], "values": [...]}
     if isinstance(field, dict) and "values" in field:
         seq = field.get("values")
     else:
@@ -102,7 +102,7 @@ def _normalize_numeric_sequence(field: Any) -> Optional[List[Number]]:
         if isinstance(v, (int, float)):
             out.append(v)
         else:
-            # Intentamos conversión suave por si viene como string numérico
+            # Try soft conversion in case it's a numeric string
             try:
                 out.append(float(v))
             except (TypeError, ValueError):
@@ -117,21 +117,21 @@ def _normalize_numeric_sequence(field: Any) -> Optional[List[Number]]:
 
 def score_repetitividad(volume_by_skill: Optional[List[Number]]) -> Dict[str, Any]:
     """
-    Repetitividad basada en volumen medio por skill.
+    Repeatability based on average volume per skill.
 
-    Regla (pensada por proceso/skill):
-      - 10 si volumen > 80
-      - 5 si 40–80
-      - 0 si < 40
+    Rule (designed per process/skill):
+      - 10 if volume > 80
+      - 5 if 40–80
+      - 0 if < 40
 
-    Si no hay datos (lista vacía o no numérica), la dimensión
-    se marca como no calculada (computed = False).
+    If there is no data (empty or non-numeric list), the dimension
+    is marked as not calculated (computed = False).
     """
     if not volume_by_skill:
         return {
             "score": None,
             "computed": False,
-            "reason": "sin_datos_volumen",
+            "reason": "no_volume_data",
             "details": {
                 "avg_volume_per_skill": None,
                 "volume_by_skill": volume_by_skill,
@@ -143,7 +143,7 @@ def score_repetitividad(volume_by_skill: Optional[List[Number]]) -> Dict[str, An
         return {
             "score": None,
             "computed": False,
-            "reason": "volumen_no_numerico",
+            "reason": "volume_not_numeric",
             "details": {
                 "avg_volume_per_skill": None,
                 "volume_by_skill": volume_by_skill,
@@ -152,13 +152,13 @@ def score_repetitividad(volume_by_skill: Optional[List[Number]]) -> Dict[str, An
 
     if avg_volume > 80:
         score = 10.0
-        reason = "alto_volumen"
+        reason = "high_volume"
     elif avg_volume >= 40:
         score = 5.0
-        reason = "volumen_medio"
+        reason = "medium_volume"
     else:
         score = 0.0
-        reason = "volumen_bajo"
+        reason = "low_volume"
 
     return {
         "score": score,
@@ -178,36 +178,36 @@ def score_repetitividad(volume_by_skill: Optional[List[Number]]) -> Dict[str, An
 def score_predictibilidad(aht_ratio: Any,
                           escalation_rate: Any) -> Dict[str, Any]:
     """
-    Predictibilidad basada en:
-      - Variabilidad AHT: ratio P90/P50
-      - Tasa de escalación (%)
+    Predictability based on:
+      - AHT variability: ratio P90/P50
+      - Escalation rate (%)
 
-    Regla:
-      - 10 si ratio < 1.5 y escalación < 10%
-      - 5 si ratio 1.5–2.0 o escalación 10–20%
-      - 0 si ratio > 2.0 y escalación > 20%
-      - 3 fallback si datos parciales
+    Rule:
+      - 10 if ratio < 1.5 and escalation < 10%
+      - 5 if ratio 1.5–2.0 or escalation 10–20%
+      - 0 if ratio > 2.0 and escalation > 20%
+      - 3 fallback if data parciales
 
-    Si no hay ni ratio ni escalación, la dimensión no se calcula.
+    If there is no ratio nor escalation, the dimension is not calculated.
     """
     if aht_ratio is None and escalation_rate is None:
         return {
             "score": None,
             "computed": False,
-            "reason": "sin_datos",
+            "reason": "no_data",
             "details": {
                 "aht_p90_p50_ratio": None,
                 "escalation_rate_pct": None,
             },
         }
 
-    # Normalizamos ratio
+    # Normalize ratio
     if aht_ratio is None or _is_nan(aht_ratio):
         ratio: Optional[float] = None
     else:
         ratio = float(aht_ratio)
 
-    # Normalizamos escalación
+    # Normalize escalation
     if escalation_rate is None or _is_nan(escalation_rate):
         esc: Optional[float] = None
     else:
@@ -217,7 +217,7 @@ def score_predictibilidad(aht_ratio: Any,
         return {
             "score": None,
             "computed": False,
-            "reason": "sin_datos",
+            "reason": "no_data",
             "details": {
                 "aht_p90_p50_ratio": None,
                 "escalation_rate_pct": None,
@@ -230,20 +230,20 @@ def score_predictibilidad(aht_ratio: Any,
     if ratio is not None and esc is not None:
         if ratio < 1.5 and esc < 10.0:
             score = 10.0
-            reason = "alta_predictibilidad"
+            reason = "high_predictability"
         elif (1.5 <= ratio <= 2.0) or (10.0 <= esc <= 20.0):
             score = 5.0
-            reason = "predictibilidad_media"
+            reason = "medium_predictability"
         elif ratio > 2.0 and esc > 20.0:
             score = 0.0
-            reason = "baja_predictibilidad"
+            reason = "low_predictability"
         else:
             score = 3.0
-            reason = "caso_intermedio"
+            reason = "intermediate_case"
     else:
-        # Datos parciales: penalizamos pero no ponemos a 0
+        # Partial data: penalize but do not set to 0
         score = 3.0
-        reason = "datos_parciales"
+        reason = "partial_data"
 
     return {
         "score": score,
@@ -263,23 +263,23 @@ def score_predictibilidad(aht_ratio: Any,
 
 def score_estructuracion(channel_distribution_pct: Any) -> Dict[str, Any]:
     """
-    Estructuración de datos usando proxy de canal.
+    Data structuring using channel proxy.
 
-    Asumimos que el canal con mayor % es texto (en proyectos reales se puede
+    We assume the channel with the highest % is text (en proyectos reales se puede
     parametrizar esta asignación).
 
-    Regla:
-      - 10 si texto > 60%
+    Rule:
+      - 10 if text > 60%
       - 5 si 30–60%
       - 0 si < 30%
 
-    Si no hay datos de canales, la dimensión no se calcula.
+    If there is no datas of channels, the dimension is not calculated.
     """
     if not channel_distribution_pct:
         return {
             "score": None,
             "computed": False,
-            "reason": "sin_datos_canal",
+            "reason": "no_channel_data",
             "details": {
                 "estimated_text_share_pct": None,
                 "channel_distribution_pct": channel_distribution_pct,
@@ -299,7 +299,7 @@ def score_estructuracion(channel_distribution_pct: Any) -> Dict[str, Any]:
         return {
             "score": None,
             "computed": False,
-            "reason": "canales_no_numericos",
+            "reason": "channels_not_numeric",
             "details": {
                 "estimated_text_share_pct": None,
                 "channel_distribution_pct": channel_distribution_pct,
@@ -308,13 +308,13 @@ def score_estructuracion(channel_distribution_pct: Any) -> Dict[str, Any]:
 
     if max_share > 60.0:
         score = 10.0
-        reason = "alta_proporcion_texto"
+        reason = "high_text_proportion"
     elif max_share >= 30.0:
         score = 5.0
-        reason = "proporcion_texto_media"
+        reason = "medium_text_proportion"
     else:
         score = 0.0
-        reason = "baja_proporcion_texto"
+        reason = "low_text_proportion"
 
     return {
         "score": score,
@@ -334,9 +334,9 @@ def score_estructuracion(channel_distribution_pct: Any) -> Dict[str, Any]:
 def score_complejidad(aht_ratio: Any,
                       escalation_rate: Any) -> Dict[str, Any]:
     """
-    Complejidad inversa del proceso (0–10).
+    Inverse complexity of the process (0–10).
 
-    1) Base: inversa lineal de la variabilidad AHT (ratio P90/P50):
+    1) Base: linear inverse de la variabilidad AHT (ratio P90/P50):
        - ratio = 1.0 -> 10
        - ratio = 1.5 -> ~7.5
        - ratio = 2.0 -> 5
@@ -345,12 +345,12 @@ def score_complejidad(aht_ratio: Any,
 
        formula_base = (3 - ratio) / (3 - 1) * 10, acotado a [0,10]
 
-    2) Ajuste por escalación:
+    2) Escalation adjustment:
        - restamos (escalation_rate / 5) puntos.
 
-    Nota: más score = proceso más "simple / automatizable".
+    Nota: higher score = process more "simple / automatizable".
 
-    Si no hay ni ratio ni escalación, la dimensión no se calcula.
+    If there is no ratio nor escalation, the dimension is not calculated.
     """
     if aht_ratio is None or _is_nan(aht_ratio):
         ratio: Optional[float] = None
@@ -366,36 +366,36 @@ def score_complejidad(aht_ratio: Any,
         return {
             "score": None,
             "computed": False,
-            "reason": "sin_datos",
+            "reason": "no_data",
             "details": {
                 "aht_p90_p50_ratio": None,
                 "escalation_rate_pct": None,
             },
         }
 
-    # Base por variabilidad
+    # Base for variability
     if ratio is None:
-        base = 5.0  # fallback neutro
-        base_reason = "sin_ratio_usamos_valor_neutro"
+        base = 5.0  # neutral fallback
+        base_reason = "no_ratio_using_neutral_value"
     else:
         base_raw = (3.0 - ratio) / (3.0 - 1.0) * 10.0
         base = _clamp(base_raw)
-        base_reason = "calculado_desde_ratio"
+        base_reason = "calculated_from_ratio"
 
-    # Ajuste por escalación
+    # Escalation adjustment
     if esc is None:
         adj = 0.0
-        adj_reason = "sin_escalacion_sin_ajuste"
+        adj_reason = "no_escalation_no_adjustment"
     else:
-        adj = - (esc / 5.0)  # cada 5 puntos de escalación resta 1
-        adj_reason = "ajuste_por_escalacion"
+        adj = - (esc / 5.0)  # every 5 escalation points subtract 1
+        adj_reason = "escalation_adjustment"
 
     final_score = _clamp(base + adj)
 
     return {
         "score": final_score,
         "computed": True,
-        "reason": "complejidad_inversa",
+        "reason": "inverse_complexity",
         "details": {
             "aht_p90_p50_ratio": ratio,
             "escalation_rate_pct": esc,
@@ -409,21 +409,21 @@ def score_complejidad(aht_ratio: Any,
 
 def score_estabilidad(peak_offpeak_ratio: Any) -> Dict[str, Any]:
     """
-    Estabilidad del proceso basada en relación pico/off-peak.
+    Process stability based on peak/off-peak ratio.
 
-    Regla:
-      - 10 si ratio < 3
+    Rule:
+      - 10 if ratio < 3
       - 7 si 3–5
       - 3 si 5–7
       - 0 si > 7
 
-    Si no hay dato de ratio, la dimensión no se calcula.
+    If there is no data of ratio, the dimension is not calculated.
     """
     if peak_offpeak_ratio is None or _is_nan(peak_offpeak_ratio):
         return {
             "score": None,
             "computed": False,
-            "reason": "sin_datos_peak_offpeak",
+            "reason": "no_peak_offpeak_data",
             "details": {
                 "peak_offpeak_ratio": None,
             },
@@ -432,16 +432,16 @@ def score_estabilidad(peak_offpeak_ratio: Any) -> Dict[str, Any]:
     r = float(peak_offpeak_ratio)
     if r < 3.0:
         score = 10.0
-        reason = "muy_estable"
+        reason = "very_stable"
     elif r < 5.0:
         score = 7.0
-        reason = "estable_moderado"
+        reason = "moderately_stable"
     elif r < 7.0:
         score = 3.0
-        reason = "pico_pronunciado"
+        reason = "pronounced_peak"
     else:
         score = 0.0
-        reason = "muy_inestable"
+        reason = "very_unstable"
 
     return {
         "score": score,
@@ -460,20 +460,20 @@ def score_estabilidad(peak_offpeak_ratio: Any) -> Dict[str, Any]:
 
 def score_roi(annual_savings: Any) -> Dict[str, Any]:
     """
-    ROI potencial anual.
+    Annual potential ROI.
 
-    Regla:
-      - 10 si ahorro > 100k €/año
-      - 5 si 10k–100k €/año
-      - 0 si < 10k €/año
+    Rule:
+      - 10 if savings > 100k €/year
+      - 5 si 10k–100k €/year
+      - 0 si < 10k €/year
 
-    Si no hay dato de ahorro, la dimensión no se calcula.
+    If there is no data of savings, the dimension is not calculated.
     """
     if annual_savings is None or _is_nan(annual_savings):
         return {
             "score": None,
             "computed": False,
-            "reason": "sin_datos_ahorro",
+            "reason": "no_savings_data",
             "details": {
                 "annual_savings_eur": None,
             },
@@ -482,13 +482,13 @@ def score_roi(annual_savings: Any) -> Dict[str, Any]:
     savings = float(annual_savings)
     if savings > 100_000:
         score = 10.0
-        reason = "roi_alto"
+        reason = "high_roi"
     elif savings >= 10_000:
         score = 5.0
-        reason = "roi_medio"
+        reason = "medium_roi"
     else:
         score = 0.0
-        reason = "roi_bajo"
+        reason = "low_roi"
 
     return {
         "score": score,
@@ -506,20 +506,20 @@ def score_roi(annual_savings: Any) -> Dict[str, Any]:
 
 def classify_agentic_score(score: Optional[float]) -> Dict[str, Any]:
     """
-    Clasificación final (alineada con frontend):
-      - ≥6: COPILOT 🤖 (Listo para Copilot)
+    Final classification (aligned with frontend):
+      - ≥6: COPILOT 🤖 (Ready for Copilot)
       - 4–5.99: OPTIMIZE 🔧 (Optimizar Primero)
       - <4: HUMAN 👤 (Requiere Gestión Humana)
 
-    Si score es None (ninguna dimensión disponible), devuelve NO_DATA.
+    If score is None (no dimension available), returns NO_DATA.
     """
     if score is None:
         return {
             "label": "NO_DATA",
             "emoji": "❓",
             "description": (
-                "No se ha podido calcular el Agentic Readiness Score porque "
-                "ninguna de las dimensiones tenía datos suficientes."
+                "Could not calculate the Agentic Readiness Score because "
+                "none of the dimensions had sufficient data."
             ),
         }
 
@@ -527,22 +527,22 @@ def classify_agentic_score(score: Optional[float]) -> Dict[str, Any]:
         label = "COPILOT"
         emoji = "🤖"
         description = (
-            "Listo para Copilot. Procesos con predictibilidad y simplicidad "
-            "suficientes para asistencia IA (sugerencias en tiempo real, autocompletado)."
+            "Ready for Copilot. Processes with sufficient predictability and simplicity "
+            "for AI assistance (real-time suggestions, autocomplete)."
         )
     elif score >= 4.0:
         label = "OPTIMIZE"
         emoji = "🔧"
         description = (
-            "Optimizar primero. Estandarizar procesos y reducir variabilidad "
-            "antes de implementar asistencia IA."
+            "Optimize first. Standardize processes and reduce variability "
+            "before implementing AI assistance."
         )
     else:
         label = "HUMAN"
         emoji = "👤"
         description = (
-            "Requiere gestión humana. Procesos complejos o variables que "
-            "necesitan intervención humana antes de considerar automatización."
+            "Requires human management. Complex or variable processes that "
+            "need human intervention before considering automation."
         )
 
     return {
@@ -604,22 +604,22 @@ class AgenticScorer:
 
     def compute_from_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
         """
-        Calcula el Agentic Readiness Score a partir de un dict de datos.
+        Calculates the Agentic Readiness Score from a data dict.
 
-        Tolerante a datos faltantes: renormaliza pesos usando solo
-        dimensiones con `computed = True`.
+        Tolerant to missing data: renormalizes weights using only
+        dimensions with `computed = True`.
 
-        Compatibilidad con pipeline:
-        - Soporta tanto el formato antiguo:
+        Pipeline compatibility:
+        - Supports both the old format:
             "volume_by_skill": [10, 20, 30]
-        - como el nuevo:
+        - and the new:
             "volume_by_skill": {"labels": [...], "values": [10, 20, 30]}
         """
         volumetry = data.get("volumetry", {})
         op = data.get("operational_performance", {})
         econ = data.get("economy_costs", {})
 
-        # Normalizamos aquí los posibles formatos para contentar al type checker
+        # Normalize here the possible formats for the type checker
         volume_by_skill = _normalize_numeric_sequence(
             volumetry.get("volume_by_skill")
         )
@@ -650,7 +650,7 @@ class AgenticScorer:
             "roi": roi,
         }
 
-        # --- Renormalización de pesos sólo con dimensiones disponibles ---
+        # --- Weight renormalization only with available dimensions ---
         effective_weights: Dict[str, float] = {}
         for name, base_w in self.base_weights.items():
             dim = sub_scores.get(name, {})
@@ -665,7 +665,7 @@ class AgenticScorer:
         else:
             normalized_weights = {}
 
-        # --- Score final ---
+        # --- Final score ---
         if not normalized_weights:
             final_score: Optional[float] = None
         else:
@@ -692,8 +692,8 @@ class AgenticScorer:
                 "metadata": {
                     "source_module": "agentic_score.py",
                     "notes": (
-                        "Modelo simplificado basado en KPIs agregados. "
-                        "Renormaliza los pesos cuando faltan dimensiones."
+                        "Simplified model based on aggregated KPIs. "
+                        "Renormalizes weights when dimensions are missing."
                     ),
                 },
             }
@@ -710,11 +710,11 @@ class AgenticScorer:
 
     def run_on_folder(self, folder_path: Union[str, Path]) -> Dict[str, Any]:
         """
-        Punto de entrada típico para el pipeline:
-        - Lee <folder>/results.json
-        - Calcula Agentic Readiness
-        - Escribe <folder>/agentic_readiness.json
-        - Devuelve el dict con el resultado
+        Typical pipeline entry point:
+        - Reads <folder>/results.json
+        - Calculates Agentic Readiness
+        - Writes <folder>/agentic_readiness.json
+        - Returns the dict with the result
         """
         data = self.load_results(folder_path)
         result = self.compute_from_data(data)

backend/beyond_metrics/agent.py

@@ -14,25 +14,25 @@ from openai import OpenAI
 
 
 DEFAULT_SYSTEM_PROMPT = (
-    "Eres un consultor experto en contact centers. "
-    "Vas a recibir resultados analíticos de un sistema de métricas "
-    "(BeyondMetrics) en formato JSON. Tu tarea es generar un informe claro, "
-    "accionable y orientado a negocio, destacando los principales hallazgos, "
-    "riesgos y oportunidades de mejora."
+    "You are an expert contact center consultant. "
+    "You will receive analytical results from a metrics system "
+    "(BeyondMetrics) in JSON format. Your task is to generate a clear, "
+    "actionable, business-oriented report, highlighting the main findings, "
+    "risks, and opportunities for improvement."
 )
 
 
 @dataclass
 class ReportAgentConfig:
     """
-    Configuración básica del agente de informes.
+    Basic configuration for the report agent.
 
     openai_api_key:
-      Se puede pasar explícitamente o leer de la variable de entorno OPENAI_API_KEY.
+      Can be passed explicitly or read from the OPENAI_API_KEY environment variable.
     model:
-      Modelo de ChatGPT a utilizar, p.ej. 'gpt-4.1-mini' o similar.
+      ChatGPT model to use, e.g. 'gpt-4.1-mini' or similar.
     system_prompt:
-      Prompt de sistema para controlar el estilo del informe.
+      System prompt to control the report style.
     """
 
     openai_api_key: Optional[str] = None
@@ -42,15 +42,15 @@ class ReportAgentConfig:
 
 class BeyondMetricsReportAgent:
     """
-    Agente muy sencillo que:
+    Simple agent that:
 
-    1) Lee el JSON de resultados de una ejecución de BeyondMetrics.
-    2) Construye un prompt con esos resultados.
-    3) Llama a ChatGPT para generar un informe en texto.
-    4) Guarda el informe en un PDF en disco, EMBEBIENDO las imágenes PNG
-       generadas por el pipeline como anexos.
+    1) Reads the JSON results from a BeyondMetrics execution.
+    2) Builds a prompt with those results.
+    3) Calls ChatGPT to generate a text report.
+    4) Saves the report to a PDF on disk, EMBEDDING the PNG images
+       generated by the pipeline as attachments.
 
-    MVP: centrado en texto + figuras incrustadas.
+    MVP: focused on text + embedded figures.
     """
 
     def __init__(self, config: Optional[ReportAgentConfig] = None) -> None:
@@ -59,16 +59,16 @@ class BeyondMetricsReportAgent:
         api_key = self.config.openai_api_key or os.getenv("OPENAI_API_KEY")
         if not api_key:
             raise RuntimeError(
-                "Falta la API key de OpenAI. "
-                "Pásala en ReportAgentConfig(openai_api_key=...) o "
-                "define la variable de entorno OPENAI_API_KEY."
+                "Missing OpenAI API key. "
+                "Pass it in ReportAgentConfig(openai_api_key=...) or "
+                "define the OPENAI_API_KEY environment variable."
             )
 
-        # Cliente de la nueva API de OpenAI
+        # New OpenAI API client
         self._client = OpenAI(api_key=api_key)
 
     # ------------------------------------------------------------------
-    # API pública principal
+    # Main public API
     # ------------------------------------------------------------------
     def generate_pdf_report(
         self,
@@ -77,48 +77,48 @@ class BeyondMetricsReportAgent:
         extra_user_prompt: str = "",
     ) -> str:
         """
-        Genera un informe en PDF a partir de una carpeta de resultados.
+        Generates a PDF report from a results folder.
 
-        Parámetros:
+        Parameters:
         - run_base:
-            Carpeta base de la ejecución. Debe contener al menos 'results.json'
-            y, opcionalmente, imágenes PNG generadas por el pipeline.
+            Base folder for the execution. Must contain at least 'results.json'
+            and, optionally, PNG images generated by the pipeline.
         - output_pdf_path:
-            Ruta completa del PDF de salida. Si es None, se crea
-            'beyondmetrics_report.pdf' dentro de run_base.
+            Full path for the output PDF. If None, creates
+            'beyondmetrics_report.pdf' inside run_base.
         - extra_user_prompt:
-            Texto adicional para afinar la petición al agente
-            (p.ej. "enfatiza eficiencia y SLA", etc.)
+            Additional text to refine the agent's request
+            (e.g. "emphasize efficiency and SLA", etc.)
 
-        Devuelve:
-        - La ruta del PDF generado.
+        Returns:
+        - The path to the generated PDF.
         """
         run_dir = Path(run_base)
         results_json = run_dir / "results.json"
         if not results_json.exists():
             raise FileNotFoundError(
-                f"No se ha encontrado {results_json}. "
-                "Asegúrate de ejecutar primero el pipeline."
+                f"{results_json} not found. "
+                "Make sure to run the pipeline first."
             )
 
-        # 1) Leer JSON de resultados
+        # 1) Read results JSON
         with results_json.open("r", encoding="utf-8") as f:
             results_data: Dict[str, Any] = json.load(f)
 
-        # 2) Buscar imágenes generadas
+        # 2) Find generated images
         image_files = sorted(p for p in run_dir.glob("*.png"))
 
-        # 3) Construir prompt de usuario
+        # 3) Build user prompt
         user_prompt = self._build_user_prompt(
             results=results_data,
             image_files=[p.name for p in image_files],
             extra_user_prompt=extra_user_prompt,
         )
 
-        # 4) Llamar a ChatGPT para obtener el texto del informe
+        # 4) Call ChatGPT to get the report text
         report_text = self._call_chatgpt(user_prompt)
 
-        # 5) Crear PDF con texto + imágenes embebidas
+        # 5) Create PDF with text + embedded images
         if output_pdf_path is None:
             output_pdf_path = str(run_dir / "beyondmetrics_report.pdf")
 
@@ -127,7 +127,7 @@ class BeyondMetricsReportAgent:
         return output_pdf_path
 
     # ------------------------------------------------------------------
-    # Construcción del prompt
+    # Prompt construction
     # ------------------------------------------------------------------
     def _build_user_prompt(
         self,
@@ -136,34 +136,34 @@ class BeyondMetricsReportAgent:
         extra_user_prompt: str = "",
     ) -> str:
         """
-        Construye el mensaje de usuario que se enviará al modelo.
-        Para un MVP, serializamos el JSON de resultados entero.
-        Más adelante se puede resumir si el JSON crece demasiado.
+        Builds the user message to be sent to the model.
+        For an MVP, we serialize the entire results JSON.
+        Later, this can be summarized if the JSON grows too large.
         """
         results_str = json.dumps(results, indent=2, ensure_ascii=False)
 
         images_section = (
-            "Imágenes generadas en la ejecución:\n"
+            "Images generated in the execution:\n"
             + "\n".join(f"- {name}" for name in image_files)
             if image_files
-            else "No se han generado imágenes en esta ejecución."
+            else "No images were generated in this execution."
         )
 
         extra = (
-            f"\n\nInstrucciones adicionales del usuario:\n{extra_user_prompt}"
+            f"\n\nAdditional user instructions:\n{extra_user_prompt}"
             if extra_user_prompt
             else ""
         )
 
         prompt = (
-            "A continuación te proporciono los resultados de una ejecución de BeyondMetrics "
-            "en formato JSON. Debes elaborar un INFORME EJECUTIVO para un cliente de "
-            "contact center. El informe debe incluir:\n"
-            "- Resumen ejecutivo en lenguaje de negocio.\n"
-            "- Principales hallazgos por dimensión.\n"
-            "- Riesgos o problemas detectados.\n"
-            "- Recomendaciones accionables.\n\n"
-            "Resultados (JSON):\n"
+            "Below I provide you with the results of a BeyondMetrics execution "
+            "in JSON format. You must produce an EXECUTIVE REPORT for a contact "
+            "center client. The report should include:\n"
+            "- Executive summary in business language.\n"
+            "- Main findings by dimension.\n"
+            "- Detected risks or issues.\n"
+            "- Actionable recommendations.\n\n"
+            "Results (JSON):\n"
             f"{results_str}\n\n"
             f"{images_section}"
             f"{extra}"
@@ -172,12 +172,12 @@ class BeyondMetricsReportAgent:
         return prompt
 
     # ------------------------------------------------------------------
-    # Llamada a ChatGPT (nueva API)
+    # ChatGPT call (new API)
     # ------------------------------------------------------------------
     def _call_chatgpt(self, user_prompt: str) -> str:
         """
-        Llama al modelo de ChatGPT y devuelve el contenido del mensaje de respuesta.
-        Implementado con la nueva API de OpenAI.
+        Calls the ChatGPT model and returns the content of the response message.
+        Implemented with the new OpenAI API.
         """
         resp = self._client.chat.completions.create(
             model=self.config.model,
@@ -190,11 +190,11 @@ class BeyondMetricsReportAgent:
 
         content = resp.choices[0].message.content
         if not isinstance(content, str):
-            raise RuntimeError("La respuesta del modelo no contiene texto.")
+            raise RuntimeError("The model response does not contain text.")
         return content
 
     # ------------------------------------------------------------------
-    # Escritura de PDF (texto + imágenes)
+    # PDF writing (text + images)
     # ------------------------------------------------------------------
     def _write_pdf(
         self,
@@ -203,11 +203,11 @@ class BeyondMetricsReportAgent:
         image_paths: Sequence[Path],
     ) -> None:
         """
-        Crea un PDF A4 con:
+        Creates an A4 PDF with:
 
-        1) Texto del informe (páginas iniciales).
-        2) Una sección de anexos donde se incrustan las imágenes PNG
-           generadas por el pipeline, escaladas para encajar en la página.
+        1) Report text (initial pages).
+        2) An appendix section where the PNG images generated by the
+           pipeline are embedded, scaled to fit the page.
         """
         output_path = str(output_path)
         c = canvas.Canvas(output_path, pagesize=A4)
@@ -220,7 +220,7 @@ class BeyondMetricsReportAgent:
 
         c.setFont("Helvetica", 11)
 
-        # --- Escribir texto principal ---
+        # --- Write main text ---
         def _wrap_line(line: str, max_chars: int = 100) -> list[str]:
             parts: list[str] = []
             current: list[str] = []
@@ -248,37 +248,37 @@ class BeyondMetricsReportAgent:
                 c.drawString(margin_x, y, line)
                 y -= line_height
 
-        # --- Anexar imágenes como figuras ---
+        # --- Append images as figures ---
         if image_paths:
-            # Nueva página para las figuras
+            # New page for figures
             c.showPage()
             c.setFont("Helvetica-Bold", 14)
-            c.drawString(margin_x, height - margin_y, "Anexo: Figuras")
+            c.drawString(margin_x, height - margin_y, "Appendix: Figures")
             c.setFont("Helvetica", 11)
 
             current_y = height - margin_y - 2 * line_height
 
             for img_path in image_paths:
-                # Si no cabe la imagen en la página, pasamos a la siguiente
+                # If the image doesn't fit on the page, move to the next one
                 available_height = current_y - margin_y
-                if available_height < 100:  # espacio mínimo
+                if available_height < 100:  # minimum space
                     c.showPage()
                     c.setFont("Helvetica-Bold", 14)
-                    c.drawString(margin_x, height - margin_y, "Anexo: Figuras (cont.)")
+                    c.drawString(margin_x, height - margin_y, "Appendix: Figures (cont.)")
                     c.setFont("Helvetica", 11)
                     current_y = height - margin_y - 2 * line_height
                     available_height = current_y - margin_y
 
-                # Título de la figura
-                title = f"Figura: {img_path.name}"
+                # Figure title
+                title = f"Figure: {img_path.name}"
                 c.drawString(margin_x, current_y, title)
                 current_y -= line_height
 
-                # Cargar imagen y escalarla
+                # Load and scale image
                 try:
                     img = ImageReader(str(img_path))
                     iw, ih = img.getSize()
-                    # Escala para encajar en ancho y alto disponibles
+                    # Scale to fit available width and height
                     max_img_height = available_height - 2 * line_height
                     scale = min(max_width / iw, max_img_height / ih)
                     if scale <= 0:
@@ -302,8 +302,8 @@ class BeyondMetricsReportAgent:
 
                     current_y = y_img - 2 * line_height
                 except Exception as e:
-                    # Si falla la carga, lo indicamos en el PDF
-                    err_msg = f"No se pudo cargar la imagen {img_path.name}: {e}"
+                    # If loading fails, indicate it in the PDF
+                    err_msg = f"Could not load image {img_path.name}: {e}"
                     c.drawString(margin_x, current_y, err_msg)
                     current_y -= 2 * line_height
 

backend/beyond_metrics/dimensions/EconomyCost.py

@@ -23,17 +23,16 @@ REQUIRED_COLUMNS_ECON: List[str] = [
 @dataclass
 class EconomyConfig:
     """
-    Parámetros manuales para la dimensión de Economía y Costes.
+    Manual parameters for the Economy and Cost dimension.
 
-    - labor_cost_per_hour: coste total/hora de un agente (fully loaded).
-    - overhead_rate: % overhead variable (ej. 0.1 = 10% sobre labor).
-    - tech_costs_annual: coste anual de tecnología (licencias, infra, ...).
-    - automation_cpi: coste por interacción automatizada (ej. 0.15€).
-    - automation_volume_share: % del volumen automatizable (0-1).
-    - automation_success_rate: % éxito de la automatización (0-1).
+    - labor_cost_per_hour: total cost/hour of an agent (fully loaded).
+    - overhead_rate: % variable overhead (e.g. 0.1 = 10% over labor).
+    - tech_costs_annual: annual technology cost (licenses, infrastructure, ...).
+    - automation_cpi: cost per automated interaction (e.g. 0.15€).
+    - automation_volume_share: % of automatable volume (0-1).
+    - automation_success_rate: % automation success (0-1).
 
-    - customer_segments: mapping opcional skill -> segmento ("high"/"medium"/"low")
-      para futuros insights de ROI por segmento.
+    - customer_segments: optional mapping skill -> segment ("high"/"medium"/"low") for future ROI insights by segment.
     """
 
     labor_cost_per_hour: float
@@ -48,20 +47,20 @@ class EconomyConfig:
 @dataclass
 class EconomyCostMetrics:
     """
-    DIMENSIÓN 4: ECONOMÍA y COSTES
+    DIMENSION 4: ECONOMY and COSTS
 
-    Propósito:
-      - Cuantificar el COSTE actual (CPI, coste anual).
-      - Estimar el impacto de overhead y tecnología.
-      - Calcular un primer estimado de "coste de ineficiencia" y ahorro potencial.
+    Purpose:
+      - Quantify the current COST (CPI, annual cost).
+      - Estimate the impact of overhead and technology.
+      - Calculate an initial estimate of "inefficiency cost" and potential savings.
 
-    Requiere:
-      - Columnas del dataset transaccional (ver REQUIRED_COLUMNS_ECON).
+    Requires:
+      - Columns from the transactional dataset (see REQUIRED_COLUMNS_ECON).
 
-    Inputs opcionales vía EconomyConfig:
-      - labor_cost_per_hour (obligatorio para cualquier cálculo de €).
+    Optional inputs via EconomyConfig:
+      - labor_cost_per_hour (required for any € calculation).
       - overhead_rate, tech_costs_annual, automation_*.
-      - customer_segments (para insights de ROI por segmento).
+      - customer_segments (for ROI insights by segment).
     """
 
     df: pd.DataFrame
@@ -72,13 +71,13 @@ class EconomyCostMetrics:
         self._prepare_data()
 
     # ------------------------------------------------------------------ #
-    # Helpers internos
+    # Internal helpers
     # ------------------------------------------------------------------ #
     def _validate_columns(self) -> None:
         missing = [c for c in REQUIRED_COLUMNS_ECON if c not in self.df.columns]
         if missing:
             raise ValueError(
-                f"Faltan columnas obligatorias para EconomyCostMetrics: {missing}"
+                f"Missing required columns for EconomyCostMetrics: {missing}"
             )
 
     def _prepare_data(self) -> None:
@@ -97,15 +96,15 @@ class EconomyCostMetrics:
             df["duration_talk"].fillna(0)
             + df["hold_time"].fillna(0)
             + df["wrap_up_time"].fillna(0)
-        )  # segundos
+        )  # seconds
 
-        # Filtrar por record_status para cálculos de AHT/CPI
-        # Solo incluir registros VALID (excluir NOISE, ZOMBIE, ABANDON)
+        # Filter by record_status for AHT/CPI calculations
+        # Only include VALID records (exclude NOISE, ZOMBIE, ABANDON)
         if "record_status" in df.columns:
             df["record_status"] = df["record_status"].astype(str).str.strip().str.upper()
             df["_is_valid_for_cost"] = df["record_status"] == "VALID"
         else:
-            # Legacy data sin record_status: incluir todo
+            # Legacy data without record_status: include all
             df["_is_valid_for_cost"] = True
 
         self.df = df
@@ -118,11 +117,11 @@ class EconomyCostMetrics:
         return self.config is not None and self.config.labor_cost_per_hour is not None
 
     # ------------------------------------------------------------------ #
-    # KPI 1: CPI por canal/skill
+    # KPI 1: CPI by channel/skill
     # ------------------------------------------------------------------ #
     def cpi_by_skill_channel(self) -> pd.DataFrame:
         """
-        CPI (Coste Por Interacción) por skill/canal.
+        CPI (Cost Per Interaction) by skill/channel.
 
         CPI = (Labor_cost_per_interaction + Overhead_variable) / EFFECTIVE_PRODUCTIVITY
 
@@ -130,19 +129,17 @@ class EconomyCostMetrics:
         - Overhead_variable = overhead_rate * Labor_cost_per_interaction
         - EFFECTIVE_PRODUCTIVITY = 0.70 (70% - accounts for non-productive time)
 
-        Excluye registros abandonados del cálculo de costes para consistencia
-        con el path del frontend (fresh CSV).
+        Excludes abandoned records from cost calculation for consistency with the frontend path (fresh CSV).
 
-        Si no hay config de costes -> devuelve DataFrame vacío.
+        If there is no cost config -> returns empty DataFrame.
 
-        Incluye queue_skill y channel como columnas (no solo índice) para que
-        el frontend pueda hacer lookup por nombre de skill.
+        Includes queue_skill and channel as columns (not just index) so that the frontend can lookup by skill name.
         """
         if not self._has_cost_config():
             return pd.DataFrame()
 
         cfg = self.config
-        assert cfg is not None  # para el type checker
+        assert cfg is not None  # for the type checker
 
         df = self.df.copy()
         if df.empty:
@@ -154,15 +151,15 @@ class EconomyCostMetrics:
         else:
             df_cost = df
 
-        # Filtrar por record_status: solo VALID para cálculo de AHT
-        # Excluye NOISE, ZOMBIE, ABANDON
+        # Filter by record_status: only VALID for AHT calculation
+        # Excludes NOISE, ZOMBIE, ABANDON
         if "_is_valid_for_cost" in df_cost.columns:
             df_cost = df_cost[df_cost["_is_valid_for_cost"] == True]
 
         if df_cost.empty:
             return pd.DataFrame()
 
-        # AHT por skill/canal (en segundos) - solo registros VALID
+        # AHT by skill/channel (in seconds) - only VALID records
         grouped = df_cost.groupby(["queue_skill", "channel"])["handle_time"].mean()
 
         if grouped.empty:
@@ -193,17 +190,16 @@ class EconomyCostMetrics:
         return out.sort_index().reset_index()
 
     # ------------------------------------------------------------------ #
-    # KPI 2: coste anual por skill/canal
+    # KPI 2: annual cost by skill/channel
     # ------------------------------------------------------------------ #
     def annual_cost_by_skill_channel(self) -> pd.DataFrame:
         """
-        Coste anual por skill/canal.
+        Annual cost by skill/channel.
 
-        cost_annual = CPI * volumen (cantidad de interacciones de la muestra).
+        cost_annual = CPI * volume (number of interactions in the sample).
 
-        Nota: por simplicidad asumimos que el dataset refleja un periodo anual.
-        Si en el futuro quieres anualizar (ej. dataset = 1 mes) se puede añadir
-        un factor de escalado en EconomyConfig.
+        Note: for simplicity we assume the dataset reflects an annual period.
+        If in the future you want to annualize (e.g. dataset = 1 month) you can add a scaling factor in EconomyConfig.
         """
         cpi_table = self.cpi_by_skill_channel()
         if cpi_table.empty:
@@ -224,18 +220,18 @@ class EconomyCostMetrics:
         return joined
 
     # ------------------------------------------------------------------ #
-    # KPI 3: desglose de costes (labor / tech / overhead)
+    # KPI 3: cost breakdown (labor / tech / overhead)
     # ------------------------------------------------------------------ #
     def cost_breakdown(self) -> Dict[str, float]:
         """
-        Desglose % de costes: labor, overhead, tech.
+        Cost breakdown %: labor, overhead, tech.
 
         labor_total = sum(labor_cost_per_interaction)
         overhead_total = labor_total * overhead_rate
-        tech_total = tech_costs_annual (si se ha proporcionado)
+        tech_total = tech_costs_annual (if provided)
 
-        Devuelve porcentajes sobre el total.
-        Si falta configuración de coste -> devuelve {}.
+        Returns percentages of the total.
+        If cost configuration is missing -> returns {}.
         """
         if not self._has_cost_config():
             return {}
@@ -258,7 +254,7 @@ class EconomyCostMetrics:
         cpi_indexed = cpi_table.set_index(["queue_skill", "channel"])
         joined = cpi_indexed.join(volume, how="left").fillna({"volume": 0})
 
-        # Costes anuales de labor y overhead
+        # Annual labor and overhead costs
         annual_labor = (joined["labor_cost"] * joined["volume"]).sum()
         annual_overhead = (joined["overhead_cost"] * joined["volume"]).sum()
         annual_tech = cfg.tech_costs_annual
@@ -278,21 +274,21 @@ class EconomyCostMetrics:
         }
 
     # ------------------------------------------------------------------ #
-    # KPI 4: coste de ineficiencia (€ por variabilidad/escalación)
+    # KPI 4: inefficiency cost (€ by variability/escalation)
     # ------------------------------------------------------------------ #
     def inefficiency_cost_by_skill_channel(self) -> pd.DataFrame:
         """
-        Estimación muy simplificada de coste de ineficiencia:
+        Very simplified estimate of inefficiency cost:
 
-        Para cada skill/canal:
+        For each skill/channel:
 
-          - AHT_p50, AHT_p90 (segundos).
+          - AHT_p50, AHT_p90 (seconds).
           - Delta = max(0, AHT_p90 - AHT_p50).
-          - Se asume que ~40% de las interacciones están por encima de la mediana.
+          - Assumes that ~40% of interactions are above the median.
           - Ineff_seconds = Delta * volume * 0.4
           - Ineff_cost = LaborCPI_per_second * Ineff_seconds
 
-        NOTA: Es un modelo aproximado para cuantificar "orden de magnitud".
+        NOTE: This is an approximate model to quantify "order of magnitude".
         """
         if not self._has_cost_config():
             return pd.DataFrame()
@@ -302,8 +298,8 @@ class EconomyCostMetrics:
 
         df = self.df.copy()
 
-        # Filtrar por record_status: solo VALID para cálculo de AHT
-        # Excluye NOISE, ZOMBIE, ABANDON
+        # Filter by record_status: only VALID for AHT calculation
+        # Excludes NOISE, ZOMBIE, ABANDON
         if "_is_valid_for_cost" in df.columns:
             df = df[df["_is_valid_for_cost"] == True]
 
@@ -318,7 +314,7 @@ class EconomyCostMetrics:
         if stats.empty:
             return pd.DataFrame()
 
-        # CPI para obtener coste/segundo de labor
+        # CPI to get cost/second of labor
         # cpi_by_skill_channel now returns with reset_index, so we need to set index for join
         cpi_table_raw = self.cpi_by_skill_channel()
         if cpi_table_raw.empty:
@@ -331,11 +327,11 @@ class EconomyCostMetrics:
         merged = merged.fillna(0.0)
 
         delta = (merged["aht_p90"] - merged["aht_p50"]).clip(lower=0.0)
-        affected_fraction = 0.4  # aproximación
+        affected_fraction = 0.4  # approximation
         ineff_seconds = delta * merged["volume"] * affected_fraction
 
-        # labor_cost = coste por interacción con AHT medio;
-        # aproximamos coste/segundo como labor_cost / AHT_medio
+        # labor_cost = cost per interaction with average AHT;
+        # approximate cost/second as labor_cost / average_AHT
         aht_mean = grouped["handle_time"].mean()
         merged["aht_mean"] = aht_mean
 
@@ -351,21 +347,21 @@ class EconomyCostMetrics:
         return merged[["aht_p50", "aht_p90", "volume", "ineff_seconds", "ineff_cost"]].reset_index()
 
     # ------------------------------------------------------------------ #
-    # KPI 5: ahorro potencial anual por automatización
+    # KPI 5: potential annual savings from automation
     # ------------------------------------------------------------------ #
     def potential_savings(self) -> Dict[str, Any]:
         """
-        Ahorro potencial anual basado en:
+        Potential annual savings based on:
 
-        Ahorro = (CPI_humano - CPI_automatizado) * Volumen_automatizable * Tasa_éxito
+        Savings = (Human_CPI - Automated_CPI) * Automatable_volume * Success_rate
 
-        Donde:
-        - CPI_humano = media ponderada de cpi_total.
-        - CPI_automatizado = config.automation_cpi
-        - Volumen_automatizable = volume_total * automation_volume_share
-        - Tasa_éxito = automation_success_rate
+        Where:
+        - Human_CPI = weighted average of cpi_total.
+        - Automated_CPI = config.automation_cpi
+        - Automatable_volume = volume_total * automation_volume_share
+        - Success_rate = automation_success_rate
 
-        Si faltan parámetros en config -> devuelve {}.
+        If config parameters are missing -> returns {}.
         """
         if not self._has_cost_config():
             return {}
@@ -384,7 +380,7 @@ class EconomyCostMetrics:
         if total_volume <= 0:
             return {}
 
-        # CPI humano medio ponderado
+        # Weighted average human CPI
         weighted_cpi = (
             (cpi_table["cpi_total"] * cpi_table["volume"]).sum() / total_volume
         )
@@ -409,12 +405,12 @@ class EconomyCostMetrics:
     # ------------------------------------------------------------------ #
     def plot_cost_waterfall(self) -> Axes:
         """
-        Waterfall de costes anuales (labor + tech + overhead).
+        Waterfall of annual costs (labor + tech + overhead).
         """
         breakdown = self.cost_breakdown()
         if not breakdown:
             fig, ax = plt.subplots()
-            ax.text(0.5, 0.5, "Sin configuración de costes", ha="center", va="center")
+            ax.text(0.5, 0.5, "No cost configuration", ha="center", va="center")
             ax.set_axis_off()
             return ax
 
@@ -436,14 +432,14 @@ class EconomyCostMetrics:
             bottoms.append(running)
             running += v
 
-        # barras estilo waterfall
+        # waterfall style bars
         x = np.arange(len(labels))
         ax.bar(x, values)
 
         ax.set_xticks(x)
         ax.set_xticklabels(labels)
-        ax.set_ylabel("€ anuales")
-        ax.set_title("Desglose anual de costes")
+        ax.set_ylabel("€ annual")
+        ax.set_title("Annual cost breakdown")
 
         for idx, v in enumerate(values):
             ax.text(idx, v, f"{v:,.0f}", ha="center", va="bottom")
@@ -454,12 +450,12 @@ class EconomyCostMetrics:
 
     def plot_cpi_by_channel(self) -> Axes:
         """
-        Gráfico de barras de CPI medio por canal.
+        Bar chart of average CPI by channel.
         """
         cpi_table = self.cpi_by_skill_channel()
         if cpi_table.empty:
             fig, ax = plt.subplots()
-            ax.text(0.5, 0.5, "Sin configuración de costes", ha="center", va="center")
+            ax.text(0.5, 0.5, "No cost configuration", ha="center", va="center")
             ax.set_axis_off()
             return ax
 
@@ -474,7 +470,7 @@ class EconomyCostMetrics:
         cpi_indexed = cpi_table.set_index(["queue_skill", "channel"])
         joined = cpi_indexed.join(volume, how="left").fillna({"volume": 0})
 
-        # CPI medio ponderado por canal
+        # Weighted average CPI by channel
         per_channel = (
             joined.reset_index()
             .groupby("channel")
@@ -486,9 +482,9 @@ class EconomyCostMetrics:
         fig, ax = plt.subplots(figsize=(6, 4))
         per_channel.plot(kind="bar", ax=ax)
 
-        ax.set_xlabel("Canal")
-        ax.set_ylabel("CPI medio (€)")
-        ax.set_title("Coste por interacción (CPI) por canal")
+        ax.set_xlabel("Channel")
+        ax.set_ylabel("Average CPI (€)")
+        ax.set_title("Cost per interaction (CPI) by channel")
         ax.grid(axis="y", alpha=0.3)
 
         return ax

backend/beyond_metrics/dimensions/OperationalPerformance.py

@@ -25,32 +25,31 @@ REQUIRED_COLUMNS_OP: List[str] = [
 @dataclass
 class OperationalPerformanceMetrics:
     """
-    Dimensión: RENDIMIENTO OPERACIONAL Y DE SERVICIO
+    Dimension: OPERATIONAL PERFORMANCE AND SERVICE
 
-    Propósito: medir el balance entre rapidez (eficiencia) y calidad de resolución,
-    más la variabilidad del servicio.
+    Purpose: measure the balance between speed (efficiency) and resolution quality, plus service variability.
 
-    Requiere como mínimo:
+    Requires at minimum:
     - interaction_id
     - datetime_start
     - queue_skill
     - channel
-    - duration_talk (segundos)
-    - hold_time (segundos)
-    - wrap_up_time (segundos)
+    - duration_talk (seconds)
+    - hold_time (seconds)
+    - wrap_up_time (seconds)
     - agent_id
     - transfer_flag (bool/int)
 
-    Columnas opcionales:
-    - is_resolved (bool/int)      -> para FCR
-    - abandoned_flag (bool/int)   -> para tasa de abandono
-    - customer_id / caller_id     -> para reincidencia y repetición de canal
-    - logged_time (segundos)      -> para occupancy_rate
+    Optional columns:
+    - is_resolved (bool/int)      -> for FCR
+    - abandoned_flag (bool/int)   -> for abandonment rate
+    - customer_id / caller_id     -> for recurrence and channel repetition
+    - logged_time (seconds)      -> for occupancy_rate
     """
 
     df: pd.DataFrame
 
-    # Benchmarks / parámetros de normalización (puedes ajustarlos)
+    # Benchmarks / normalization parameters (you can adjust them)
     AHT_GOOD: float = 300.0     # 5 min
     AHT_BAD: float = 900.0      # 15 min
     VAR_RATIO_GOOD: float = 1.2 # P90/P50 ~1.2 muy estable
@@ -61,19 +60,19 @@ class OperationalPerformanceMetrics:
         self._prepare_data()
 
     # ------------------------------------------------------------------ #
-    # Helpers internos
+    # Internal helpers
     # ------------------------------------------------------------------ #
     def _validate_columns(self) -> None:
         missing = [c for c in REQUIRED_COLUMNS_OP if c not in self.df.columns]
         if missing:
             raise ValueError(
-                f"Faltan columnas obligatorias para OperationalPerformanceMetrics: {missing}"
+                f"Missing required columns for OperationalPerformanceMetrics: {missing}"
             )
 
     def _prepare_data(self) -> None:
         df = self.df.copy()
 
-        # Tipos
+        # Types
         df["datetime_start"] = pd.to_datetime(df["datetime_start"], errors="coerce")
 
         for col in ["duration_talk", "hold_time", "wrap_up_time"]:
@@ -86,13 +85,13 @@ class OperationalPerformanceMetrics:
             + df["wrap_up_time"].fillna(0)
         )
 
-        # v3.0: Filtrar NOISE y ZOMBIE para cálculos de variabilidad
+        # v3.0: Filter NOISE and ZOMBIE for variability calculations
         # record_status: 'VALID', 'NOISE', 'ZOMBIE', 'ABANDON'
-        # Para AHT/CV solo usamos 'VALID' (excluye noise, zombie, abandon)
+        # For AHT/CV we only use 'VALID' (excludes noise, zombie, abandon)
         if "record_status" in df.columns:
             df["record_status"] = df["record_status"].astype(str).str.strip().str.upper()
-            # Crear máscara para registros válidos: SOLO "VALID"
-            # Excluye explícitamente NOISE, ZOMBIE, ABANDON y cualquier otro valor
+            # Create mask for valid records: ONLY "VALID"
+            # Explicitly excludes NOISE, ZOMBIE, ABANDON and any other value
             df["_is_valid_for_cv"] = df["record_status"] == "VALID"
 
             # Log record_status breakdown for debugging
@@ -104,21 +103,21 @@ class OperationalPerformanceMetrics:
                 print(f"   - {status}: {count}")
             print(f"   VALID rows for AHT calculation: {valid_count}")
         else:
-            # Legacy data sin record_status: incluir todo
+            # Legacy data without record_status: include all
             df["_is_valid_for_cv"] = True
             print(f"[OperationalPerformance] No record_status column - using all {len(df)} rows")
 
-        # Normalización básica
+        # Basic normalization
         df["queue_skill"] = df["queue_skill"].astype(str).str.strip()
         df["channel"] = df["channel"].astype(str).str.strip()
         df["agent_id"] = df["agent_id"].astype(str).str.strip()
 
-        # Flags opcionales convertidos a bool cuando existan
+        # Optional flags converted to bool when they exist
         for flag_col in ["is_resolved", "abandoned_flag", "transfer_flag"]:
             if flag_col in df.columns:
                 df[flag_col] = df[flag_col].astype(int).astype(bool)
 
-        # customer_id: usamos customer_id si existe, si no caller_id
+        # customer_id: we use customer_id if it exists, otherwise caller_id
         if "customer_id" in df.columns:
             df["customer_id"] = df["customer_id"].astype(str)
         elif "caller_id" in df.columns:
@@ -126,8 +125,8 @@ class OperationalPerformanceMetrics:
         else:
             df["customer_id"] = None
 
-        # logged_time opcional
-        # Normalizamos logged_time: siempre será una serie float con NaN si no existe
+        # logged_time optional
+        # Normalize logged_time: will always be a float series with NaN if it does not exist
         df["logged_time"] = pd.to_numeric(df.get("logged_time", np.nan), errors="coerce")
 
 
@@ -138,16 +137,16 @@ class OperationalPerformanceMetrics:
         return self.df.empty
 
     # ------------------------------------------------------------------ #
-    # AHT y variabilidad
+    # AHT and variability
     # ------------------------------------------------------------------ #
     def aht_distribution(self) -> Dict[str, float]:
         """
-        Devuelve P10, P50, P90 del AHT y el ratio P90/P50 como medida de variabilidad.
+        Returns P10, P50, P90 of AHT and the P90/P50 ratio as a measure of variability.
 
-        v3.0: Filtra NOISE y ZOMBIE para el cálculo de variabilidad.
-        Solo usa registros con record_status='valid' o sin status (legacy).
+        v3.0: Filters NOISE and ZOMBIE for variability calculation.
+        Only uses records with record_status='valid' or without status (legacy).
         """
-        # Filtrar solo registros válidos para cálculo de variabilidad
+        # Filter only valid records for variability calculation
         df_valid = self.df[self.df["_is_valid_for_cv"] == True]
         ht = df_valid["handle_time"].dropna().astype(float)
         if ht.empty:
@@ -167,10 +166,9 @@ class OperationalPerformanceMetrics:
 
     def talk_hold_acw_p50_by_skill(self) -> pd.DataFrame:
         """
-        P50 de talk_time, hold_time y wrap_up_time por skill.
+        P50 of talk_time, hold_time and wrap_up_time by skill.
 
-        Incluye queue_skill como columna (no solo índice) para que
-        el frontend pueda hacer lookup por nombre de skill.
+        Includes queue_skill as a column (not just index) so that the frontend can lookup by skill name.
         """
         df = self.df
 
@@ -192,24 +190,24 @@ class OperationalPerformanceMetrics:
         return result.round(2).sort_index().reset_index()
 
     # ------------------------------------------------------------------ #
-    # FCR, escalación, abandono, reincidencia, repetición canal
+    # FCR, escalation, abandonment, recurrence, channel repetition
     # ------------------------------------------------------------------ #
     def fcr_rate(self) -> float:
         """
         FCR (First Contact Resolution).
 
-        Prioridad 1: Usar fcr_real_flag del CSV si existe
-        Prioridad 2: Calcular como 100 - escalation_rate
+        Priority 1: Use fcr_real_flag from CSV if it exists
+        Priority 2: Calculate as 100 - escalation_rate
         """
         df = self.df
         total = len(df)
         if total == 0:
             return float("nan")
 
-        # Prioridad 1: Usar fcr_real_flag si existe
+        # Priority 1: Use fcr_real_flag if it exists
         if "fcr_real_flag" in df.columns:
             col = df["fcr_real_flag"]
-            # Normalizar a booleano
+            # Normalize to boolean
             if col.dtype == "O":
                 fcr_mask = (
                     col.astype(str)
@@ -224,7 +222,7 @@ class OperationalPerformanceMetrics:
             fcr = (fcr_count / total) * 100.0
             return float(max(0.0, min(100.0, round(fcr, 2))))
 
-        # Prioridad 2: Fallback a 100 - escalation_rate
+        # Priority 2: Fallback to 100 - escalation_rate
         try:
             esc = self.escalation_rate()
         except Exception:
@@ -239,7 +237,7 @@ class OperationalPerformanceMetrics:
 
     def escalation_rate(self) -> float:
         """
-        % de interacciones que requieren escalación (transfer_flag == True).
+        % of interactions that require escalation (transfer_flag == True).
         """
         df = self.df
         total = len(df)
@@ -251,17 +249,17 @@ class OperationalPerformanceMetrics:
 
     def abandonment_rate(self) -> float:
         """
-        % de interacciones abandonadas.
+        % of abandoned interactions.
 
-        Busca en orden: is_abandoned, abandoned_flag, abandoned
-        Si ninguna columna existe, devuelve NaN.
+        Searches in order: is_abandoned, abandoned_flag, abandoned
+        If no column exists, returns NaN.
         """
         df = self.df
         total = len(df)
         if total == 0:
             return float("nan")
 
-        # Buscar columna de abandono en orden de prioridad
+        # Search for abandonment column in priority order
         abandon_col = None
         for col_name in ["is_abandoned", "abandoned_flag", "abandoned"]:
             if col_name in df.columns:
@@ -273,7 +271,7 @@ class OperationalPerformanceMetrics:
 
         col = df[abandon_col]
 
-        # Normalizar a booleano
+        # Normalize to boolean
         if col.dtype == "O":
             abandon_mask = (
                 col.astype(str)
@@ -289,10 +287,9 @@ class OperationalPerformanceMetrics:
 
     def high_hold_time_rate(self, threshold_seconds: float = 60.0) -> float:
         """
-        % de interacciones con hold_time > threshold (por defecto 60s).
+        % of interactions with hold_time > threshold (default 60s).
 
-        Proxy de complejidad: si el agente tuvo que poner en espera al cliente
-        más de 60 segundos, probablemente tuvo que consultar/investigar.
+        Complexity proxy: if the agent had to put the customer on hold for more than 60 seconds, they probably had to consult/investigate.
         """
         df = self.df
         total = len(df)
@@ -306,44 +303,43 @@ class OperationalPerformanceMetrics:
 
     def recurrence_rate_7d(self) -> float:
         """
-        % de clientes que vuelven a contactar en < 7 días para el MISMO skill.
+        % of customers who contact again in < 7 days for the SAME skill.
 
-        Se basa en customer_id (o caller_id si no hay customer_id) + queue_skill.
-        Calcula:
-        - Para cada combinación cliente + skill, ordena por datetime_start
-        - Si hay dos contactos consecutivos separados < 7 días (mismo cliente, mismo skill),
-          cuenta como "recurrente"
-        - Tasa = nº clientes recurrentes / nº total de clientes
+        Based on customer_id (or caller_id if no customer_id) + queue_skill.
+        Calculates:
+        - For each client + skill combination, sorts by datetime_start
+        - If there are two consecutive contacts separated by < 7 days (same client, same skill), counts as "recurrent"
+        - Rate = number of recurrent clients / total number of clients
 
-        NOTA: Solo cuenta como recurrencia si el cliente llama por el MISMO skill.
-        Un cliente que llama a "Ventas" y luego a "Soporte" NO es recurrente.
+        NOTE: Only counts as recurrence if the client calls for the SAME skill.
+        A client who calls "Sales" and then "Support" is NOT recurrent.
         """
 
         df = self.df.dropna(subset=["datetime_start"]).copy()
 
-        # Normalizar identificador de cliente
+        # Normalize client identifier
         if "customer_id" not in df.columns:
             if "caller_id" in df.columns:
                 df["customer_id"] = df["caller_id"]
             else:
-                # No hay identificador de cliente -> no se puede calcular
+                # No client identifier -> cannot calculate
                 return float("nan")
 
         df = df.dropna(subset=["customer_id"])
         if df.empty:
             return float("nan")
 
-        # Ordenar por cliente + skill + fecha
+        # Sort by client + skill + date
         df = df.sort_values(["customer_id", "queue_skill", "datetime_start"])
 
-        # Diferencia de tiempo entre contactos consecutivos por cliente Y skill
-        # Esto asegura que solo contamos recontactos del mismo cliente para el mismo skill
+        # Time difference between consecutive contacts by client AND skill
+        # This ensures we only count re-contacts from the same client for the same skill
         df["delta"] = df.groupby(["customer_id", "queue_skill"])["datetime_start"].diff()
 
-        # Marcamos los contactos que ocurren a menos de 7 días del anterior (mismo skill)
+        # Mark contacts that occur less than 7 days from the previous one (same skill)
         recurrence_mask = df["delta"] < pd.Timedelta(days=7)
 
-        # Nº de clientes que tienen al menos un contacto recurrente (para cualquier skill)
+        # Number of clients who have at least one recurrent contact (for any skill)
         recurrent_customers = df.loc[recurrence_mask, "customer_id"].nunique()
         total_customers = df["customer_id"].nunique()
 
@@ -356,9 +352,9 @@ class OperationalPerformanceMetrics:
 
     def repeat_channel_rate(self) -> float:
         """
-        % de reincidencias (<7 días) en las que el cliente usa el MISMO canal.
+        % of recurrences (<7 days) in which the client uses the SAME channel.
 
-        Si no hay customer_id/caller_id o solo un contacto por cliente, devuelve NaN.
+        If there is no customer_id/caller_id or only one contact per client, returns NaN.
         """
         df = self.df.dropna(subset=["datetime_start"]).copy()
         if df["customer_id"].isna().all():
@@ -387,11 +383,11 @@ class OperationalPerformanceMetrics:
     # ------------------------------------------------------------------ #
     def occupancy_rate(self) -> float:
         """
-        Tasa de ocupación:
+        Occupancy rate:
 
         occupancy = sum(handle_time) / sum(logged_time) * 100.
 
-        Requiere columna 'logged_time'. Si no existe o es todo 0, devuelve NaN.
+        Requires 'logged_time' column. If it does not exist or is all 0, returns NaN.
         """
         df = self.df
         if "logged_time" not in df.columns:
@@ -408,23 +404,23 @@ class OperationalPerformanceMetrics:
         return float(round(occ * 100, 2))
 
     # ------------------------------------------------------------------ #
-    # Score de rendimiento 0-10
+    # Performance score 0-10
     # ------------------------------------------------------------------ #
     def performance_score(self) -> Dict[str, float]:
         """
-        Calcula un score 0-10 combinando:
-        - AHT (bajo es mejor)
-        - FCR (alto es mejor)
-        - Variabilidad (P90/P50, bajo es mejor)
-        - Otros factores (ocupación / escalación)
+        Calculates a 0-10 score combining:
+        - AHT (lower is better)
+        - FCR (higher is better)
+        - Variability (P90/P50, lower is better)
+        - Other factors (occupancy / escalation)
 
-        Fórmula:
+        Formula:
         score = 0.4 * (10 - AHT_norm) +
                 0.3 * FCR_norm +
                 0.2 * (10 - Var_norm) +
                 0.1 * Otros_score
 
-        Donde *_norm son valores en escala 0-10.
+        Where *_norm are values on a 0-10 scale.
         """
         dist = self.aht_distribution()
         if not dist:
@@ -433,15 +429,15 @@ class OperationalPerformanceMetrics:
         p50 = dist["p50"]
         ratio = dist["p90_p50_ratio"]
 
-        # AHT_normalized: 0 (mejor) a 10 (peor)
+        # AHT_normalized: 0 (better) to 10 (worse)
         aht_norm = self._scale_to_0_10(p50, self.AHT_GOOD, self.AHT_BAD)
-        # FCR_normalized: 0-10 directamente desde % (0-100)
+        # FCR_normalized: 0-10 directly from % (0-100)
         fcr_pct = self.fcr_rate()
         fcr_norm = fcr_pct / 10.0 if not np.isnan(fcr_pct) else 0.0
-        # Variabilidad_normalized: 0 (ratio bueno) a 10 (ratio malo)
+        # Variability_normalized: 0 (good ratio) to 10 (bad ratio)
         var_norm = self._scale_to_0_10(ratio, self.VAR_RATIO_GOOD, self.VAR_RATIO_BAD)
 
-        # Otros factores: combinamos ocupación (ideal ~80%) y escalación (ideal baja)
+        # Other factors: combine occupancy (ideal ~80%) and escalation (ideal low)
         occ = self.occupancy_rate()
         esc = self.escalation_rate()
 
@@ -467,26 +463,26 @@ class OperationalPerformanceMetrics:
 
     def _scale_to_0_10(self, value: float, good: float, bad: float) -> float:
         """
-        Escala linealmente un valor:
+        Linearly scales a value:
         - good -> 0
         - bad  -> 10
-        Con saturación fuera de rango.
+        With saturation outside range.
         """
         if np.isnan(value):
-            return 5.0  # neutro
+            return 5.0  # neutral
 
         if good == bad:
             return 5.0
 
         if good < bad:
-            # Menor es mejor
+            # Lower is better
             if value <= good:
                 return 0.0
             if value >= bad:
                 return 10.0
             return 10.0 * (value - good) / (bad - good)
         else:
-            # Mayor es mejor
+            # Higher is better
             if value >= good:
                 return 0.0
             if value <= bad:
@@ -495,19 +491,19 @@ class OperationalPerformanceMetrics:
 
     def _compute_other_factors_score(self, occ_pct: float, esc_pct: float) -> float:
         """
-        Otros factores (0-10) basados en:
-        - ocupación ideal alrededor de 80%
-        - tasa de escalación ideal baja (<10%)
+        Other factors (0-10) based on:
+        - ideal occupancy around 80%
+        - ideal escalation rate low (<10%)
         """
-        # Ocupación: 0 penalización si está entre 75-85, se penaliza fuera
+        # Occupancy: 0 penalty if between 75-85, penalized outside
         if np.isnan(occ_pct):
             occ_penalty = 5.0
         else:
             deviation = abs(occ_pct - 80.0)
-            occ_penalty = min(10.0, deviation / 5.0 * 2.0)  # cada 5 puntos se suman 2, máx 10
+            occ_penalty = min(10.0, deviation / 5.0 * 2.0)  # each 5 points add 2, max 10
         occ_score = max(0.0, 10.0 - occ_penalty)
 
-        # Escalación: 0-10 donde 0% -> 10 puntos, >=40% -> 0
+        # Escalation: 0-10 where 0% -> 10 points, >=40% -> 0
         if np.isnan(esc_pct):
             esc_score = 5.0
         else:
@@ -518,7 +514,7 @@ class OperationalPerformanceMetrics:
             else:
                 esc_score = 10.0 * (1.0 - esc_pct / 40.0)
 
-        # Media simple de ambos
+        # Simple average of both
         return (occ_score + esc_score) / 2.0
 
     # ------------------------------------------------------------------ #
@@ -526,29 +522,29 @@ class OperationalPerformanceMetrics:
     # ------------------------------------------------------------------ #
     def plot_aht_boxplot_by_skill(self) -> Axes:
         """
-        Boxplot del AHT por skill (P10-P50-P90 visual).
+        Boxplot of AHT by skill (P10-P50-P90 visual).
         """
         df = self.df.copy()
 
         if df.empty or "handle_time" not in df.columns:
             fig, ax = plt.subplots()
-            ax.text(0.5, 0.5, "Sin datos de AHT", ha="center", va="center")
+            ax.text(0.5, 0.5, "No AHT data", ha="center", va="center")
             ax.set_axis_off()
             return ax
 
         df = df.dropna(subset=["handle_time"])
         if df.empty:
             fig, ax = plt.subplots()
-            ax.text(0.5, 0.5, "AHT no disponible", ha="center", va="center")
+            ax.text(0.5, 0.5, "AHT not available", ha="center", va="center")
             ax.set_axis_off()
             return ax
 
         fig, ax = plt.subplots(figsize=(8, 4))
         df.boxplot(column="handle_time", by="queue_skill", ax=ax, showfliers=False)
 
-        ax.set_xlabel("Skill / Cola")
-        ax.set_ylabel("AHT (segundos)")
-        ax.set_title("Distribución de AHT por skill")
+        ax.set_xlabel("Skill / Queue")
+        ax.set_ylabel("AHT (seconds)")
+        ax.set_title("AHT distribution by skill")
         plt.suptitle("")
         plt.xticks(rotation=45, ha="right")
         ax.grid(axis="y", alpha=0.3)
@@ -557,14 +553,14 @@ class OperationalPerformanceMetrics:
 
     def plot_resolution_funnel_by_skill(self) -> Axes:
         """
-        Funnel / barras apiladas de Talk + Hold + ACW por skill (P50).
+        Funnel / stacked bars of Talk + Hold + ACW by skill (P50).
 
-        Permite ver el equilibrio de tiempos por skill.
+        Allows viewing the time balance by skill.
         """
         p50 = self.talk_hold_acw_p50_by_skill()
         if p50.empty:
             fig, ax = plt.subplots()
-            ax.text(0.5, 0.5, "Sin datos para funnel", ha="center", va="center")
+            ax.text(0.5, 0.5, "No data for funnel", ha="center", va="center")
             ax.set_axis_off()
             return ax
 
@@ -583,27 +579,26 @@ class OperationalPerformanceMetrics:
 
         ax.set_xticks(x)
         ax.set_xticklabels(skills, rotation=45, ha="right")
-        ax.set_ylabel("Segundos")
-        ax.set_title("Funnel de resolución (P50) por skill")
+        ax.set_ylabel("Seconds")
+        ax.set_title("Resolution funnel (P50) by skill")
         ax.legend()
         ax.grid(axis="y", alpha=0.3)
 
         return ax
 
     # ------------------------------------------------------------------ #
-    # Métricas por skill (para consistencia frontend cached/fresh)
+    # Metrics by skill (for frontend cached/fresh consistency)
     # ------------------------------------------------------------------ #
     def metrics_by_skill(self) -> List[Dict[str, Any]]:
         """
-        Calcula métricas operacionales por skill:
-        - transfer_rate: % de interacciones con transfer_flag == True
-        - abandonment_rate: % de interacciones abandonadas
-        - fcr_tecnico: 100 - transfer_rate (sin transferencia)
-        - fcr_real: % sin transferencia Y sin recontacto 7d (si hay datos)
-        - volume: número de interacciones
+        Calculates operational metrics by skill:
+        - transfer_rate: % of interactions with transfer_flag == True
+        - abandonment_rate: % of abandoned interactions
+        - fcr_tecnico: 100 - transfer_rate (without transfer)
+        - fcr_real: % without transfer AND without 7d re-contact (if there is data)
+        - volume: number of interactions
 
-        Devuelve una lista de dicts, uno por skill, para que el frontend
-        tenga acceso a las métricas reales por skill (no estimadas).
+        Returns a list of dicts, one per skill, so that the frontend has access to real metrics by skill (not estimated).
         """
         df = self.df
         if df.empty:
@@ -611,14 +606,14 @@ class OperationalPerformanceMetrics:
 
         results = []
 
-        # Detectar columna de abandono
+        # Detect abandonment column
         abandon_col = None
         for col_name in ["is_abandoned", "abandoned_flag", "abandoned"]:
             if col_name in df.columns:
                 abandon_col = col_name
                 break
 
-        # Detectar columna de repeat_call_7d para FCR real
+        # Detect repeat_call_7d column for real FCR
         repeat_col = None
         for col_name in ["repeat_call_7d", "repeat_7d", "is_repeat_7d"]:
             if col_name in df.columns:
@@ -637,7 +632,7 @@ class OperationalPerformanceMetrics:
             else:
                 transfer_rate = 0.0
 
-            # FCR Técnico = 100 - transfer_rate
+            # Technical FCR = 100 - transfer_rate
             fcr_tecnico = float(round(100.0 - transfer_rate, 2))
 
             # Abandonment rate
@@ -656,7 +651,7 @@ class OperationalPerformanceMetrics:
                 abandoned = int(abandon_mask.sum())
                 abandonment_rate = float(round(abandoned / total * 100, 2))
 
-            # FCR Real (sin transferencia Y sin recontacto 7d)
+            # Real FCR (without transfer AND without 7d re-contact)
             fcr_real = fcr_tecnico  # default to fcr_tecnico if no repeat data
             if repeat_col and "transfer_flag" in group.columns:
                 repeat_data = group[repeat_col]
@@ -670,13 +665,13 @@ class OperationalPerformanceMetrics:
                 else:
                     repeat_mask = pd.to_numeric(repeat_data, errors="coerce").fillna(0) > 0
 
-                # FCR Real: no transfer AND no repeat
+                # Real FCR: no transfer AND no repeat
                 fcr_real_mask = (~group["transfer_flag"]) & (~repeat_mask)
                 fcr_real_count = fcr_real_mask.sum()
                 fcr_real = float(round(fcr_real_count / total * 100, 2))
 
-            # AHT Mean (promedio de handle_time sobre registros válidos)
-            # Filtramos solo registros 'valid' (excluye noise/zombie) para consistencia
+            # AHT Mean (average of handle_time over valid records)
+            # Filter only 'valid' records (excludes noise/zombie) for consistency
             if "_is_valid_for_cv" in group.columns:
                 valid_records = group[group["_is_valid_for_cv"]]
             else:
@@ -687,15 +682,15 @@ class OperationalPerformanceMetrics:
             else:
                 aht_mean = 0.0
 
-            # AHT Total (promedio de handle_time sobre TODOS los registros)
-            # Incluye NOISE, ZOMBIE, ABANDON - solo para información/comparación
+            # AHT Total (average of handle_time over ALL records)
+            # Includes NOISE, ZOMBIE, ABANDON - for information/comparison only
             if len(group) > 0 and "handle_time" in group.columns:
                 aht_total = float(round(group["handle_time"].mean(), 2))
             else:
                 aht_total = 0.0
 
-            # Hold Time Mean (promedio de hold_time sobre registros válidos)
-            # Consistente con fresh path que usa MEAN, no P50
+            # Hold Time Mean (average of hold_time over valid records)
+            # Consistent with fresh path that uses MEAN, not P50
             if len(valid_records) > 0 and "hold_time" in valid_records.columns:
                 hold_time_mean = float(round(valid_records["hold_time"].mean(), 2))
             else:

backend/beyond_metrics/dimensions/SatisfactionExperience.py

@@ -24,11 +24,10 @@ REQUIRED_COLUMNS_SAT: List[str] = [
 @dataclass
 class SatisfactionExperienceMetrics:
     """
-    Dimensión 3: SATISFACCIÓN y EXPERIENCIA
+    Dimension 3: SATISFACTION and EXPERIENCE
 
-    Todas las columnas de satisfacción (csat/nps/ces/aht) son OPCIONALES.
-    Si no están, las métricas que las usan devuelven vacío/NaN pero
-    nunca rompen el pipeline.
+    All satisfaction columns (csat/nps/ces/aht) are OPTIONAL.
+    If they are not present, the metrics that use them return empty/NaN but never break the pipeline.
     """
 
     df: pd.DataFrame
@@ -44,7 +43,7 @@ class SatisfactionExperienceMetrics:
         missing = [c for c in REQUIRED_COLUMNS_SAT if c not in self.df.columns]
         if missing:
             raise ValueError(
-                f"Faltan columnas obligatorias para SatisfactionExperienceMetrics: {missing}"
+                f"Missing required columns for SatisfactionExperienceMetrics: {missing}"
             )
 
     def _prepare_data(self) -> None:
@@ -52,7 +51,7 @@ class SatisfactionExperienceMetrics:
 
         df["datetime_start"] = pd.to_datetime(df["datetime_start"], errors="coerce")
 
-        # Duraciones base siempre existen
+        # Base durations always exist
         for col in ["duration_talk", "hold_time", "wrap_up_time"]:
             df[col] = pd.to_numeric(df[col], errors="coerce")
 
@@ -63,16 +62,16 @@ class SatisfactionExperienceMetrics:
             + df["wrap_up_time"].fillna(0)
         )
 
-        # csat_score opcional
+        # csat_score optional
         df["csat_score"] = pd.to_numeric(df.get("csat_score", np.nan), errors="coerce")
 
-        # aht opcional: si existe columna explícita la usamos, si no usamos handle_time
+        # aht optional: if explicit column exists we use it, otherwise we use handle_time
         if "aht" in df.columns:
             df["aht"] = pd.to_numeric(df["aht"], errors="coerce")
         else:
             df["aht"] = df["handle_time"]
 
-        # NPS / CES opcionales
+        # NPS / CES optional
         df["nps_score"] = pd.to_numeric(df.get("nps_score", np.nan), errors="coerce")
         df["ces_score"] = pd.to_numeric(df.get("ces_score", np.nan), errors="coerce")
 
@@ -90,8 +89,8 @@ class SatisfactionExperienceMetrics:
     # ------------------------------------------------------------------ #
     def csat_avg_by_skill_channel(self) -> pd.DataFrame:
         """
-        CSAT promedio por skill/canal.
-        Si no hay csat_score, devuelve DataFrame vacío.
+        Average CSAT by skill/channel.
+        If there is no csat_score, returns empty DataFrame.
         """
         df = self.df
         if "csat_score" not in df.columns or df["csat_score"].notna().sum() == 0:
@@ -115,7 +114,7 @@ class SatisfactionExperienceMetrics:
 
     def nps_avg_by_skill_channel(self) -> pd.DataFrame:
         """
-        NPS medio por skill/canal, si existe nps_score.
+        Average NPS by skill/channel, if nps_score exists.
         """
         df = self.df
         if "nps_score" not in df.columns or df["nps_score"].notna().sum() == 0:
@@ -139,7 +138,7 @@ class SatisfactionExperienceMetrics:
 
     def ces_avg_by_skill_channel(self) -> pd.DataFrame:
         """
-        CES medio por skill/canal, si existe ces_score.
+        Average CES by skill/channel, if ces_score exists.
         """
         df = self.df
         if "ces_score" not in df.columns or df["ces_score"].notna().sum() == 0:
@@ -163,11 +162,11 @@ class SatisfactionExperienceMetrics:
     
     def csat_global(self) -> float:
         """
-        CSAT medio global (todas las interacciones).
+        Global average CSAT (all interactions).
 
-        Usa la columna opcional `csat_score`:
-        - Si no existe, devuelve NaN.
-        - Si todos los valores son NaN / vacíos, devuelve NaN.
+        Uses the optional `csat_score` column:
+        - If it does not exist, returns NaN.
+        - If all values are NaN / empty, returns NaN.
         """
         df = self.df
         if "csat_score" not in df.columns:
@@ -183,8 +182,8 @@ class SatisfactionExperienceMetrics:
 
     def csat_aht_correlation(self) -> Dict[str, Any]:
         """
-        Correlación Pearson CSAT vs AHT.
-        Si falta csat o aht, o no hay varianza, devuelve NaN y código adecuado.
+        Pearson correlation CSAT vs AHT.
+        If csat or aht is missing, or there is no variance, returns NaN and appropriate code.
         """
         df = self.df
         if "csat_score" not in df.columns or df["csat_score"].notna().sum() == 0:
@@ -216,8 +215,8 @@ class SatisfactionExperienceMetrics:
 
     def csat_aht_skill_summary(self) -> pd.DataFrame:
         """
-        Resumen por skill con clasificación del "sweet spot".
-        Si falta csat o aht, devuelve DataFrame vacío.
+        Summary by skill with "sweet spot" classification.
+        If csat or aht is missing, returns empty DataFrame.
         """
         df = self.df
         if df["csat_score"].notna().sum() == 0 or df["aht"].notna().sum() == 0:
@@ -258,20 +257,20 @@ class SatisfactionExperienceMetrics:
     # ------------------------------------------------------------------ #
     def plot_csat_vs_aht_scatter(self) -> Axes:
         """
-        Scatter CSAT vs AHT por skill.
-        Si no hay datos suficientes, devuelve un Axes con mensaje.
+        Scatter CSAT vs AHT by skill.
+        If there is insufficient data, returns an Axes with message.
         """
         df = self.df
         if df["csat_score"].notna().sum() == 0 or df["aht"].notna().sum() == 0:
             fig, ax = plt.subplots()
-            ax.text(0.5, 0.5, "Sin datos de CSAT/AHT", ha="center", va="center")
+            ax.text(0.5, 0.5, "No CSAT/AHT data", ha="center", va="center")
             ax.set_axis_off()
             return ax
 
         df = df.dropna(subset=["csat_score", "aht"]).copy()
         if df.empty:
             fig, ax = plt.subplots()
-            ax.text(0.5, 0.5, "Sin datos de CSAT/AHT", ha="center", va="center")
+            ax.text(0.5, 0.5, "No CSAT/AHT data", ha="center", va="center")
             ax.set_axis_off()
             return ax
 
@@ -280,9 +279,9 @@ class SatisfactionExperienceMetrics:
         for skill, sub in df.groupby("queue_skill"):
             ax.scatter(sub["aht"], sub["csat_score"], label=skill, alpha=0.7)
 
-        ax.set_xlabel("AHT (segundos)")
+        ax.set_xlabel("AHT (seconds)")
         ax.set_ylabel("CSAT")
-        ax.set_title("CSAT vs AHT por skill")
+        ax.set_title("CSAT vs AHT by skill")
         ax.grid(alpha=0.3)
         ax.legend(title="Skill", bbox_to_anchor=(1.05, 1), loc="upper left")
 
@@ -291,28 +290,28 @@ class SatisfactionExperienceMetrics:
 
     def plot_csat_distribution(self) -> Axes:
         """
-        Histograma de CSAT.
-        Si no hay csat_score, devuelve un Axes con mensaje.
+        CSAT histogram.
+        If there is no csat_score, returns an Axes with message.
         """
         df = self.df
         if "csat_score" not in df.columns or df["csat_score"].notna().sum() == 0:
             fig, ax = plt.subplots()
-            ax.text(0.5, 0.5, "Sin datos de CSAT", ha="center", va="center")
+            ax.text(0.5, 0.5, "No CSAT data", ha="center", va="center")
             ax.set_axis_off()
             return ax
 
         df = df.dropna(subset=["csat_score"]).copy()
         if df.empty:
             fig, ax = plt.subplots()
-            ax.text(0.5, 0.5, "Sin datos de CSAT", ha="center", va="center")
+            ax.text(0.5, 0.5, "No CSAT data", ha="center", va="center")
             ax.set_axis_off()
             return ax
 
         fig, ax = plt.subplots(figsize=(6, 4))
         ax.hist(df["csat_score"], bins=10, alpha=0.7)
         ax.set_xlabel("CSAT")
-        ax.set_ylabel("Frecuencia")
-        ax.set_title("Distribución de CSAT")
+        ax.set_ylabel("Frequency")
+        ax.set_title("CSAT distribution")
         ax.grid(axis="y", alpha=0.3)
 
         return ax

backend/beyond_metrics/dimensions/Volumetria.py

@@ -20,15 +20,15 @@ REQUIRED_COLUMNS_VOLUMETRIA: List[str] = [
 @dataclass
 class VolumetriaMetrics:
     """
-    Métricas de volumetría basadas en el nuevo esquema de datos.
+    Volumetry metrics based on the new data schema.
 
-    Columnas mínimas requeridas:
+    Minimum required columns:
     - interaction_id
     - datetime_start
     - queue_skill
     - channel
 
-    Otras columnas pueden existir pero no son necesarias para estas métricas.
+    Other columns may exist but are not required for these metrics.
     """
 
     df: pd.DataFrame
@@ -38,41 +38,41 @@ class VolumetriaMetrics:
         self._prepare_data()
 
     # ------------------------------------------------------------------ #
-    # Helpers internos
+    # Internal helpers
     # ------------------------------------------------------------------ #
     def _validate_columns(self) -> None:
         missing = [c for c in REQUIRED_COLUMNS_VOLUMETRIA if c not in self.df.columns]
         if missing:
             raise ValueError(
-                f"Faltan columnas obligatorias para VolumetriaMetrics: {missing}"
+                f"Missing required columns for VolumetriaMetrics: {missing}"
             )
 
     def _prepare_data(self) -> None:
         df = self.df.copy()
 
-        # Asegurar tipo datetime
+        # Ensure datetime type
         df["datetime_start"] = pd.to_datetime(df["datetime_start"], errors="coerce")
 
-        # Normalizar strings
+        # Normalize strings
         df["queue_skill"] = df["queue_skill"].astype(str).str.strip()
         df["channel"] = df["channel"].astype(str).str.strip()
 
-        # Guardamos el df preparado
+        # Store the prepared dataframe
         self.df = df
 
     # ------------------------------------------------------------------ #
-    # Propiedades útiles
+    # Useful properties
     # ------------------------------------------------------------------ #
     @property
     def is_empty(self) -> bool:
         return self.df.empty
 
     # ------------------------------------------------------------------ #
-    # Métricas numéricas / tabulares
+    # Numeric / tabular metrics
     # ------------------------------------------------------------------ #
     def volume_by_channel(self) -> pd.Series:
         """
-        Nº de interacciones por canal.
+        Number of interactions by channel.
         """
         return self.df.groupby("channel")["interaction_id"].nunique().sort_values(
             ascending=False
@@ -80,7 +80,7 @@ class VolumetriaMetrics:
 
     def volume_by_skill(self) -> pd.Series:
         """
-        Nº de interacciones por skill / cola.
+        Number of interactions by skill / queue.
         """
         return self.df.groupby("queue_skill")["interaction_id"].nunique().sort_values(
             ascending=False
@@ -88,7 +88,7 @@ class VolumetriaMetrics:
 
     def channel_distribution_pct(self) -> pd.Series:
         """
-        Distribución porcentual del volumen por canal.
+        Percentage distribution of volume by channel.
         """
         counts = self.volume_by_channel()
         total = counts.sum()
@@ -98,7 +98,7 @@ class VolumetriaMetrics:
 
     def skill_distribution_pct(self) -> pd.Series:
         """
-        Distribución porcentual del volumen por skill.
+        Percentage distribution of volume by skill.
         """
         counts = self.volume_by_skill()
         total = counts.sum()
@@ -108,12 +108,12 @@ class VolumetriaMetrics:
 
     def heatmap_24x7(self) -> pd.DataFrame:
         """
-        Matriz [día_semana x hora] con nº de interacciones.
-        dayofweek: 0=Lunes ... 6=Domingo
+        Matrix [day_of_week x hour] with number of interactions.
+        dayofweek: 0=Monday ... 6=Sunday
         """
         df = self.df.dropna(subset=["datetime_start"]).copy()
         if df.empty:
-            # Devolvemos un df vacío pero con índice/columnas esperadas
+            # Return an empty dataframe with expected index/columns
             idx = range(7)
             cols = range(24)
             return pd.DataFrame(0, index=idx, columns=cols)
@@ -137,8 +137,8 @@ class VolumetriaMetrics:
 
     def monthly_seasonality_cv(self) -> float:
         """
-        Coeficiente de variación del volumen mensual.
-        CV = std / mean (en %).
+        Coefficient of variation of monthly volume.
+        CV = std / mean (in %).
         """
         df = self.df.dropna(subset=["datetime_start"]).copy()
         if df.empty:
@@ -161,9 +161,9 @@ class VolumetriaMetrics:
 
     def peak_offpeak_ratio(self) -> float:
         """
-        Ratio de volumen entre horas pico y valle.
+        Volume ratio between peak and off-peak hours.
 
-        Definimos pico como horas 10:00–19:59, resto valle.
+        We define peak as hours 10:00–19:59, rest as off-peak.
         """
         df = self.df.dropna(subset=["datetime_start"]).copy()
         if df.empty:
@@ -184,7 +184,7 @@ class VolumetriaMetrics:
 
     def concentration_top20_skills_pct(self) -> float:
         """
-        % del volumen concentrado en el top 20% de skills (por nº de interacciones).
+        % of volume concentrated in the top 20% of skills (by number of interactions).
         """
         counts = (
             self.df.groupby("queue_skill")["interaction_id"].nunique().sort_values(
@@ -210,8 +210,8 @@ class VolumetriaMetrics:
     # ------------------------------------------------------------------ #
     def plot_heatmap_24x7(self) -> Axes:
         """
-        Heatmap de volumen por día de la semana (0-6) y hora (0-23).
-        Devuelve Axes para que el pipeline pueda guardar la figura.
+        Heatmap of volume by day of week (0-6) and hour (0-23).
+        Returns Axes so the pipeline can save the figure.
         """
         data = self.heatmap_24x7()
 
@@ -222,45 +222,45 @@ class VolumetriaMetrics:
         ax.set_xticklabels([str(h) for h in range(24)])
 
         ax.set_yticks(range(7))
-        ax.set_yticklabels(["L", "M", "X", "J", "V", "S", "D"])
+        ax.set_yticklabels(["M", "T", "W", "T", "F", "S", "S"])
 
 
-        ax.set_xlabel("Hora del día")
-        ax.set_ylabel("Día de la semana")
-        ax.set_title("Volumen por día de la semana y hora")
+        ax.set_xlabel("Hour of day")
+        ax.set_ylabel("Day of week")
+        ax.set_title("Volume by day of week and hour")
 
-        plt.colorbar(im, ax=ax, label="Nº interacciones")
+        plt.colorbar(im, ax=ax, label="# interactions")
 
         return ax
 
     def plot_channel_distribution(self) -> Axes:
         """
-        Distribución de volumen por canal.
+        Volume distribution by channel.
         """
         series = self.volume_by_channel()
 
         fig, ax = plt.subplots(figsize=(6, 4))
         series.plot(kind="bar", ax=ax)
 
-        ax.set_xlabel("Canal")
-        ax.set_ylabel("Nº interacciones")
-        ax.set_title("Volumen por canal")
+        ax.set_xlabel("Channel")
+        ax.set_ylabel("# interactions")
+        ax.set_title("Volume by channel")
         ax.grid(axis="y", alpha=0.3)
 
         return ax
 
     def plot_skill_pareto(self) -> Axes:
         """
-        Pareto simple de volumen por skill (solo barras de volumen).
+        Simple Pareto chart of volume by skill (volume bars only).
         """
         series = self.volume_by_skill()
 
         fig, ax = plt.subplots(figsize=(10, 4))
         series.plot(kind="bar", ax=ax)
 
-        ax.set_xlabel("Skill / Cola")
-        ax.set_ylabel("Nº interacciones")
-        ax.set_title("Pareto de volumen por skill")
+        ax.set_xlabel("Skill / Queue")
+        ax.set_ylabel("# interactions")
+        ax.set_title("Pareto chart of volume by skill")
         ax.grid(axis="y", alpha=0.3)
 
         plt.xticks(rotation=45, ha="right")

backend/beyond_metrics/pipeline.py

@@ -23,7 +23,7 @@ LOGGER = logging.getLogger(__name__)
 
 def setup_basic_logging(level: str = "INFO") -> None:
     """
-    Configuración básica de logging, por si se necesita desde scripts.
+    Basic logging configuration, if needed from scripts.
     """
     logging.basicConfig(
         level=getattr(logging, level.upper(), logging.INFO),
@@ -33,10 +33,10 @@ def setup_basic_logging(level: str = "INFO") -> None:
 
 def _import_class(path: str) -> type:
     """
-    Import dinámico de una clase a partir de un string tipo:
+    Dynamic import of a class from a string like:
         "beyond_metrics.dimensions.VolumetriaMetrics"
     """
-    LOGGER.debug("Importando clase %s", path)
+    LOGGER.debug("Importing class %s", path)
     module_name, class_name = path.rsplit(".", 1)
     module = import_module(module_name)
     cls = getattr(module, class_name)
@@ -45,7 +45,7 @@ def _import_class(path: str) -> type:
 
 def _serialize_for_json(obj: Any) -> Any:
     """
-    Convierte objetos típicos de numpy/pandas en tipos JSON-friendly.
+    Converts typical numpy/pandas objects to JSON-friendly types.
     """
     if obj is None or isinstance(obj, (str, int, float, bool)):
         return obj
@@ -73,12 +73,12 @@ PostRunCallback = Callable[[Dict[str, Any], str, ResultsSink], None]
 @dataclass
 class BeyondMetricsPipeline:
     """
-    Pipeline principal de BeyondMetrics.
+    Main BeyondMetrics pipeline.
 
-    - Lee un CSV desde un DataSource (local, S3, Google Drive, etc.).
-    - Ejecuta dimensiones configuradas en un dict de configuración.
-    - Serializa resultados numéricos/tabulares a JSON.
-    - Guarda las imágenes de los métodos que comienzan por 'plot_'.
+    - Reads a CSV from a DataSource (local, S3, Google Drive, etc.).
+    - Executes dimensions configured in a config dict.
+    - Serializes numeric/tabular results to JSON.
+    - Saves images from methods starting with 'plot_'.
     """
 
     datasource: DataSource
@@ -95,39 +95,39 @@ class BeyondMetricsPipeline:
         write_results_json: bool = True,
     ) -> Dict[str, Any]:
         
-        LOGGER.info("Inicio de ejecución de BeyondMetricsPipeline")
-        LOGGER.info("Leyendo CSV de entrada: %s", input_path)
+        LOGGER.info("Starting BeyondMetricsPipeline execution")
+        LOGGER.info("Reading input CSV: %s", input_path)
 
-        # 1) Leer datos
+        # 1) Read data
         df = self.datasource.read_csv(input_path)
-        LOGGER.info("CSV leído con %d filas y %d columnas", df.shape[0], df.shape[1])
+        LOGGER.info("CSV read with %d rows and %d columns", df.shape[0], df.shape[1])
 
-        # 2) Determinar carpeta/base de salida para esta ejecución
+        # 2) Determine output folder/base for this execution
         run_base = run_dir.rstrip("/")
-        LOGGER.info("Ruta base de esta ejecución: %s", run_base)
+        LOGGER.info("Base path for this execution: %s", run_base)
 
-        # 3) Ejecutar dimensiones
+        # 3) Execute dimensions
         dimensions_cfg = self.dimensions_config
         if not isinstance(dimensions_cfg, dict):
-            raise ValueError("El bloque 'dimensions' debe ser un dict.")
+            raise ValueError("The 'dimensions' block must be a dict.")
 
         all_results: Dict[str, Any] = {}
 
         for dim_name, dim_cfg in dimensions_cfg.items():
             if not isinstance(dim_cfg, dict):
-                raise ValueError(f"Config inválida para dimensión '{dim_name}' (debe ser dict).")
+                raise ValueError(f"Invalid config for dimension '{dim_name}' (must be dict).")
 
             if not dim_cfg.get("enabled", True):
-                LOGGER.info("Dimensión '%s' desactivada; se omite.", dim_name)
+                LOGGER.info("Dimension '%s' disabled; skipping.", dim_name)
                 continue
 
             class_path = dim_cfg.get("class")
             if not class_path:
-                raise ValueError(f"Falta 'class' en la dimensión '{dim_name}'.")
+                raise ValueError(f"Missing 'class' in dimension '{dim_name}'.")
 
             metrics: List[str] = dim_cfg.get("metrics", [])
             if not metrics:
-                LOGGER.info("Dimensión '%s' sin métricas configuradas; se omite.", dim_name)
+                LOGGER.info("Dimension '%s' has no configured metrics; skipping.", dim_name)
                 continue
 
             cls = _import_class(class_path)
@@ -136,35 +136,35 @@ class BeyondMetricsPipeline:
             if self.dimension_params is not None:
                 extra_kwargs = self.dimension_params.get(dim_name, {}) or {}
 
-            # Las dimensiones reciben df en el constructor
+            # Dimensions receive df in the constructor
             instance = cls(df, **extra_kwargs)
 
             dim_results: Dict[str, Any] = {}
 
             for metric_name in metrics:
-                LOGGER.info("  - Ejecutando métrica '%s.%s'", dim_name, metric_name)
+                LOGGER.info("  - Executing metric '%s.%s'", dim_name, metric_name)
                 result = self._execute_metric(instance, metric_name, run_base, dim_name)
                 dim_results[metric_name] = result
 
             all_results[dim_name] = dim_results
 
-        # 4) Guardar JSON de resultados (opcional)
+        # 4) Save results JSON (optional)
         if write_results_json:
             results_json_path = f"{run_base}/results.json"
-            LOGGER.info("Guardando resultados en JSON: %s", results_json_path)
+            LOGGER.info("Saving results to JSON: %s", results_json_path)
             self.sink.write_json(results_json_path, all_results)
 
-        # 5) Ejecutar callbacks post-run (scorers, agentes, etc.)
+        # 5) Execute post-run callbacks (scorers, agents, etc.)
         if self.post_run:
-            LOGGER.info("Ejecutando %d callbacks post-run...", len(self.post_run))
+            LOGGER.info("Executing %d post-run callbacks...", len(self.post_run))
             for cb in self.post_run:
                 try:
-                    LOGGER.info("Ejecutando post-run callback: %s", cb)
+                    LOGGER.info("Executing post-run callback: %s", cb)
                     cb(all_results, run_base, self.sink)
                 except Exception:
-                    LOGGER.exception("Error ejecutando post-run callback %s", cb)
+                    LOGGER.exception("Error executing post-run callback %s", cb)
 
-        LOGGER.info("Ejecución completada correctamente.")
+        LOGGER.info("Execution completed successfully.")
         return all_results
 
 
@@ -176,42 +176,42 @@ class BeyondMetricsPipeline:
         dim_name: str,
     ) -> Any:
         """
-        Ejecuta una métrica:
+        Executes a metric:
 
-        - Si empieza por 'plot_' -> se asume que devuelve Axes:
-            - se guarda la figura como PNG
-            - se devuelve {"type": "image", "path": "..."}
-        - Si no, se serializa el valor a JSON.
+        - If it starts with 'plot_' -> assumed to return Axes:
+            - the figure is saved as PNG
+            - returns {"type": "image", "path": "..."}
+        - Otherwise, the value is serialized to JSON.
 
-        Además, para métricas categóricas (por skill/canal) de la dimensión
-        'volumetry', devolvemos explícitamente etiquetas y valores para que
-        el frontend pueda saber a qué pertenece cada número.
+        Additionally, for categorical metrics (by skill/channel) from the
+        'volumetry' dimension, we explicitly return labels and values so
+        the frontend can know what each number belongs to.
         """
         method = getattr(instance, metric_name, None)
         if method is None or not callable(method):
             raise ValueError(
-                f"La métrica '{metric_name}' no existe en {type(instance).__name__}"
+                f"Metric '{metric_name}' does not exist in {type(instance).__name__}"
             )
 
-        # Caso plots
+        # Plot case
         if metric_name.startswith("plot_"):
             ax = method()
             if not isinstance(ax, Axes):
                 raise TypeError(
-                    f"La métrica '{metric_name}' de '{type(instance).__name__}' "
-                    f"debería devolver un matplotlib.axes.Axes"
+                    f"Metric '{metric_name}' of '{type(instance).__name__}' "
+                    f"should return a matplotlib.axes.Axes"
                 )
             fig = ax.get_figure()
             if fig is None:
                 raise RuntimeError(
-                    "Axes.get_figure() devolvió None, lo cual no debería pasar."
+                    "Axes.get_figure() returned None, which should not happen."
                 )
             fig = cast(Figure, fig)
 
             filename = f"{dim_name}_{metric_name}.png"
             img_path = f"{run_base}/{filename}"
 
-            LOGGER.debug("Guardando figura en %s", img_path)
+            LOGGER.debug("Saving figure to %s", img_path)
             self.sink.write_figure(img_path, fig)
             plt.close(fig)
 
@@ -220,12 +220,12 @@ class BeyondMetricsPipeline:
                 "path": img_path,
             }
 
-        # Caso numérico/tabular
+        # Numeric/tabular case
         value = method()
 
-        # Caso especial: series categóricas de volumetría (por skill / canal)
-        # Devolvemos {"labels": [...], "values": [...]} para mantener la
-        # información de etiquetas en el JSON.
+        # Special case: categorical series from volumetry (by skill / channel)
+        # Return {"labels": [...], "values": [...]} to maintain
+        # label information in the JSON.
         if (
             dim_name == "volumetry"
             and isinstance(value, pd.Series)
@@ -238,7 +238,7 @@ class BeyondMetricsPipeline:
             }
         ):
             labels = [str(idx) for idx in value.index.tolist()]
-            # Aseguramos que todos los valores sean numéricos JSON-friendly
+            # Ensure all values are JSON-friendly numeric
             values = [float(v) for v in value.astype(float).tolist()]
             return {
                 "labels": labels,
@@ -251,7 +251,7 @@ class BeyondMetricsPipeline:
 
 def load_dimensions_config(path: str) -> Dict[str, Any]:
     """
-    Carga un JSON de configuración que contiene solo el bloque 'dimensions'.
+    Loads a JSON configuration file containing only the 'dimensions' block.
     """
     import json
     from pathlib import Path
@@ -261,7 +261,7 @@ def load_dimensions_config(path: str) -> Dict[str, Any]:
 
     dimensions = cfg.get("dimensions")
     if dimensions is None:
-        raise ValueError("El fichero de configuración debe contener un bloque 'dimensions'.")
+        raise ValueError("The configuration file must contain a 'dimensions' block.")
 
     return dimensions
 
@@ -274,12 +274,12 @@ def build_pipeline(
     post_run: Optional[List[PostRunCallback]] = None,
 ) -> BeyondMetricsPipeline:
     """
-    Crea un BeyondMetricsPipeline a partir de:
-    - ruta al JSON con dimensiones/métricas
-    - un DataSource ya construido (local/S3/Drive)
-    - un ResultsSink ya construido (local/S3/Drive)
-    - una lista opcional de callbacks post_run que se ejecutan al final
-      (útil para scorers, agentes de IA, etc.)
+    Creates a BeyondMetricsPipeline from:
+    - path to JSON with dimensions/metrics
+    - an already constructed DataSource (local/S3/Drive)
+    - an already constructed ResultsSink (local/S3/Drive)
+    - an optional list of post_run callbacks that execute at the end
+      (useful for scorers, AI agents, etc.)
     """
     dims_cfg = load_dimensions_config(dimensions_config_path)
     return BeyondMetricsPipeline(

frontend/components/OpportunityPrioritizer.tsx

@@ -14,6 +14,7 @@
 
 import React, { useState, useMemo } from 'react';
 import { motion, AnimatePresence } from 'framer-motion';
+import { useTranslation } from 'react-i18next';
 import { Opportunity, DrilldownDataPoint, AgenticTier } from '../types';
 import {
   ChevronRight,
@@ -57,56 +58,56 @@ interface EnrichedOpportunity extends Opportunity {
   annualCost?: number;
 }
 
-// Tier configuration
+// Tier configuration - labels and descriptions will be translated at usage time
 const TIER_CONFIG: Record<AgenticTier, {
   icon: React.ReactNode;
-  label: string;
+  labelKey: string;
   color: string;
   bgColor: string;
   borderColor: string;
   savingsRate: string;
-  timeline: string;
-  description: string;
+  timelineKey: string;
+  descriptionKey: string;
 }> = {
   'AUTOMATE': {
     icon: <Bot size={18} />,
-    label: 'Automatizar',
+    labelKey: 'opportunityPrioritizer.tierLabels.automate',
     color: 'text-emerald-700',
     bgColor: 'bg-emerald-50',
     borderColor: 'border-emerald-300',
     savingsRate: '70%',
-    timeline: '3-6 meses',
-    description: 'Automatización completa con agentes IA'
+    timelineKey: 'opportunityPrioritizer.timelines.automate',
+    descriptionKey: 'opportunityPrioritizer.tierDescriptions.automate'
   },
   'ASSIST': {
     icon: <Headphones size={18} />,
-    label: 'Asistir',
+    labelKey: 'opportunityPrioritizer.tierLabels.assist',
     color: 'text-blue-700',
     bgColor: 'bg-blue-50',
     borderColor: 'border-blue-300',
     savingsRate: '30%',
-    timeline: '6-9 meses',
-    description: 'Copilot IA para agentes humanos'
+    timelineKey: 'opportunityPrioritizer.timelines.assist',
+    descriptionKey: 'opportunityPrioritizer.tierDescriptions.assist'
   },
   'AUGMENT': {
     icon: <BookOpen size={18} />,
-    label: 'Optimizar',
+    labelKey: 'opportunityPrioritizer.tierLabels.augment',
     color: 'text-amber-700',
     bgColor: 'bg-amber-50',
     borderColor: 'border-amber-300',
     savingsRate: '15%',
-    timeline: '9-12 meses',
-    description: 'Estandarización y mejora de procesos'
+    timelineKey: 'opportunityPrioritizer.timelines.augment',
+    descriptionKey: 'opportunityPrioritizer.tierDescriptions.augment'
   },
   'HUMAN-ONLY': {
     icon: <Users size={18} />,
-    label: 'Humano',
+    labelKey: 'opportunityPrioritizer.tierLabels.human',
     color: 'text-slate-600',
     bgColor: 'bg-slate-50',
     borderColor: 'border-slate-300',
     savingsRate: '0%',
-    timeline: 'N/A',
-    description: 'Requiere intervención humana'
+    timelineKey: 'N/A',
+    descriptionKey: 'opportunityPrioritizer.tierDescriptions.humanOnly'
   }
 };
 
@@ -115,6 +116,7 @@ const OpportunityPrioritizer: React.FC<OpportunityPrioritizerProps> = ({
   drilldownData,
   costPerHour = 20
 }) => {
+  const { t } = useTranslation();
   const [expandedId, setExpandedId] = useState<string | null>(null);
   const [showAllOpportunities, setShowAllOpportunities] = useState(false);
 
@@ -175,29 +177,23 @@ const OpportunityPrioritizer: React.FC<OpportunityPrioritizerProps> = ({
       // Timeline based on tier
       const timelineMonths = tier === 'AUTOMATE' ? 4 : tier === 'ASSIST' ? 7 : 10;
 
-      // Generate "why" explanation
-      const whyPrioritized: string[] = [];
-      if (opp.savings > 50000) whyPrioritized.push(`Alto ahorro potencial (€${(opp.savings / 1000).toFixed(0)}K/año)`);
-      if (lookupData?.volume && lookupData.volume > 1000) whyPrioritized.push(`Alto volumen (${lookupData.volume.toLocaleString()} interacciones)`);
-      if (tier === 'AUTOMATE') whyPrioritized.push('Proceso altamente predecible y repetitivo');
-      if (cv < 60) whyPrioritized.push('Baja variabilidad en tiempos de gestión');
-      if (transfer < 15) whyPrioritized.push('Baja tasa de transferencias');
-      if (opp.feasibility >= 7) whyPrioritized.push('Alta factibilidad técnica');
+      // Generate "why" explanation - store keys for translation
+      const whyPrioritized: { key: string; params?: any }[] = [];
+      if (opp.savings > 50000) whyPrioritized.push({ key: 'reasons.highSavingsPotential', params: { amount: (opp.savings / 1000).toFixed(0) } });
+      if (lookupData?.volume && lookupData.volume > 1000) whyPrioritized.push({ key: 'reasons.highVolume', params: { volume: lookupData.volume.toLocaleString() } });
+      if (tier === 'AUTOMATE') whyPrioritized.push({ key: 'reasons.highlyPredictable' });
+      if (cv < 60) whyPrioritized.push({ key: 'reasons.lowVariability' });
+      if (transfer < 15) whyPrioritized.push({ key: 'reasons.lowTransferRate' });
+      if (opp.feasibility >= 7) whyPrioritized.push({ key: 'reasons.highFeasibility' });
 
-      // Generate next steps
+      // Generate next steps - store keys for translation
       const nextSteps: string[] = [];
       if (tier === 'AUTOMATE') {
-        nextSteps.push('Definir flujos conversacionales principales');
-        nextSteps.push('Identificar integraciones necesarias (CRM, APIs)');
-        nextSteps.push('Crear piloto con 10% del volumen');
+        nextSteps.push('steps.automate1', 'steps.automate2', 'steps.automate3');
       } else if (tier === 'ASSIST') {
-        nextSteps.push('Mapear puntos de fricción del agente');
-        nextSteps.push('Diseñar sugerencias contextuales');
-        nextSteps.push('Piloto con equipo seleccionado');
+        nextSteps.push('steps.assist1', 'steps.assist2', 'steps.assist3');
       } else {
-        nextSteps.push('Analizar causa raíz de variabilidad');
-        nextSteps.push('Estandarizar procesos y scripts');
-        nextSteps.push('Capacitar equipo en mejores prácticas');
+        nextSteps.push('steps.augment1', 'steps.augment2', 'steps.augment3');
       }
 
       return {
@@ -248,8 +244,8 @@ const OpportunityPrioritizer: React.FC<OpportunityPrioritizerProps> = ({
     return (
       <div className="bg-white p-8 rounded-xl border border-slate-200 text-center">
         <AlertTriangle className="mx-auto mb-4 text-amber-500" size={48} />
-        <h3 className="text-lg font-semibold text-slate-700">No hay oportunidades identificadas</h3>
-        <p className="text-slate-500 mt-2">Los datos actuales no muestran oportunidades de automatización viables.</p>
+        <h3 className="text-lg font-semibold text-slate-700">{t('opportunityPrioritizer.noOpportunitiesTitle')}</h3>
+        <p className="text-slate-500 mt-2">{t('opportunityPrioritizer.noOpportunitiesDescription')}</p>
       </div>
     );
   }
@@ -260,9 +256,9 @@ const OpportunityPrioritizer: React.FC<OpportunityPrioritizerProps> = ({
       <div className="p-6 border-b border-slate-200">
         <div className="flex items-center justify-between">
           <div>
-            <h2 className="text-xl font-bold text-gray-900">Oportunidades Priorizadas</h2>
+            <h2 className="text-xl font-bold text-gray-900">{t('opportunityPrioritizer.title')}</h2>
             <p className="text-sm text-gray-500 mt-1">
-              {enrichedOpportunities.length} iniciativas ordenadas por potencial de ahorro y factibilidad
+              {t('opportunityPrioritizer.subtitle', { count: enrichedOpportunities.length })}
             </p>
           </div>
         </div>
@@ -273,50 +269,50 @@ const OpportunityPrioritizer: React.FC<OpportunityPrioritizerProps> = ({
         <div className="bg-white rounded-lg p-4 border border-slate-200 shadow-sm">
           <div className="flex items-center gap-2 text-slate-500 text-xs mb-1">
             <DollarSign size={14} />
-            <span>Ahorro Total Identificado</span>
+            <span>{t('opportunityPrioritizer.totalSavingsIdentified')}</span>
           </div>
           <div className="text-3xl font-bold text-slate-800">
             €{(summary.totalSavings / 1000).toFixed(0)}K
           </div>
-          <div className="text-xs text-slate-500">anuales</div>
+          <div className="text-xs text-slate-500">{t('opportunityPrioritizer.annual')}</div>
         </div>
 
         <div className="bg-emerald-50 rounded-lg p-4 border border-emerald-200 shadow-sm">
           <div className="flex items-center gap-2 text-emerald-600 text-xs mb-1">
             <Bot size={14} />
-            <span>Quick Wins (AUTOMATE)</span>
+            <span>{t('opportunityPrioritizer.quickWins')}</span>
           </div>
           <div className="text-3xl font-bold text-emerald-700">
             {summary.byTier.AUTOMATE.length}
           </div>
           <div className="text-xs text-emerald-600">
-            €{(summary.byTier.AUTOMATE.reduce((s, o) => s + o.savings, 0) / 1000).toFixed(0)}K en 3-6 meses
+            €{(summary.byTier.AUTOMATE.reduce((s, o) => s + o.savings, 0) / 1000).toFixed(0)}K {t('opportunityPrioritizer.inMonths', { months: '3-6' })}
           </div>
         </div>
 
         <div className="bg-blue-50 rounded-lg p-4 border border-blue-200 shadow-sm">
           <div className="flex items-center gap-2 text-blue-600 text-xs mb-1">
             <Headphones size={14} />
-            <span>Asistencia (ASSIST)</span>
+            <span>{t('opportunityPrioritizer.assistance')}</span>
           </div>
           <div className="text-3xl font-bold text-blue-700">
             {summary.byTier.ASSIST.length}
           </div>
           <div className="text-xs text-blue-600">
-            €{(summary.byTier.ASSIST.reduce((s, o) => s + o.savings, 0) / 1000).toFixed(0)}K en 6-9 meses
+            €{(summary.byTier.ASSIST.reduce((s, o) => s + o.savings, 0) / 1000).toFixed(0)}K {t('opportunityPrioritizer.inMonths', { months: '6-9' })}
           </div>
         </div>
 
         <div className="bg-amber-50 rounded-lg p-4 border border-amber-200 shadow-sm">
           <div className="flex items-center gap-2 text-amber-600 text-xs mb-1">
             <BookOpen size={14} />
-            <span>Optimización (AUGMENT)</span>
+            <span>{t('opportunityPrioritizer.optimization')}</span>
           </div>
           <div className="text-3xl font-bold text-amber-700">
             {summary.byTier.AUGMENT.length}
           </div>
           <div className="text-xs text-amber-600">
-            €{(summary.byTier.AUGMENT.reduce((s, o) => s + o.savings, 0) / 1000).toFixed(0)}K en 9-12 meses
+            €{(summary.byTier.AUGMENT.reduce((s, o) => s + o.savings, 0) / 1000).toFixed(0)}K {t('opportunityPrioritizer.inMonths', { months: '9-12' })}
           </div>
         </div>
       </div>
@@ -326,8 +322,8 @@ const OpportunityPrioritizer: React.FC<OpportunityPrioritizerProps> = ({
         <div className="p-6 bg-gradient-to-r from-emerald-50 to-green-50 border-b-2 border-emerald-200">
           <div className="flex items-center gap-2 mb-4">
             <Sparkles className="text-emerald-600" size={20} />
-            <span className="text-emerald-800 font-bold text-lg">EMPIEZA AQUÍ</span>
-            <span className="bg-emerald-600 text-white text-xs px-2 py-0.5 rounded-full">Prioridad #1</span>
+            <span className="text-emerald-800 font-bold text-lg">{t('opportunityPrioritizer.startHere')}</span>
+            <span className="bg-emerald-600 text-white text-xs px-2 py-0.5 rounded-full">{t('opportunityPrioritizer.priority1')}</span>
           </div>
 
           <div className="bg-white rounded-xl border-2 border-emerald-300 p-6 shadow-lg">
@@ -343,7 +339,7 @@ const OpportunityPrioritizer: React.FC<OpportunityPrioritizerProps> = ({
                       {topOpportunity.name.replace(/^[^\w\s]+\s*/, '')}
                     </h3>
                     <span className={`text-sm font-medium ${TIER_CONFIG[topOpportunity.tier].color}`}>
-                      {TIER_CONFIG[topOpportunity.tier].label} • {TIER_CONFIG[topOpportunity.tier].description}
+                      {t(TIER_CONFIG[topOpportunity.tier].labelKey)} • {t(TIER_CONFIG[topOpportunity.tier].descriptionKey)}
                     </span>
                   </div>
                 </div>
@@ -351,25 +347,25 @@ const OpportunityPrioritizer: React.FC<OpportunityPrioritizerProps> = ({
                 {/* Key metrics */}
                 <div className="grid grid-cols-2 md:grid-cols-4 gap-4 mb-4">
                   <div className="bg-green-50 rounded-lg p-3">
-                    <div className="text-xs text-green-600 mb-1">Ahorro Anual</div>
+                    <div className="text-xs text-green-600 mb-1">{t('opportunityPrioritizer.annualSavings')}</div>
                     <div className="text-xl font-bold text-green-700">
                       €{(topOpportunity.savings / 1000).toFixed(0)}K
                     </div>
                   </div>
                   <div className="bg-slate-50 rounded-lg p-3">
-                    <div className="text-xs text-slate-500 mb-1">Volumen</div>
+                    <div className="text-xs text-slate-500 mb-1">{t('opportunityPrioritizer.volume')}</div>
                     <div className="text-xl font-bold text-slate-700">
                       {topOpportunity.volume.toLocaleString()}
                     </div>
                   </div>
                   <div className="bg-slate-50 rounded-lg p-3">
-                    <div className="text-xs text-slate-500 mb-1">Timeline</div>
+                    <div className="text-xs text-slate-500 mb-1">{t('opportunityPrioritizer.timeline')}</div>
                     <div className="text-xl font-bold text-slate-700">
-                      {topOpportunity.timelineMonths} meses
+                      {topOpportunity.timelineMonths} {t('opportunityPrioritizer.months')}
                     </div>
                   </div>
                   <div className="bg-slate-50 rounded-lg p-3">
-                    <div className="text-xs text-slate-500 mb-1">Agentic Score</div>
+                    <div className="text-xs text-slate-500 mb-1">{t('opportunityPrioritizer.agenticScore')}</div>
                     <div className="text-xl font-bold text-slate-700">
                       {topOpportunity.agenticScore.toFixed(1)}/10
                     </div>
@@ -380,13 +376,13 @@ const OpportunityPrioritizer: React.FC<OpportunityPrioritizerProps> = ({
                 <div className="mb-4">
                   <h4 className="text-sm font-semibold text-slate-700 mb-2 flex items-center gap-2">
                     <Info size={14} />
-                    ¿Por qué es la prioridad #1?
+                    {t('opportunityPrioritizer.whyPriority1')}
                   </h4>
                   <ul className="space-y-1">
                     {topOpportunity.whyPrioritized.slice(0, 4).map((reason, i) => (
                       <li key={i} className="flex items-center gap-2 text-sm text-slate-600">
                         <CheckCircle2 size={14} className="text-emerald-500 flex-shrink-0" />
-                        {reason}
+                        {t(`opportunityPrioritizer.${reason.key}`, reason.params)}
                       </li>
                     ))}
                   </ul>
@@ -397,7 +393,7 @@ const OpportunityPrioritizer: React.FC<OpportunityPrioritizerProps> = ({
               <div className="lg:w-80 bg-emerald-50 rounded-lg p-4 border border-emerald-200">
                 <h4 className="text-sm font-semibold text-emerald-800 mb-3 flex items-center gap-2">
                   <ArrowRight size={14} />
-                  Próximos Pasos
+                  {t('opportunityPrioritizer.nextSteps')}
                 </h4>
                 <ol className="space-y-2">
                   {topOpportunity.nextSteps.map((step, i) => (
@@ -405,12 +401,12 @@ const OpportunityPrioritizer: React.FC<OpportunityPrioritizerProps> = ({
                       <span className="bg-emerald-600 text-white w-5 h-5 rounded-full flex items-center justify-center text-xs flex-shrink-0 mt-0.5">
                         {i + 1}
                       </span>
-                      {step}
+                      {t(`opportunityPrioritizer.${step}`)}
                     </li>
                   ))}
                 </ol>
                 <button className="mt-4 w-full bg-emerald-600 hover:bg-emerald-700 text-white font-medium py-2 px-4 rounded-lg transition-colors flex items-center justify-center gap-2">
-                  Ver Detalle Completo
+                  {t('opportunityPrioritizer.viewCompleteDetail')}
                   <ChevronRight size={16} />
                 </button>
               </div>
@@ -423,7 +419,7 @@ const OpportunityPrioritizer: React.FC<OpportunityPrioritizerProps> = ({
       <div className="p-6">
         <h3 className="text-lg font-bold text-slate-800 mb-4 flex items-center gap-2">
           <BarChart3 size={20} />
-          Todas las Oportunidades Priorizadas
+          {t('opportunityPrioritizer.allOpportunities')}
         </h3>
 
         <div className="space-y-3">
@@ -460,18 +456,18 @@ const OpportunityPrioritizer: React.FC<OpportunityPrioritizerProps> = ({
                       {opp.name.replace(/^[^\w\s]+\s*/, '')}
                     </h4>
                     <span className={`text-xs ${TIER_CONFIG[opp.tier].color}`}>
-                      {TIER_CONFIG[opp.tier].label} • {TIER_CONFIG[opp.tier].timeline}
+                      {t(TIER_CONFIG[opp.tier].labelKey)} • {t(TIER_CONFIG[opp.tier].timelineKey)}
                     </span>
                   </div>
 
                   {/* Quick stats */}
                   <div className="hidden md:flex items-center gap-6">
                     <div className="text-right">
-                      <div className="text-xs text-slate-500">Ahorro</div>
+                      <div className="text-xs text-slate-500">{t('opportunityPrioritizer.savings')}</div>
                       <div className="font-bold text-green-600">€{(opp.savings / 1000).toFixed(0)}K</div>
                     </div>
                     <div className="text-right">
-                      <div className="text-xs text-slate-500">Volumen</div>
+                      <div className="text-xs text-slate-500">{t('opportunityPrioritizer.volume')}</div>
                       <div className="font-semibold text-slate-700">{opp.volume.toLocaleString()}</div>
                     </div>
                     <div className="text-right">
@@ -482,7 +478,7 @@ const OpportunityPrioritizer: React.FC<OpportunityPrioritizerProps> = ({
 
                   {/* Visual bar: Value vs Effort */}
                   <div className="hidden lg:block w-32">
-                    <div className="text-xs text-slate-500 mb-1">Valor / Esfuerzo</div>
+                    <div className="text-xs text-slate-500 mb-1">{t('opportunityPrioritizer.valueEffort')}</div>
                     <div className="flex h-2 rounded-full overflow-hidden bg-slate-100">
                       <div
                         className="bg-emerald-500 transition-all"
@@ -494,8 +490,8 @@ const OpportunityPrioritizer: React.FC<OpportunityPrioritizerProps> = ({
                       />
                     </div>
                     <div className="flex justify-between text-[10px] text-slate-400 mt-0.5">
-                      <span>Valor</span>
-                      <span>Esfuerzo</span>
+                      <span>{t('opportunityPrioritizer.value')}</span>
+                      <span>{t('opportunityPrioritizer.effort')}</span>
                     </div>
                   </div>
 
@@ -523,12 +519,12 @@ const OpportunityPrioritizer: React.FC<OpportunityPrioritizerProps> = ({
                       <div className="grid grid-cols-1 md:grid-cols-2 gap-4">
                         {/* Why prioritized */}
                         <div>
-                          <h5 className="text-sm font-semibold text-slate-700 mb-2">¿Por qué esta posición?</h5>
+                          <h5 className="text-sm font-semibold text-slate-700 mb-2">{t('opportunityPrioritizer.whyThisPosition')}</h5>
                           <ul className="space-y-1">
                             {opp.whyPrioritized.map((reason, i) => (
                               <li key={i} className="flex items-center gap-2 text-sm text-slate-600">
                                 <CheckCircle2 size={12} className="text-emerald-500 flex-shrink-0" />
-                                {reason}
+                                {t(`opportunityPrioritizer.${reason.key}`, reason.params)}
                               </li>
                             ))}
                           </ul>
@@ -536,7 +532,7 @@ const OpportunityPrioritizer: React.FC<OpportunityPrioritizerProps> = ({
 
                         {/* Metrics */}
                         <div>
-                          <h5 className="text-sm font-semibold text-slate-700 mb-2">Métricas Clave</h5>
+                          <h5 className="text-sm font-semibold text-slate-700 mb-2">{t('opportunityPrioritizer.keyMetrics')}</h5>
                           <div className="grid grid-cols-2 gap-2">
                             <div className="bg-white rounded p-2 border border-slate-200">
                               <div className="text-xs text-slate-500">CV AHT</div>
@@ -551,12 +547,12 @@ const OpportunityPrioritizer: React.FC<OpportunityPrioritizerProps> = ({
                               <div className="font-semibold text-slate-700">{opp.fcr_rate.toFixed(1)}%</div>
                             </div>
                             <div className="bg-white rounded p-2 border border-slate-200">
-                              <div className="text-xs text-slate-500">Riesgo</div>
+                              <div className="text-xs text-slate-500">{t('roadmap.risk')}</div>
                               <div className={`font-semibold ${
                                 opp.riskLevel === 'low' ? 'text-emerald-600' :
                                 opp.riskLevel === 'medium' ? 'text-amber-600' : 'text-red-600'
                               }`}>
-                                {opp.riskLevel === 'low' ? 'Bajo' : opp.riskLevel === 'medium' ? 'Medio' : 'Alto'}
+                                {t(`roadmap.risk${opp.riskLevel.charAt(0).toUpperCase() + opp.riskLevel.slice(1)}`)}
                               </div>
                             </div>
                           </div>
@@ -565,11 +561,11 @@ const OpportunityPrioritizer: React.FC<OpportunityPrioritizerProps> = ({
 
                       {/* Next steps */}
                       <div className="mt-4 pt-4 border-t border-slate-200">
-                        <h5 className="text-sm font-semibold text-slate-700 mb-2">Próximos Pasos</h5>
+                        <h5 className="text-sm font-semibold text-slate-700 mb-2">{t('opportunityPrioritizer.nextSteps')}</h5>
                         <div className="flex flex-wrap gap-2">
                           {opp.nextSteps.map((step, i) => (
                             <span key={i} className="bg-white border border-slate-200 rounded-full px-3 py-1 text-xs text-slate-600">
-                              {i + 1}. {step}
+                              {i + 1}. {t(`opportunityPrioritizer.${step}`)}
                             </span>
                           ))}
                         </div>
@@ -591,12 +587,12 @@ const OpportunityPrioritizer: React.FC<OpportunityPrioritizerProps> = ({
             {showAllOpportunities ? (
               <>
                 <ChevronDown size={16} className="rotate-180" />
-                Mostrar menos
+                {t('opportunityPrioritizer.showLess')}
               </>
             ) : (
               <>
                 <ChevronDown size={16} />
-                Ver {enrichedOpportunities.length - 5} oportunidades más
+                {t('opportunityPrioritizer.viewMore', { count: enrichedOpportunities.length - 5 })}
               </>
             )}
           </button>
@@ -609,9 +605,7 @@ const OpportunityPrioritizer: React.FC<OpportunityPrioritizerProps> = ({
           <div className="flex items-start gap-2">
             <Info size={14} className="flex-shrink-0 mt-0.5" />
             <div>
-              <strong>Metodología de priorización:</strong> Las oportunidades se ordenan por potencial de ahorro TCO (volumen × tasa de contención × diferencial CPI).
-              La clasificación de tier (AUTOMATE/ASSIST/AUGMENT) se basa en el Agentic Readiness Score considerando predictibilidad (CV AHT),
-              resolutividad (FCR + Transfer), volumen, calidad de datos y simplicidad del proceso.
+              <strong>{t('opportunityPrioritizer.methodology')}</strong> {t('opportunityPrioritizer.methodologyDescription')}
             </div>
           </div>
         </div>

frontend/components/tabs/Law10Tab.tsx

@@ -1209,7 +1209,7 @@ function Law10SummaryRoadmap({
         <div className="p-2 bg-slate-100 rounded-lg">
           <FileText className="w-5 h-5 text-slate-600" />
         </div>
-        <h3 className="font-semibold text-gray-900 text-lg">Resumen de Cumplimiento - Todos los Requisitos</h3>
+        <h3 className="font-semibold text-gray-900 text-lg">{t('law10.summary.title')}</h3>
       </div>
 
       {/* Scorecard con todos los requisitos */}
@@ -1217,11 +1217,11 @@ function Law10SummaryRoadmap({
         <table className="w-full text-sm">
           <thead>
             <tr className="border-b border-gray-200 bg-gray-50">
-              <th className="text-left py-3 px-3 font-medium text-gray-600">Requisito</th>
-              <th className="text-left py-3 px-3 font-medium text-gray-600">Descripcion</th>
-              <th className="text-center py-3 px-3 font-medium text-gray-600">Estado</th>
-              <th className="text-center py-3 px-3 font-medium text-gray-600">Score</th>
-              <th className="text-left py-3 px-3 font-medium text-gray-600">Gap</th>
+              <th className="text-left py-3 px-3 font-medium text-gray-600">{t('law10.summaryTable.requirement')}</th>
+              <th className="text-left py-3 px-3 font-medium text-gray-600">{t('law10.summaryTable.description')}</th>
+              <th className="text-center py-3 px-3 font-medium text-gray-600">{t('law10.summaryTable.status')}</th>
+              <th className="text-center py-3 px-3 font-medium text-gray-600">{t('law10.summaryTable.score')}</th>
+              <th className="text-left py-3 px-3 font-medium text-gray-600">{t('law10.summaryTable.gap')}</th>
             </tr>
           </thead>
           <tbody>
@@ -1241,7 +1241,7 @@ function Law10SummaryRoadmap({
                   <div className="flex items-center justify-center gap-2">
                     <StatusIcon status={req.result.status} />
                     <Badge
-                      label={getStatusLabel(req.result.status)}
+                      label={getStatusLabel(req.result.status, t)}
                       variant={getStatusBadgeVariant(req.result.status)}
                       size="sm"
                     />
@@ -1271,40 +1271,40 @@ function Law10SummaryRoadmap({
       <div className="flex flex-wrap gap-4 mb-6 p-3 bg-gray-50 rounded-lg text-xs">
         <div className="flex items-center gap-2">
           <CheckCircle className="w-4 h-4 text-emerald-500" />
-          <span className="text-gray-600">Cumple: Requisito satisfecho</span>
+          <span className="text-gray-600">{t('law10.summaryTable.legend.complies')}</span>
         </div>
         <div className="flex items-center gap-2">
           <AlertTriangle className="w-4 h-4 text-amber-500" />
-          <span className="text-gray-600">Parcial: Requiere mejoras</span>
+          <span className="text-gray-600">{t('law10.summaryTable.legend.partial')}</span>
         </div>
         <div className="flex items-center gap-2">
           <XCircle className="w-4 h-4 text-red-500" />
-          <span className="text-gray-600">No Cumple: Accion urgente</span>
+          <span className="text-gray-600">{t('law10.summaryTable.legend.notComply')}</span>
         </div>
         <div className="flex items-center gap-2">
           <HelpCircle className="w-4 h-4 text-gray-400" />
-          <span className="text-gray-600">Sin Datos: Campos no disponibles en CSV</span>
+          <span className="text-gray-600">{t('law10.summaryTable.legend.noData')}</span>
         </div>
       </div>
 
       {/* Inversion Estimada */}
       <div className="grid grid-cols-1 md:grid-cols-3 gap-4 p-4 bg-gray-50 rounded-lg">
         <div className="text-center">
-          <p className="text-xs text-gray-500 mb-1">Coste de no cumplimiento</p>
-          <p className="text-xl font-bold text-red-600">Hasta 100K</p>
-          <p className="text-xs text-gray-400">Multas potenciales/infraccion</p>
+          <p className="text-xs text-gray-500 mb-1">{t('law10.summaryTable.investment.nonComplianceCost')}</p>
+          <p className="text-xl font-bold text-red-600">{t('law10.summaryTable.investment.upTo100k')}</p>
+          <p className="text-xs text-gray-400">{t('law10.summaryTable.investment.potentialFines')}</p>
         </div>
         <div className="text-center">
-          <p className="text-xs text-gray-500 mb-1">Inversion recomendada</p>
+          <p className="text-xs text-gray-500 mb-1">{t('law10.summaryTable.investment.recommendedInvestment')}</p>
           <p className="text-xl font-bold text-blue-600">{formatCurrency(estimatedInvestment())}</p>
-          <p className="text-xs text-gray-400">Basada en tu operacion</p>
+          <p className="text-xs text-gray-400">{t('law10.summaryTable.investment.basedOnOperation')}</p>
         </div>
         <div className="text-center">
-          <p className="text-xs text-gray-500 mb-1">ROI de cumplimiento</p>
+          <p className="text-xs text-gray-500 mb-1">{t('law10.summaryTable.investment.complianceRoi')}</p>
           <p className="text-xl font-bold text-emerald-600">
             {data.economicModel?.roi3yr ? `${Math.round(data.economicModel.roi3yr / 2)}%` : 'Alto'}
           </p>
-          <p className="text-xs text-gray-400">Evitar sanciones + mejora CX</p>
+          <p className="text-xs text-gray-400">{t('law10.summaryTable.investment.avoidSanctions')}</p>
         </div>
       </div>
     </Card>
@@ -1313,29 +1313,31 @@ function Law10SummaryRoadmap({
 
 // Seccion: Resumen de Madurez de Datos
 function DataMaturitySummary({ data }: { data: AnalysisData }) {
+  const { t } = useTranslation();
+
   // Usar datos economicos reales cuando esten disponibles
   const currentAnnualCost = data.economicModel?.currentAnnualCost || 0;
   const annualSavings = data.economicModel?.annualSavings || 0;
   // Datos disponibles
   const availableData = [
-    { name: 'Cobertura temporal 24/7', article: 'Art. 14' },
-    { name: 'Distribucion geografica', article: 'Art. 15 parcial' },
-    { name: 'Calidad resolucion proxy', article: 'Art. 17 indirecto' },
+    { name: t('law10.dataMaturity.items.coverage247'), article: t('law10.dataMaturity.article', { number: '14' }) },
+    { name: t('law10.dataMaturity.items.geoDistribution'), article: t('law10.dataMaturity.articlePartial', { number: '15' }) },
+    { name: t('law10.dataMaturity.items.resolutionQuality'), article: t('law10.dataMaturity.articleIndirect', { number: '17' }) },
   ];
 
   // Datos estimables
   const estimableData = [
-    { name: 'ASA <3min via proxy abandono', article: 'Art. 8.2', error: '±10%' },
-    { name: 'Lenguas cooficiales via pais', article: 'Art. 15', error: 'sin detalle' },
+    { name: t('law10.dataMaturity.items.asa3min'), article: t('law10.dataMaturity.article', { number: '8.2' }), error: t('law10.dataMaturity.errorMargin', { margin: '10' }) },
+    { name: t('law10.dataMaturity.items.officialLanguages'), article: t('law10.dataMaturity.article', { number: '15' }), error: t('law10.dataMaturity.noDetail') },
   ];
 
   // Datos no disponibles
   const missingData = [
-    { name: 'Tiempo resolucion casos', article: 'Art. 17' },
-    { name: 'Cobros indebidos <5 dias', article: 'Art. 17' },
-    { name: 'Transfer a supervisor', article: 'Art. 8' },
-    { name: 'Info incidencias <2h', article: 'Art. 17' },
-    { name: 'Auditoria ENAC', article: 'Art. 22', note: 'requiere contratacion externa' },
+    { name: t('law10.dataMaturity.items.caseResolutionTime'), article: t('law10.dataMaturity.article', { number: '17' }) },
+    { name: t('law10.dataMaturity.items.undueBilling'), article: t('law10.dataMaturity.article', { number: '17' }) },
+    { name: t('law10.dataMaturity.items.supervisorTransfer'), article: t('law10.dataMaturity.article', { number: '8' }) },
+    { name: t('law10.dataMaturity.items.incidentInfo'), article: t('law10.dataMaturity.article', { number: '17' }) },
+    { name: t('law10.dataMaturity.items.enacAudit'), article: t('law10.dataMaturity.article', { number: '22' }), note: t('law10.dataMaturity.items.externalContractRequired') },
   ];
 
   return (
@@ -1344,17 +1346,17 @@ function DataMaturitySummary({ data }: { data: AnalysisData }) {
         <div className="p-2 bg-indigo-100 rounded-lg">
           <TrendingUp className="w-5 h-5 text-indigo-600" />
         </div>
-        <h3 className="font-semibold text-gray-900 text-lg">Resumen: Madurez de Datos para Compliance</h3>
+        <h3 className="font-semibold text-gray-900 text-lg">{t('law10.dataMaturity.title')}</h3>
       </div>
 
-      <p className="text-sm text-gray-600 mb-4">Tu nivel actual de instrumentacion:</p>
+      <p className="text-sm text-gray-600 mb-4">{t('law10.dataMaturity.currentLevel')}</p>
 
       <div className="grid grid-cols-1 md:grid-cols-3 gap-4 mb-6">
         {/* Datos disponibles */}
         <div className="p-4 bg-emerald-50 border border-emerald-200 rounded-lg">
           <div className="flex items-center gap-2 mb-3">
             <CheckCircle className="w-5 h-5 text-emerald-600" />
-            <p className="font-semibold text-emerald-800">DATOS DISPONIBLES (3/10)</p>
+            <p className="font-semibold text-emerald-800">{t('law10.dataMaturity.availableData')}</p>
           </div>
           <ul className="space-y-2 text-sm">
             {availableData.map((item, idx) => (
@@ -1370,7 +1372,7 @@ function DataMaturitySummary({ data }: { data: AnalysisData }) {
         <div className="p-4 bg-amber-50 border border-amber-200 rounded-lg">
           <div className="flex items-center gap-2 mb-3">
             <AlertTriangle className="w-5 h-5 text-amber-600" />
-            <p className="font-semibold text-amber-800">DATOS ESTIMABLES (2/10)</p>
+            <p className="font-semibold text-amber-800">{t('law10.dataMaturity.estimableData')}</p>
           </div>
           <ul className="space-y-2 text-sm">
             {estimableData.map((item, idx) => (
@@ -1386,7 +1388,7 @@ function DataMaturitySummary({ data }: { data: AnalysisData }) {
         <div className="p-4 bg-red-50 border border-red-200 rounded-lg">
           <div className="flex items-center gap-2 mb-3">
             <XCircle className="w-5 h-5 text-red-600" />
-            <p className="font-semibold text-red-800">NO DISPONIBLES (5/10)</p>
+            <p className="font-semibold text-red-800">{t('law10.dataMaturity.unavailableData')}</p>
           </div>
           <ul className="space-y-2 text-sm">
             {missingData.map((item, idx) => (
@@ -1406,28 +1408,28 @@ function DataMaturitySummary({ data }: { data: AnalysisData }) {
       <div className="p-4 bg-gray-50 rounded-lg">
         <div className="flex items-center gap-2 mb-4">
           <Lightbulb className="w-5 h-5 text-amber-500" />
-          <p className="font-semibold text-gray-800">INVERSION SUGERIDA PARA COMPLIANCE COMPLETO</p>
+          <p className="font-semibold text-gray-800">{t('law10.dataMaturity.investment.title')}</p>
         </div>
 
         <div className="grid grid-cols-1 md:grid-cols-2 gap-4 mb-4">
           {/* Fase 1 */}
           <div className="p-3 bg-white rounded border border-gray-200">
-            <p className="font-medium text-gray-800 mb-2">Fase 1 - Instrumentacion (Q1 2026)</p>
+            <p className="font-medium text-gray-800 mb-2">{t('law10.dataMaturity.investment.phase1.title')}</p>
             <ul className="space-y-1 text-sm text-gray-600">
               <li className="flex justify-between">
-                <span>• Tracking ASA real</span>
+                <span>{t('law10.dataMaturity.investment.phase1.realAsaTracking')}</span>
                 <span className="font-semibold">5-8K</span>
               </li>
               <li className="flex justify-between">
-                <span>• Sistema ticketing/casos</span>
+                <span>{t('law10.dataMaturity.investment.phase1.ticketingSystem')}</span>
                 <span className="font-semibold">15-25K</span>
               </li>
               <li className="flex justify-between">
-                <span>• Enriquecimiento lenguas</span>
+                <span>{t('law10.dataMaturity.investment.phase1.languageEnrichment')}</span>
                 <span className="font-semibold">2K</span>
               </li>
               <li className="flex justify-between border-t border-gray-100 pt-1 mt-1">
-                <span className="font-medium">Subtotal:</span>
+                <span className="font-medium">{t('law10.dataMaturity.investment.phase1.subtotal')}</span>
                 <span className="font-bold text-blue-600">22-35K</span>
               </li>
             </ul>
@@ -1435,22 +1437,22 @@ function DataMaturitySummary({ data }: { data: AnalysisData }) {
 
           {/* Fase 2 */}
           <div className="p-3 bg-white rounded border border-gray-200">
-            <p className="font-medium text-gray-800 mb-2">Fase 2 - Operaciones (Q2-Q3 2026)</p>
+            <p className="font-medium text-gray-800 mb-2">{t('law10.dataMaturity.investment.phase2.title')}</p>
             <ul className="space-y-1 text-sm text-gray-600">
               <li className="flex justify-between">
-                <span>• Cobertura 24/7 (chatbot + on-call)</span>
-                <span className="font-semibold">65K/año</span>
+                <span>{t('law10.dataMaturity.investment.phase2.coverage247')}</span>
+                <span className="font-semibold">65K/yr</span>
               </li>
               <li className="flex justify-between">
-                <span>• Copilot IA (reducir AHT)</span>
-                <span className="font-semibold">35K + 8K/mes</span>
+                <span>{t('law10.dataMaturity.investment.phase2.aiCopilot')}</span>
+                <span className="font-semibold">35K + 8K/mo</span>
               </li>
               <li className="flex justify-between">
-                <span>• Auditor ENAC</span>
-                <span className="font-semibold">12-18K/año</span>
+                <span>{t('law10.dataMaturity.investment.phase2.enacAuditor')}</span>
+                <span className="font-semibold">12-18K/yr</span>
               </li>
               <li className="flex justify-between border-t border-gray-100 pt-1 mt-1">
-                <span className="font-medium">Subtotal año 1:</span>
+                <span className="font-medium">{t('law10.dataMaturity.investment.phase2.subtotalYear1')}</span>
                 <span className="font-bold text-blue-600">112-118K</span>
               </li>
             </ul>
@@ -1460,21 +1462,21 @@ function DataMaturitySummary({ data }: { data: AnalysisData }) {
         {/* Totales - usar datos reales cuando disponibles */}
         <div className="grid grid-cols-3 gap-4 pt-4 border-t border-gray-200">
           <div className="text-center">
-            <p className="text-xs text-gray-500 mb-1">Inversion Total</p>
+            <p className="text-xs text-gray-500 mb-1">{t('law10.dataMaturity.investment.totals.totalInvestment')}</p>
             <p className="text-xl font-bold text-blue-600">
               {currentAnnualCost > 0 ? formatCurrency(Math.round(currentAnnualCost * 0.05)) : '134-153K'}
             </p>
-            <p className="text-xs text-gray-400">~5% coste anual</p>
+            <p className="text-xs text-gray-400">{t('law10.dataMaturity.investment.totals.percentAnnualCost')}</p>
           </div>
           <div className="text-center">
-            <p className="text-xs text-gray-500 mb-1">Riesgo Evitado</p>
+            <p className="text-xs text-gray-500 mb-1">{t('law10.dataMaturity.investment.totals.riskAvoided')}</p>
             <p className="text-xl font-bold text-red-600">
               {currentAnnualCost > 0 ? formatCurrency(Math.min(1000000, currentAnnualCost * 0.3)) : '750K-1M'}
             </p>
-            <p className="text-xs text-gray-400">sanciones potenciales</p>
+            <p className="text-xs text-gray-400">{t('law10.dataMaturity.investment.totals.potentialSanctions')}</p>
           </div>
           <div className="text-center">
-            <p className="text-xs text-gray-500 mb-1">ROI Compliance</p>
+            <p className="text-xs text-gray-500 mb-1">{t('law10.dataMaturity.investment.totals.complianceRoi')}</p>
             <p className="text-xl font-bold text-emerald-600">
               {data.economicModel?.roi3yr ? `${data.economicModel.roi3yr}%` : '490-650%'}
             </p>

frontend/components/tabs/RoadmapTab.tsx

@@ -187,7 +187,8 @@ const calcularPaybackCompleto = (
   ahorroAnual: number,
   waves: string[],
   esHabilitador: boolean,
-  incluyeQuickWin: boolean
+  incluyeQuickWin: boolean,
+  t: any
 ): PaybackInfo => {
   // 1. Caso especial: escenario habilitador con poco ahorro directo
   if (esHabilitador || ahorroAnual < inversion * 0.1) {
@@ -195,11 +196,10 @@ const calcularPaybackCompleto = (
       meses: -1,
       mesesImplementacion: calcularMesesImplementacion(waves, incluyeQuickWin),
       mesesRecuperacion: -1,
-      texto: 'Ver Wave 3-4',
+      texto: t('roadmap.payback.seeWave34'),
       clase: 'text-blue-600',
       esRecuperable: false,
-      tooltip: 'Esta inversión se recupera con las waves de automatización (W3-W4). ' +
-               'El payback se calcula sobre el roadmap completo, no sobre waves habilitadoras aisladas.'
+      tooltip: t('roadmap.payback.recoversWithAutomation')
     };
   }
 
@@ -212,11 +212,10 @@ const calcularPaybackCompleto = (
       meses: -1,
       mesesImplementacion: 0,
       mesesRecuperacion: -1,
-      texto: 'No recuperable',
+      texto: t('roadmap.payback.notRecoverable'),
       clase: 'text-red-600',
       esRecuperable: false,
-      tooltip: 'El ahorro anual no supera los costes recurrentes. ' +
-               `Margen neto: ${formatCurrency(margenAnual)}/año`
+      tooltip: t('roadmap.payback.savingsDoNotCoverRecurringWithMargin', { margin: formatCurrency(margenAnual) })
     };
   }
 
@@ -230,7 +229,7 @@ const calcularPaybackCompleto = (
   const paybackTotal = mesesImplementacion + mesesRecuperacion;
 
   // 7. Formatear resultado según duración
-  return formatearPaybackResult(paybackTotal, mesesImplementacion, mesesRecuperacion, margenMensual, inversion);
+  return formatearPaybackResult(paybackTotal, mesesImplementacion, mesesRecuperacion, margenMensual, inversion, t);
 };
 
 /**
@@ -241,17 +240,21 @@ const formatearPaybackResult = (
   mesesImpl: number,
   mesesRec: number,
   margenMensual: number,
-  inversion: number
+  inversion: number,
+  t: any
 ): PaybackInfo => {
-  const tooltipBase = `Implementación: ${mesesImpl} meses → Recuperación: ${mesesRec} meses. ` +
-                     `Margen: ${formatCurrency(margenMensual * 12)}/año.`;
+  const tooltipBase = t('roadmap.payback.implementationRecoveryMargin', {
+    impl: mesesImpl,
+    rec: mesesRec,
+    margin: formatCurrency(margenMensual * 12)
+  });
 
   if (meses <= 0) {
     return {
       meses: 0,
       mesesImplementacion: mesesImpl,
       mesesRecuperacion: mesesRec,
-      texto: 'Inmediato',
+      texto: t('roadmap.payback.immediate'),
       clase: 'text-emerald-600',
       esRecuperable: true,
       tooltip: tooltipBase
@@ -290,7 +293,7 @@ const formatearPaybackResult = (
       texto: `${meses} meses`,
       clase: 'text-amber-600',
       esRecuperable: true,
-      tooltip: tooltipBase + ' ⚠️ Periodo de recuperación moderado.'
+      tooltip: tooltipBase + ' ⚠️ ' + t('roadmap.payback.moderateRecoveryPeriod')
     };
   }
 
@@ -303,7 +306,7 @@ const formatearPaybackResult = (
     texto: `${anos} años`,
     clase: 'text-orange-600',
     esRecuperable: true,
-    tooltip: tooltipBase + ' ⚠️ Periodo de recuperación largo. Considerar escenario menos ambicioso.'
+    tooltip: tooltipBase + ' ⚠️ ' + t('roadmap.payback.longRecoveryPeriod')
   };
 };
 
@@ -393,11 +396,12 @@ interface BubbleDataPoint {
 }
 
 // v3.5: Colores por Tier
+// Note: labels are now set dynamically using t() in the component
 const TIER_COLORS: Record<AgenticTier, { fill: string; stroke: string; label: string }> = {
-  'AUTOMATE': { fill: '#059669', stroke: '#047857', label: 'Automatizar' },
-  'ASSIST': { fill: '#3B82F6', stroke: '#2563EB', label: 'Asistir' },
-  'AUGMENT': { fill: '#F59E0B', stroke: '#D97706', label: 'Optimizar' },
-  'HUMAN-ONLY': { fill: '#EF4444', stroke: '#DC2626', label: 'Humano' }
+  'AUTOMATE': { fill: '#059669', stroke: '#047857', label: '' },
+  'ASSIST': { fill: '#3B82F6', stroke: '#2563EB', label: '' },
+  'AUGMENT': { fill: '#F59E0B', stroke: '#D97706', label: '' },
+  'HUMAN-ONLY': { fill: '#EF4444', stroke: '#DC2626', label: '' }
 };
 
 // v3.6: Constantes CPI para cálculo de ahorro TCO
@@ -1013,14 +1017,14 @@ function WaveCard({
             {entryCriteria && (
               <div className="p-2.5 bg-blue-50 rounded-lg border border-blue-200">
                 <p className="text-[10px] text-blue-700 font-bold mb-1.5 flex items-center gap-1">
-                  <ArrowRight className="w-3 h-3" /> ENTRADA
+                  <ArrowRight className="w-3 h-3" /> {t('roadmap.table.entry')}
                 </p>
                 <div className="space-y-1 text-[10px]">
                   <p className="text-blue-600">
-                    <span className="font-medium">Tier:</span> {entryCriteria.tierFrom.join(', ')}
+                    <span className="font-medium">{t('roadmap.table.tierLabel')}</span> {entryCriteria.tierFrom.join(', ')}
                   </p>
                   <p className="text-blue-600">
-                    <span className="font-medium">Score:</span> {entryCriteria.scoreRange}
+                    <span className="font-medium">{t('roadmap.table.scoreLabel')}</span> {entryCriteria.scoreRange}
                   </p>
                   <div className="pt-1 border-t border-blue-200 mt-1">
                     {entryCriteria.requiredMetrics.map((m, i) => (
@@ -1035,14 +1039,14 @@ function WaveCard({
             {exitCriteria && (
               <div className="p-2.5 bg-emerald-50 rounded-lg border border-emerald-200">
                 <p className="text-[10px] text-emerald-700 font-bold mb-1.5 flex items-center gap-1">
-                  <CheckCircle className="w-3 h-3" /> SALIDA
+                  <CheckCircle className="w-3 h-3" /> {t('roadmap.table.exit')}
                 </p>
                 <div className="space-y-1 text-[10px]">
                   <p className="text-emerald-600">
-                    <span className="font-medium">Tier:</span> {exitCriteria.tierTo}
+                    <span className="font-medium">{t('roadmap.table.tierLabel')}</span> {exitCriteria.tierTo}
                   </p>
                   <p className="text-emerald-600">
-                    <span className="font-medium">Score:</span> {exitCriteria.scoreTarget}
+                    <span className="font-medium">{t('roadmap.table.scoreLabel')}</span> {exitCriteria.scoreTarget}
                   </p>
                   <div className="pt-1 border-t border-emerald-200 mt-1">
                     {exitCriteria.kpiTargets.map((k, i) => (
@@ -1067,19 +1071,19 @@ function WaveCard({
             <div className="bg-gray-100 px-3 py-2 border-b border-gray-200">
               <p className="text-xs font-semibold text-gray-700 flex items-center gap-1">
                 <Target className="w-3.5 h-3.5 text-blue-500" />
-                Top Colas por Volumen × Impacto
+                {t('roadmap.table.topQueuesByVolumeImpact')}
               </p>
             </div>
             <div className="overflow-x-auto">
               <table className="w-full text-[10px]">
                 <thead className="bg-gray-50">
                   <tr>
-                    <th className="text-left py-1.5 px-2 font-medium text-gray-500">Cola</th>
-                    <th className="text-right py-1.5 px-2 font-medium text-gray-500">Vol/mes</th>
-                    <th className="text-right py-1.5 px-2 font-medium text-gray-500">Score</th>
-                    <th className="text-center py-1.5 px-2 font-medium text-gray-500">Tier</th>
-                    <th className="text-left py-1.5 px-2 font-medium text-gray-500">Red Flags</th>
-                    <th className="text-right py-1.5 px-2 font-medium text-gray-500">Potencial</th>
+                    <th className="text-left py-1.5 px-2 font-medium text-gray-500">{t('roadmap.table.queue')}</th>
+                    <th className="text-right py-1.5 px-2 font-medium text-gray-500">{t('roadmap.table.volPerMonth')}</th>
+                    <th className="text-right py-1.5 px-2 font-medium text-gray-500">{t('roadmap.table.score')}</th>
+                    <th className="text-center py-1.5 px-2 font-medium text-gray-500">{t('roadmap.table.tier')}</th>
+                    <th className="text-left py-1.5 px-2 font-medium text-gray-500">{t('roadmap.table.redFlags')}</th>
+                    <th className="text-right py-1.5 px-2 font-medium text-gray-500">{t('roadmap.table.potential')}</th>
                   </tr>
                 </thead>
                 <tbody>
@@ -1128,14 +1132,14 @@ function WaveCard({
             </div>
             {/* v3.7: Nota explicativa de Red Flags */}
             <div className="px-3 py-1.5 bg-gray-50 border-t border-gray-200 text-[9px] text-gray-500">
-              <span className="font-medium">Red Flags:</span> CV &gt;120% (alta variabilidad) · Transfer &gt;50% (proceso fragmentado) · Vol &lt;50 (muestra pequeña) · Valid &lt;30% (datos ruidosos)
+              {t('roadmap.table.redFlagsNote')}
             </div>
           </div>
         )}
 
         {/* Skills afectados */}
         <div>
-          <p className="text-xs text-gray-500 font-medium mb-2">Skills ({wave.skills.length}):</p>
+          <p className="text-xs text-gray-500 font-medium mb-2">{t('roadmap.table.skills')} ({wave.skills.length}):</p>
           <div className="flex flex-wrap gap-1">
             {wave.skills.map((skill, idx) => (
               <span key={idx} className="text-xs bg-gray-100 text-gray-600 px-2 py-1 rounded">
@@ -1152,15 +1156,15 @@ function WaveCard({
             <p className="text-sm font-bold text-red-700">{formatCurrency(wave.inversionSetup)}</p>
           </div>
           <div className="p-2 bg-amber-50 rounded border border-amber-100">
-            <p className="text-[10px] text-amber-600 font-medium">Recurrente/año</p>
+            <p className="text-[10px] text-amber-600 font-medium">{t('roadmap.table.recurringPerYear')}</p>
             <p className="text-sm font-bold text-amber-700">{formatCurrency(wave.costoRecurrenteAnual)}</p>
           </div>
           <div className="p-2 bg-emerald-50 rounded border border-emerald-100">
-            <p className="text-[10px] text-emerald-600 font-medium">Ahorro/año</p>
+            <p className="text-[10px] text-emerald-600 font-medium">{t('roadmap.comparison.savingsPerYear')}</p>
             <p className="text-sm font-bold text-emerald-700">{formatCurrency(wave.ahorroAnual)}</p>
           </div>
           <div className="p-2 bg-blue-50 rounded border border-blue-100">
-            <p className="text-[10px] text-blue-600 font-medium">Margen/año</p>
+            <p className="text-[10px] text-blue-600 font-medium">{t('roadmap.comparison.marginPerYear')}</p>
             <p className="text-sm font-bold text-blue-700">{formatCurrency(margenAnual)}</p>
           </div>
         </div>
@@ -1178,7 +1182,7 @@ function WaveCard({
           <div className="space-y-4 pt-2">
             {/* Iniciativas */}
             <div>
-              <p className="text-xs text-gray-500 font-medium mb-2">Iniciativas:</p>
+              <p className="text-xs text-gray-500 font-medium mb-2">{t('roadmap.table.initiativesLabel')}</p>
               <div className="space-y-2">
                 {wave.iniciativas.map((init, idx) => (
                   <div key={idx} className="flex items-start gap-2 p-2 bg-gray-50 rounded text-xs">
@@ -1188,9 +1192,9 @@ function WaveCard({
                     <div className="flex-1">
                       <p className="font-medium text-gray-700">{init.nombre}</p>
                       <p className="text-gray-500">
-                        Setup: {formatCurrency(init.setup)} | Rec: {formatCurrency(init.recurrente)}/mes
+                        {t('roadmap.table.setup')} {formatCurrency(init.setup)} | {t('roadmap.table.rec')} {formatCurrency(init.recurrente)}{t('agenticReadiness.table.perMonth')}
                       </p>
-                      <p className="text-blue-600 mt-1">KPI: {init.kpi}</p>
+                      <p className="text-blue-600 mt-1">{t('roadmap.table.kpi')} {init.kpi}</p>
                     </div>
                   </div>
                 ))}
@@ -1199,7 +1203,7 @@ function WaveCard({
 
             {/* Criterios de éxito */}
             <div>
-              <p className="text-xs text-gray-500 font-medium mb-2">✅ Criterios de éxito:</p>
+              <p className="text-xs text-gray-500 font-medium mb-2">{t('roadmap.table.successCriteriaLabel')}</p>
               <ul className="space-y-1">
                 {wave.criteriosExito.map((criterio, idx) => (
                   <li key={idx} className="text-xs text-gray-600 flex items-start gap-2">
@@ -1214,14 +1218,14 @@ function WaveCard({
             {wave.esCondicional && wave.condicion && (
               <div className="p-2 bg-amber-50 rounded border border-amber-200">
                 <p className="text-xs text-amber-700">
-                  <strong>⚠️ Condición:</strong> {wave.condicion}
+                  <strong>{t('roadmap.table.condition')}</strong> {wave.condicion}
                 </p>
               </div>
             )}
 
             {/* Proveedor */}
             <div className="text-xs text-gray-500">
-              <strong>Proveedor:</strong> {wave.proveedor}
+              <strong>{t('roadmap.table.provider')}</strong> {wave.proveedor}
             </div>
           </div>
         )}
@@ -1244,11 +1248,11 @@ function ScenarioComparison({ escenarios }: { escenarios: EscenarioData[] }) {
       <div className="p-4 border-b border-gray-200 bg-gray-50">
         <h3 className="font-semibold text-gray-800 flex items-center gap-2">
           <Target className="w-5 h-5 text-blue-500" />
-          Escenarios de Inversión
+          {t('roadmap.comparison.title')}
         </h3>
         <p className="text-xs text-gray-500 mt-1">
-          Comparación de opciones según nivel de compromiso
-          <span className="ml-2 text-gray-400" title="ROI basado en benchmarks de industria. El ROI ajustado considera factores de riesgo de implementación.">
+          {t('roadmap.comparison.subtitle')}
+          <span className="ml-2 text-gray-400" title={t('roadmap.comparison.tooltip')}>
             ℹ️
           </span>
         </p>
@@ -1258,20 +1262,20 @@ function ScenarioComparison({ escenarios }: { escenarios: EscenarioData[] }) {
         <table className="w-full text-sm">
           <thead className="bg-gray-50">
             <tr>
-              <th className="text-left py-3 px-4 font-medium text-gray-600">Escenario</th>
-              <th className="text-right py-3 px-4 font-medium text-gray-600">Inversión</th>
-              <th className="text-right py-3 px-4 font-medium text-gray-600">Recurrente</th>
+              <th className="text-left py-3 px-4 font-medium text-gray-600">{t('roadmap.comparison.scenario')}</th>
+              <th className="text-right py-3 px-4 font-medium text-gray-600">{t('roadmap.comparison.investment')}</th>
+              <th className="text-right py-3 px-4 font-medium text-gray-600">{t('roadmap.comparison.recurring')}</th>
               <th className="text-right py-3 px-4 font-medium text-gray-600">
-                Ahorro
-                <span className="block text-[10px] text-gray-400 font-normal">(ajustado)</span>
+                {t('roadmap.comparison.savings')}
+                <span className="block text-[10px] text-gray-400 font-normal">({t('roadmap.comparison.adjusted')})</span>
               </th>
-              <th className="text-right py-3 px-4 font-medium text-gray-600">Margen</th>
-              <th className="text-right py-3 px-4 font-medium text-gray-600">Payback</th>
+              <th className="text-right py-3 px-4 font-medium text-gray-600">{t('roadmap.comparison.margin')}</th>
+              <th className="text-right py-3 px-4 font-medium text-gray-600">{t('roadmap.comparison.payback')}</th>
               <th className="text-right py-3 px-4 font-medium text-gray-600">
-                ROI 3a
-                <span className="block text-[10px] text-gray-400 font-normal">(ajustado)</span>
+                {t('roadmap.comparison.roi3y')}
+                <span className="block text-[10px] text-gray-400 font-normal">({t('roadmap.comparison.adjusted')})</span>
               </th>
-              <th className="text-center py-3 px-4 font-medium text-gray-600">Riesgo</th>
+              <th className="text-center py-3 px-4 font-medium text-gray-600">{t('roadmap.comparison.risk')}</th>
             </tr>
           </thead>
           <tbody>
@@ -1292,10 +1296,10 @@ function ScenarioComparison({ escenarios }: { escenarios: EscenarioData[] }) {
                   <td className="py-3 px-4">
                     <div className="flex items-center gap-2">
                       {esc.esHabilitador && (
-                        <span className="text-blue-500" title="Wave habilitadora - su valor está en desbloquear waves posteriores">💡</span>
+                        <span className="text-blue-500" title={t('roadmap.comparison.enablerWaveTooltip')}>💡</span>
                       )}
                       {!esc.esRentable && !esc.esHabilitador && (
-                        <span className="text-red-500" title="Margen anual negativo">❌</span>
+                        <span className="text-red-500" title={t('roadmap.comparison.negativeMarginTooltip')}>❌</span>
                       )}
                       <span className={`font-medium ${
                         esc.esHabilitador ? 'text-blue-700' :
@@ -1306,12 +1310,12 @@ function ScenarioComparison({ escenarios }: { escenarios: EscenarioData[] }) {
                       </span>
                       {esc.esHabilitador && (
                         <span className="text-[10px] bg-blue-500 text-white px-2 py-0.5 rounded-full">
-                          Habilitador
+                          {t('roadmap.comparison.enabler')}
                         </span>
                       )}
                       {esc.esRecomendado && !esc.esHabilitador && esc.esRentable && (
                         <span className="text-[10px] bg-emerald-500 text-white px-2 py-0.5 rounded-full">
-                          Recomendado
+                          {t('roadmap.comparison.recommended')}
                         </span>
                       )}
                     </div>
@@ -1321,29 +1325,29 @@ function ScenarioComparison({ escenarios }: { escenarios: EscenarioData[] }) {
                     {formatCurrency(esc.inversionTotal)}
                   </td>
                   <td className="text-right py-3 px-4 text-amber-600">
-                    {formatCurrency(esc.costoRecurrenteAnual)}/año
+                    {formatCurrency(esc.costoRecurrenteAnual)}{t('agenticReadiness.table.perYear')}
                   </td>
                   <td className="text-right py-3 px-4">
-                    <div className="text-emerald-600">{formatCurrency(esc.ahorroAnual)}/año</div>
+                    <div className="text-emerald-600">{formatCurrency(esc.ahorroAnual)}{t('agenticReadiness.table.perYear')}</div>
                     {esc.esHabilitador && esc.potencialHabilitado > 0 && (
-                      <div className="text-[10px] text-blue-600" title={`Desbloquea ${esc.wavesHabilitadas.join(', ')}`}>
-                        (habilita {formatCurrency(esc.potencialHabilitado)})
+                      <div className="text-[10px] text-blue-600" title={t('roadmap.scenarios.unlocks', { waves: esc.wavesHabilitadas.join(', ') })}>
+                        ({t('roadmap.scenarios.enablesAmount', { amount: formatCurrency(esc.potencialHabilitado) })})
                       </div>
                     )}
                     {!esc.esHabilitador && esc.ahorroAjustado !== esc.ahorroAnual && (
                       <div className="text-[10px] text-gray-500">
-                        ({formatCurrency(esc.ahorroAjustado)} ajust.)
+                        ({formatCurrency(esc.ahorroAjustado)} {t('roadmap.comparison.adjusted')})
                       </div>
                     )}
                   </td>
                   <td className="text-right py-3 px-4">
                     {esc.esHabilitador ? (
                       <span className="text-xs px-2 py-1 bg-blue-100 text-blue-700 rounded-full font-medium">
-                        Prerrequisito
+                        {t('roadmap.comparison.prerequisite')}
                       </span>
                     ) : (
                       <span className={`font-bold ${esc.margenAnual <= 0 ? 'text-red-600' : 'text-blue-600'}`}>
-                        {esc.margenAnual <= 0 ? '-' : ''}{formatCurrency(Math.abs(esc.margenAnual))}/año
+                        {esc.margenAnual <= 0 ? '-' : ''}{formatCurrency(Math.abs(esc.margenAnual))}{t('agenticReadiness.table.perYear')}
                       </span>
                     )}
                   </td>
@@ -1368,8 +1372,8 @@ function ScenarioComparison({ escenarios }: { escenarios: EscenarioData[] }) {
                   <td className="text-right py-3 px-4">
                     {esc.esHabilitador ? (
                       <span className="text-xs px-2 py-1 bg-blue-100 text-blue-700 rounded-full font-medium"
-                        title="El ROI se calcula sobre el roadmap completo">
-                        Prerrequisito
+                        title={t('roadmap.comparison.roiCalculatedOn')}>
+                        {t('roadmap.comparison.prerequisite')}
                       </span>
                     ) : (
                       <div className="flex flex-col items-end">
@@ -1379,12 +1383,12 @@ function ScenarioComparison({ escenarios }: { escenarios: EscenarioData[] }) {
                         }`}>
                           {roiInfo.text}
                           {roiInfo.isHighWarning && (
-                            <span className="ml-1" title="ROI proyectado. Validar con piloto.">⚠️</span>
+                            <span className="ml-1" title={t('roadmap.comparison.projectedRoiTooltip')}>⚠️</span>
                           )}
                         </span>
                         {roiInfo.showAjustado && esc.roi3AnosAjustado > 0 && (
-                          <span className="text-[10px] text-gray-500" title="ROI ajustado por riesgo de implementación">
-                            ({esc.roi3AnosAjustado.toFixed(1)}% ajust.)
+                          <span className="text-[10px] text-gray-500" title={t('roadmap.comparison.adjustedRoiTooltip')}>
+                            ({esc.roi3AnosAjustado.toFixed(1)}% {t('roadmap.comparison.adjusted').slice(0, 5)}.)
                           </span>
                         )}
                       </div>
@@ -1392,7 +1396,7 @@ function ScenarioComparison({ escenarios }: { escenarios: EscenarioData[] }) {
                   </td>
                   <td className="text-center py-3 px-4">
                     <span className={`text-xs px-2 py-1 rounded-full ${riesgoColors[esc.riesgo]}`}>
-                      {esc.riesgo.charAt(0).toUpperCase() + esc.riesgo.slice(1)}
+                      {t(`roadmap.comparison.risk${esc.riesgo.charAt(0).toUpperCase() + esc.riesgo.slice(1)}`)}
                     </span>
                   </td>
                 </tr>
@@ -1404,13 +1408,11 @@ function ScenarioComparison({ escenarios }: { escenarios: EscenarioData[] }) {
 
       {/* Nota sobre cálculos */}
       <div className="px-4 py-2 bg-gray-50 border-t border-gray-200 text-[10px] text-gray-500">
-        <strong>Payback:</strong> Tiempo implementación + tiempo recuperación.
-        Wave 1: 6m, W2: 3m, W3: 3m, W4: 6m. Ahorro comienza al 50% de última wave.
+        {t('roadmap.comparison.paybackNote')}
         <br />
-        <strong>ROI:</strong> (Ahorro 3a - Coste Total 3a) / Coste Total 3a × 100.
-        Ajustado aplica riesgo: W1-2: 75-90%, W3: 60%, W4: 50%.
+        {t('roadmap.comparison.roiNote')}
         <br />
-        <strong>💡 Habilitador:</strong> Waves que desbloquean ROI de waves posteriores. Su payback se evalúa con el roadmap completo.
+        <strong>💡 {t('roadmap.comparison.enabler')}:</strong> {t('roadmap.comparison.enablerNote')}
       </div>
 
       {/* Recomendación destacada */}
@@ -1437,16 +1439,18 @@ function ScenarioComparison({ escenarios }: { escenarios: EscenarioData[] }) {
               )}
               <div className="flex-1">
                 <p className={`text-sm font-medium ${textColor}`}>
-                  {isEnabling ? 'Recomendación (Habilitador)' : 'Recomendación'}
+                  {isEnabling ? t('roadmap.comparison.recommendationEnabler') : t('roadmap.comparison.recommendation')}
                 </p>
                 <p className={`text-xs mt-1 ${subTextColor}`}>
-                  {recomendado?.recomendacion || 'Iniciar con escenario conservador para validar modelo antes de escalar.'}
+                  {recomendado?.recomendacion || t('roadmap.scenarios.startConservative')}
                 </p>
                 {isEnabling && recomendado?.potencialHabilitado > 0 && (
                   <div className="mt-2 p-2 bg-white/60 rounded border border-blue-200">
                     <p className="text-xs text-blue-800">
-                      <strong>💡 Valor real de esta inversión:</strong> Desbloquea {formatCurrency(recomendado.potencialHabilitado)}/año
-                      en {recomendado.wavesHabilitadas.join(' y ')}. Sin esta base, las waves posteriores no son viables.
+                      <strong>{t('roadmap.scenarios.enablerValue')}</strong> {t('roadmap.scenarios.enablerUnlocks', {
+                        amount: formatCurrency(recomendado.potencialHabilitado),
+                        waves: recomendado.wavesHabilitadas.join(' y ')
+                      })}
                     </p>
                   </div>
                 )}
@@ -1472,34 +1476,38 @@ interface DecisionGate {
 }
 
 // v3.6: Decision Gates alineados con nueva nomenclatura y criterios de Tier
-const DECISION_GATES: DecisionGate[] = [
+// Note: Decision gates are rendered using translation keys dynamically
+const getDecisionGates = (t: any): DecisionGate[] => [
   {
     id: 'gate1',
     afterWave: 'wave1',
-    question: '¿CV ≤75% en 3+ colas?',
-    criteria: 'Red flags eliminados, Tier 4→3',
-    goAction: 'Iniciar AUGMENT',
-    noGoAction: 'Extender FOUNDATION'
+    question: t('roadmap.decisionGates.gate1Question'),
+    criteria: t('roadmap.decisionGates.gate1Criteria'),
+    goAction: t('roadmap.decisionGates.gate1GoAction'),
+    noGoAction: t('roadmap.decisionGates.gate1NoGoAction')
   },
   {
     id: 'gate2',
     afterWave: 'wave2',
-    question: '¿Score ≥5.5 en target?',
-    criteria: 'CV ≤90%, Transfer ≤30%',
-    goAction: 'Iniciar ASSIST',
-    noGoAction: 'Consolidar AUGMENT'
+    question: t('roadmap.decisionGates.gate2Question'),
+    criteria: t('roadmap.decisionGates.gate2Criteria'),
+    goAction: t('roadmap.decisionGates.gate2GoAction'),
+    noGoAction: t('roadmap.decisionGates.gate2NoGoAction')
   },
   {
     id: 'gate3',
     afterWave: 'wave3',
-    question: '¿Score ≥7.5 en 2+ colas?',
-    criteria: 'CV ≤75%, FCR ≥50%',
-    goAction: 'Lanzar AUTOMATE',
-    noGoAction: 'Expandir ASSIST'
+    question: t('roadmap.decisionGates.gate3Question'),
+    criteria: t('roadmap.decisionGates.gate3Criteria'),
+    goAction: t('roadmap.decisionGates.gate3GoAction'),
+    noGoAction: t('roadmap.decisionGates.gate3NoGoAction')
   }
 ];
 
 function RoadmapTimeline({ waves }: { waves: WaveData[] }) {
+  const { t } = useTranslation();
+  const DECISION_GATES = getDecisionGates(t);
+
   const waveColors: Record<string, { bg: string; border: string; connector: string }> = {
     wave1: { bg: 'bg-blue-100', border: 'border-blue-400', connector: 'bg-blue-400' },
     wave2: { bg: 'bg-emerald-100', border: 'border-emerald-400', connector: 'bg-emerald-400' },
@@ -1509,8 +1517,8 @@ function RoadmapTimeline({ waves }: { waves: WaveData[] }) {
 
   return (
     <div className="bg-white rounded-xl border border-gray-200 p-6">
-      <h3 className="font-semibold text-gray-800 mb-2">Roadmap de Transformación 2026-2027</h3>
-      <p className="text-xs text-gray-500 mb-6">Cada wave depende del éxito de la anterior. Los puntos de decisión permiten ajustar según resultados reales.</p>
+      <h3 className="font-semibold text-gray-800 mb-2">{t('roadmap.timeline.title')}</h3>
+      <p className="text-xs text-gray-500 mb-6">{t('roadmap.timeline.subtitle')}</p>
 
       {/* Timeline horizontal con waves y gates */}
       <div className="relative">
@@ -1552,11 +1560,11 @@ function RoadmapTimeline({ waves }: { waves: WaveData[] }) {
                     {/* Wave metrics */}
                     <div className="grid grid-cols-2 gap-1 text-[10px]">
                       <div className="bg-white/60 rounded px-1.5 py-1">
-                        <span className="text-gray-500">Setup:</span>
+                        <span className="text-gray-500">{t('roadmap.timeline.setup')}</span>
                         <span className="font-semibold text-gray-700 ml-1">{formatCurrency(wave.inversionSetup)}</span>
                       </div>
                       <div className="bg-white/60 rounded px-1.5 py-1">
-                        <span className="text-gray-500">Ahorro:</span>
+                        <span className="text-gray-500">{t('roadmap.comparison.savingsLabel')}</span>
                         <span className="font-semibold text-emerald-600 ml-1">{formatCurrency(wave.ahorroAnual)}</span>
                       </div>
                     </div>
@@ -1564,7 +1572,7 @@ function RoadmapTimeline({ waves }: { waves: WaveData[] }) {
                     {/* Conditional badge */}
                     {wave.esCondicional && (
                       <div className="absolute -top-2 -right-2 bg-amber-500 text-white text-[8px] px-1.5 py-0.5 rounded-full font-medium">
-                        Condicional
+                        {t('roadmap.comparison.conditional')}
                       </div>
                     )}
 
@@ -1574,7 +1582,7 @@ function RoadmapTimeline({ waves }: { waves: WaveData[] }) {
                       wave.riesgo === 'medio' ? 'bg-amber-500 text-white' :
                       'bg-red-500 text-white'
                     }`}>
-                      {wave.riesgo === 'bajo' ? '● Bajo' : wave.riesgo === 'medio' ? '● Medio' : '● Alto'}
+                      ● {t(`roadmap.comparison.risk${wave.riesgo.charAt(0).toUpperCase() + wave.riesgo.slice(1)}`)}
                     </div>
                   </div>
                 </motion.div>
@@ -1710,8 +1718,15 @@ export function RoadmapTab({ data }: RoadmapTabProps) {
 
   // Generar texto dinámico para Wave 2
   const wave2Description = skillsListos > 0
-    ? `${bestSkill?.skill || 'Skill principal'} es el skill con mejor Score (${bestSkillScore.toFixed(1)}/10, categoría "Copilot"). Volumen ${bestSkillVolume.toLocaleString()}/año = mayor impacto económico.`
-    : `Ningún skill alcanza actualmente Score ≥6. El mejor candidato es ${bestSkill?.skill || 'N/A'} con Score ${bestSkillScore.toFixed(1)}/10. Requiere optimización previa en Wave 1.`;
+    ? t('roadmap.wave2Description.ready', {
+        skill: bestSkill?.skill || 'Skill principal',
+        score: bestSkillScore.toFixed(1),
+        volume: bestSkillVolume.toLocaleString()
+      })
+    : t('roadmap.wave2Description.notReady', {
+        skill: bestSkill?.skill || 'N/A',
+        score: bestSkillScore.toFixed(1)
+      });
 
   const wave2Skills = skillsListos > 0
     ? skillsCopilot.map(s => s.skill)
@@ -1761,9 +1776,9 @@ export function RoadmapTab({ data }: RoadmapTabProps) {
   const waves: WaveData[] = [
     {
       id: 'wave1',
-      nombre: 'Wave 1',
-      titulo: 'FOUNDATION',
-      trimestre: 'Q1-Q2 2026',
+      nombre: t('roadmap.waves.wave1Name'),
+      titulo: t('roadmap.waves.wave1Title'),
+      trimestre: t('roadmap.waves.wave1Quarter'),
       tipo: 'consulting',
       icon: <Settings className="w-5 h-5" />,
       color: 'text-gray-600',
@@ -1773,30 +1788,34 @@ export function RoadmapTab({ data }: RoadmapTabProps) {
       costoRecurrenteAnual: 0,
       ahorroAnual: 0, // Wave habilitadora
       esCondicional: false,
-      porQueNecesario: `${tierCounts['HUMAN-ONLY'].length + tierCounts.AUGMENT.length} de ${allQueues.length} colas están en Tier 3-4 (${Math.round((wave1Volume / totalVolume) * 100)}% del volumen). Red flags: CV >75%, Transfer >20%. Automatizar sin estandarizar = fracaso garantizado.`,
+      porQueNecesario: t('roadmap.porQueNecesarioTemplates.wave1', {
+        count: tierCounts['HUMAN-ONLY'].length + tierCounts.AUGMENT.length,
+        total: allQueues.length,
+        pct: Math.round((wave1Volume / totalVolume) * 100)
+      }),
       skills: wave1Queues.length > 0
         ? [...new Set(drilldownData.filter(s => s.originalQueues.some(q => q.tier === 'HUMAN-ONLY' || q.tier === 'AUGMENT')).map(s => s.skill))].slice(0, 5)
         : skillsNeedStandardization.map(s => s.skill).slice(0, 5),
       iniciativas: [
-        { nombre: 'Análisis de variabilidad y red flags', setup: 15000, recurrente: 0, kpi: 'Mapear causas de CV >75% y Transfer >20%' },
-        { nombre: 'Rediseño y documentación de procesos', setup: 20000, recurrente: 0, kpi: 'Scripts estandarizados para 80% casuística' },
-        { nombre: 'Training y certificación de agentes', setup: 12000, recurrente: 0, kpi: 'Certificación 90% agentes, adherencia >85%' }
+        { nombre: t('roadmap.initiatives.wave1Init1'), setup: 15000, recurrente: 0, kpi: t('roadmap.initiatives.wave1Init1Kpi') },
+        { nombre: t('roadmap.initiatives.wave1Init2'), setup: 20000, recurrente: 0, kpi: t('roadmap.initiatives.wave1Init2Kpi') },
+        { nombre: t('roadmap.initiatives.wave1Init3'), setup: 12000, recurrente: 0, kpi: t('roadmap.initiatives.wave1Init3Kpi') }
       ],
       criteriosExito: [
-        `CV AHT ≤75% en al menos ${Math.max(3, Math.ceil(wave1Queues.length * 0.3))} colas de alto volumen`,
-        'Transfer ≤20% global',
-        'Red flags eliminados en colas prioritarias',
-        `Al menos ${Math.ceil(wave1Queues.length * 0.2)} colas migran de Tier 4 → Tier 3`
+        t('roadmap.successCriteriaTemplates.wave1Criterion1', { count: Math.max(3, Math.ceil(wave1Queues.length * 0.3)) }),
+        t('roadmap.successCriteriaTemplates.wave1Criterion2'),
+        t('roadmap.successCriteriaTemplates.wave1Criterion3'),
+        t('roadmap.successCriteriaTemplates.wave1Criterion4', { count: Math.ceil(wave1Queues.length * 0.2) })
       ],
       riesgo: 'bajo',
-      riesgoDescripcion: 'Consultoría con entregables tangibles. No requiere tecnología.',
-      proveedor: 'Beyond Consulting o tercero especializado'
+      riesgoDescripcion: t('roadmap.waves.wave1RiskDescription'),
+      proveedor: t('roadmap.waves.wave1Provider')
     },
     {
       id: 'wave2',
-      nombre: 'Wave 2',
-      titulo: 'AUGMENT',
-      trimestre: 'Q3 2026',
+      nombre: t('roadmap.waves.wave2Name'),
+      titulo: t('roadmap.waves.wave2Title'),
+      trimestre: t('roadmap.waves.wave2Quarter'),
       tipo: 'beyond_consulting',
       icon: <TrendingUp className="w-5 h-5" />,
       color: 'text-amber-600',
@@ -1806,30 +1825,33 @@ export function RoadmapTab({ data }: RoadmapTabProps) {
       costoRecurrenteAnual: 40000,
       ahorroAnual: potentialSavings.AUGMENT, // 15% efficiency - calculado desde datos reales
       esCondicional: true,
-      condicion: 'Requiere CV ≤75% post-Wave 1 en colas target',
-      porQueNecesario: `Implementar herramientas de soporte para colas Tier 3 (Score 3.5-5.5). Objetivo: elevar score a ≥5.5 para habilitar Wave 3. Foco en ${tierCounts.AUGMENT.length} colas con ${tierVolumes.AUGMENT.toLocaleString()} int/mes.`,
+      condicion: t('roadmap.waves.wave2Condition'),
+      porQueNecesario: t('roadmap.porQueNecesarioTemplates.wave2', {
+        count: tierCounts.AUGMENT.length,
+        volume: tierVolumes.AUGMENT.toLocaleString()
+      }),
       skills: tierCounts.AUGMENT.length > 0
         ? [...new Set(drilldownData.filter(s => s.originalQueues.some(q => q.tier === 'AUGMENT')).map(s => s.skill))].slice(0, 4)
-        : ['Colas que alcancen Score 3.5-5.5 post Wave 1'],
+        : [t('roadmap.fallbackSkills.wave1')],
       iniciativas: [
-        { nombre: 'Knowledge Base contextual', setup: 20000, recurrente: 2000, kpi: 'Hold time -25%, uso KB +40%' },
-        { nombre: 'Scripts dinámicos con IA', setup: 15000, recurrente: 1500, kpi: 'Adherencia scripts +30%' }
+        { nombre: t('roadmap.initiatives.wave2Init1'), setup: 20000, recurrente: 2000, kpi: t('roadmap.initiatives.wave2Init1Kpi') },
+        { nombre: t('roadmap.initiatives.wave2Init2'), setup: 15000, recurrente: 1500, kpi: t('roadmap.initiatives.wave2Init2Kpi') }
       ],
       criteriosExito: [
-        'Score promedio sube de 3.5-5.5 → ≥5.5',
-        'AHT -15% vs baseline',
-        'CV ≤90% en colas target',
-        `${Math.ceil(tierCounts.AUGMENT.length * 0.5)} colas migran de Tier 3 → Tier 2`
+        t('roadmap.successCriteriaTemplates.wave2Criterion1'),
+        t('roadmap.successCriteriaTemplates.wave2Criterion2'),
+        t('roadmap.successCriteriaTemplates.wave2Criterion3'),
+        t('roadmap.successCriteriaTemplates.wave2Criterion4', { count: Math.ceil(tierCounts.AUGMENT.length * 0.5) })
       ],
       riesgo: 'bajo',
-      riesgoDescripcion: 'Herramientas de soporte, bajo riesgo de integración.',
-      proveedor: 'BEYOND (KB + Scripts IA)'
+      riesgoDescripcion: t('roadmap.waves.wave2RiskDescription'),
+      proveedor: t('roadmap.waves.wave2Provider')
     },
     {
       id: 'wave3',
-      nombre: 'Wave 3',
-      titulo: 'ASSIST',
-      trimestre: 'Q4 2026',
+      nombre: t('roadmap.waves.wave3Name'),
+      titulo: t('roadmap.waves.wave3Title'),
+      trimestre: t('roadmap.waves.wave3Quarter'),
       tipo: 'beyond',
       icon: <Bot className="w-5 h-5" />,
       color: 'text-blue-600',
@@ -1839,31 +1861,34 @@ export function RoadmapTab({ data }: RoadmapTabProps) {
       costoRecurrenteAnual: 78000,
       ahorroAnual: potentialSavings.ASSIST, // 30% efficiency - calculado desde datos reales
       esCondicional: true,
-      condicion: 'Requiere Score ≥5.5 Y CV ≤90% Y Transfer ≤30%',
-      porQueNecesario: `Copilot IA para agentes en colas Tier 2. Sugerencias en tiempo real, autocompletado, next-best-action. Objetivo: elevar score a ≥7.5 para Wave 4. Target: ${tierCounts.ASSIST.length} colas con ${tierVolumes.ASSIST.toLocaleString()} int/mes.`,
+      condicion: t('roadmap.waves.wave3Condition'),
+      porQueNecesario: t('roadmap.porQueNecesarioTemplates.wave3', {
+        count: tierCounts.ASSIST.length,
+        volume: tierVolumes.ASSIST.toLocaleString()
+      }),
       skills: tierCounts.ASSIST.length > 0
         ? [...new Set(drilldownData.filter(s => s.originalQueues.some(q => q.tier === 'ASSIST')).map(s => s.skill))].slice(0, 4)
-        : ['Colas que alcancen Score ≥5.5 post Wave 2'],
+        : [t('roadmap.fallbackSkills.wave2')],
       iniciativas: [
-        { nombre: 'Agent Assist / Copilot IA', setup: 45000, recurrente: 4500, kpi: 'AHT -30%, sugerencias aceptadas >60%' },
-        { nombre: 'Automatización parcial (FAQs, routing)', setup: 25000, recurrente: 3000, kpi: 'Deflection rate 15%' }
+        { nombre: t('roadmap.initiatives.wave3Init1'), setup: 45000, recurrente: 4500, kpi: t('roadmap.initiatives.wave3Init1Kpi') },
+        { nombre: t('roadmap.initiatives.wave3Init2'), setup: 25000, recurrente: 3000, kpi: t('roadmap.initiatives.wave3Init2Kpi') }
       ],
       criteriosExito: [
-        'Score promedio sube de 5.5-7.5 → ≥7.5',
-        'AHT -30% vs baseline Wave 2',
-        'CV ≤75% en colas target',
-        'Transfer ≤20%',
-        `${Math.ceil(tierCounts.ASSIST.length * 0.4)} colas migran de Tier 2 → Tier 1`
+        t('roadmap.successCriteriaTemplates.wave3Criterion1'),
+        t('roadmap.successCriteriaTemplates.wave3Criterion2'),
+        t('roadmap.successCriteriaTemplates.wave3Criterion3'),
+        t('roadmap.successCriteriaTemplates.wave3Criterion4'),
+        t('roadmap.successCriteriaTemplates.wave3Criterion5', { count: Math.ceil(tierCounts.ASSIST.length * 0.4) })
       ],
       riesgo: 'medio',
-      riesgoDescripcion: 'Integración con plataforma contact center. Piloto 4 semanas mitiga.',
-      proveedor: 'BEYOND (Copilot + Routing IA)'
+      riesgoDescripcion: t('roadmap.waves.wave3RiskDescription'),
+      proveedor: t('roadmap.waves.wave3Provider')
     },
     {
       id: 'wave4',
-      nombre: 'Wave 4',
-      titulo: 'AUTOMATE',
-      trimestre: 'Q1-Q2 2027',
+      nombre: t('roadmap.waves.wave4Name'),
+      titulo: t('roadmap.waves.wave4Title'),
+      trimestre: t('roadmap.waves.wave4Quarter'),
       tipo: 'beyond',
       icon: <Rocket className="w-5 h-5" />,
       color: 'text-emerald-600',
@@ -1873,24 +1898,27 @@ export function RoadmapTab({ data }: RoadmapTabProps) {
       costoRecurrenteAnual: 108000,
       ahorroAnual: potentialSavings.AUTOMATE, // 70% containment - calculado desde datos reales
       esCondicional: true,
-      condicion: 'Requiere Score ≥7.5 Y CV ≤75% Y Transfer ≤20% Y FCR ≥50%',
-      porQueNecesario: `Automatización end-to-end para colas Tier 1. Voicebot/Chatbot transaccional con 70% contención. Solo viable con procesos maduros. Target actual: ${tierCounts.AUTOMATE.length} colas con ${tierVolumes.AUTOMATE.toLocaleString()} int/mes.`,
+      condicion: t('roadmap.waves.wave4Condition'),
+      porQueNecesario: t('roadmap.porQueNecesarioTemplates.wave4', {
+        count: tierCounts.AUTOMATE.length,
+        volume: tierVolumes.AUTOMATE.toLocaleString()
+      }),
       skills: tierCounts.AUTOMATE.length > 0
         ? [...new Set(drilldownData.filter(s => s.originalQueues.some(q => q.tier === 'AUTOMATE')).map(s => s.skill))].slice(0, 4)
-        : ['Colas que alcancen Score ≥7.5 post Wave 3'],
+        : [t('roadmap.fallbackSkills.wave3')],
       iniciativas: [
-        { nombre: 'Voicebot/Chatbot transaccional', setup: 55000, recurrente: 6000, kpi: 'Contención 70%+, CSAT ≥4/5' },
-        { nombre: 'IVR inteligente con NLU', setup: 30000, recurrente: 3000, kpi: 'Pre-calificación 80%+, transferencia warm' }
+        { nombre: t('roadmap.initiatives.wave4Init1'), setup: 55000, recurrente: 6000, kpi: t('roadmap.initiatives.wave4Init1Kpi') },
+        { nombre: t('roadmap.initiatives.wave4Init2'), setup: 30000, recurrente: 3000, kpi: t('roadmap.initiatives.wave4Init2Kpi') }
       ],
       criteriosExito: [
-        'Contención ≥70% en colas automatizadas',
-        'CSAT se mantiene o mejora (≥4/5)',
-        'Escalado a humano <30%',
-        'ROI acumulado >300%'
+        t('roadmap.successCriteriaTemplates.wave4Criterion1'),
+        t('roadmap.successCriteriaTemplates.wave4Criterion2'),
+        t('roadmap.successCriteriaTemplates.wave4Criterion3'),
+        t('roadmap.successCriteriaTemplates.wave4Criterion4')
       ],
       riesgo: 'alto',
-      riesgoDescripcion: 'Muy condicional. Requiere éxito demostrado en Waves 1-3.',
-      proveedor: 'BEYOND (Voicebot + IVR + Chatbot)'
+      riesgoDescripcion: t('roadmap.waves.wave4RiskDescription'),
+      proveedor: t('roadmap.waves.wave4Provider')
     }
   ];
 
@@ -1952,24 +1980,24 @@ export function RoadmapTab({ data }: RoadmapTabProps) {
   // v3.9: Calcular payback completo para cada escenario
   const consPaybackInfo = calcularPaybackCompleto(
     consInversion, consMargen, consSavings,
-    ['wave1', 'wave2'], consEsHabilitador, false
+    ['wave1', 'wave2'], consEsHabilitador, false, t
   );
   const modPaybackInfo = calcularPaybackCompleto(
     modInversion, modMargen, modSavings,
-    ['wave1', 'wave2', 'wave3'], modEsHabilitador, false
+    ['wave1', 'wave2', 'wave3'], modEsHabilitador, false, t
   );
   // Agresivo incluye Wave 4 (Quick Wins potenciales si hay AUTOMATE queues)
   const agrIncluyeQuickWin = tierCounts.AUTOMATE.length >= 3;
   const agrPaybackInfo = calcularPaybackCompleto(
     agrInversion, agrMargen, agrSavings,
-    ['wave1', 'wave2', 'wave3', 'wave4'], agrEsHabilitador, agrIncluyeQuickWin
+    ['wave1', 'wave2', 'wave3', 'wave4'], agrEsHabilitador, agrIncluyeQuickWin, t
   );
 
   const escenarios: EscenarioData[] = [
     {
       id: 'conservador',
-      nombre: 'Conservador',
-      descripcion: 'FOUNDATION + AUGMENT (Wave 1-2)',
+      nombre: t('roadmap.scenarios.conservativeName'),
+      descripcion: t('roadmap.scenarios.conservativeDesc'),
       waves: ['wave1', 'wave2'],
       inversionTotal: consInversion,
       costoRecurrenteAnual: consRec,
@@ -1982,7 +2010,7 @@ export function RoadmapTab({ data }: RoadmapTabProps) {
       roi3AnosAjustado: calculateROI3Years(consInversion, consRec, consSavingsAjustado),
       riesgo: 'bajo',
       recomendacion: consEsHabilitador
-        ? `✅ Recomendado como HABILITADOR. Desbloquea ${formatCurrency(consPotencialHabilitado)}/año en Wave 3-4. Objetivo: mover ${Math.ceil(wave1Queues.length * 0.3)} colas de Tier 4→3.`
+        ? `✅ Recomendado como HABILITADOR. Desbloquea ${formatCurrency(consPotencialHabilitado)}{t('agenticReadiness.table.perYear')} en Wave 3-4. Objetivo: mover ${Math.ceil(wave1Queues.length * 0.3)} colas de Tier 4→3.`
         : `✅ Recomendado. Validar modelo con riesgo bajo. Objetivo: mover ${Math.ceil(wave1Queues.length * 0.3)} colas de Tier 4→3.`,
       esRecomendado: true,
       esRentable: consMargen > 0,
@@ -1993,8 +2021,8 @@ export function RoadmapTab({ data }: RoadmapTabProps) {
     },
     {
       id: 'moderado',
-      nombre: 'Moderado',
-      descripcion: 'FOUNDATION + AUGMENT + ASSIST (Wave 1-3)',
+      nombre: t('roadmap.scenarios.moderateName'),
+      descripcion: t('roadmap.scenarios.moderateDesc'),
       waves: ['wave1', 'wave2', 'wave3'],
       inversionTotal: modInversion,
       costoRecurrenteAnual: modRec,
@@ -2007,7 +2035,7 @@ export function RoadmapTab({ data }: RoadmapTabProps) {
       roi3AnosAjustado: calculateROI3Years(modInversion, modRec, modSavingsAjustado),
       riesgo: 'medio',
       recomendacion: modEsHabilitador
-        ? `Habilitador parcial. Desbloquea ${formatCurrency(modPotencialHabilitado)}/año en Wave 4. Decidir Go/No-Go en Q3 2026.`
+        ? `Habilitador parcial. Desbloquea ${formatCurrency(modPotencialHabilitado)}{t('agenticReadiness.table.perYear')} en Wave 4. Decidir Go/No-Go en Q3 2026.`
         : `Decidir Go/No-Go en Q3 2026 basado en resultados Wave 1-2. Requiere Score ≥5.5 en colas target.`,
       esRecomendado: false,
       esRentable: modMargen > 0,
@@ -2018,8 +2046,8 @@ export function RoadmapTab({ data }: RoadmapTabProps) {
     },
     {
       id: 'agresivo',
-      nombre: 'Agresivo',
-      descripcion: 'Roadmap completo (Wave 1-4)',
+      nombre: t('roadmap.scenarios.aggressiveName'),
+      descripcion: t('roadmap.scenarios.aggressiveDesc'),
       waves: ['wave1', 'wave2', 'wave3', 'wave4'],
       inversionTotal: agrInversion,
       costoRecurrenteAnual: agrRec,
@@ -2228,27 +2256,47 @@ export function RoadmapTab({ data }: RoadmapTabProps) {
 
     if (automateCount >= 3) {
       return {
-        action: 'Lanzar Wave 4 (AUTOMATE) en piloto',
-        rationale: `${automateCount} colas ya tienen Score ≥7.5 con volumen de ${tierVolumes.AUTOMATE.toLocaleString()} int/mes.`,
-        nextStep: `Iniciar piloto de automatización en las 2-3 colas de mayor volumen con ahorro potencial de ${formatCurrency(potentialSavings.AUTOMATE)}/año.`
+        action: t('roadmap.specificRecommendations.launchWave4'),
+        rationale: t('roadmap.specificRecommendations.launchWave4Rationale', {
+          count: automateCount,
+          volume: tierVolumes.AUTOMATE.toLocaleString()
+        }),
+        nextStep: t('roadmap.specificRecommendations.launchWave4NextStep', {
+          amount: formatCurrency(potentialSavings.AUTOMATE)
+        })
       };
     } else if (assistCount >= 5 || pctHighTier >= 30) {
       return {
-        action: 'Iniciar Wave 3 (ASSIST) con Copilot',
-        rationale: `${assistCount} colas tienen Score 5.5-7.5, representando ${Math.round((tierVolumes.ASSIST / totalVolume) * 100)}% del volumen.`,
-        nextStep: `Desplegar Copilot IA en colas Tier 2 para elevar score a ≥7.5 y habilitar Wave 4. Inversión: ${formatCurrency(wave3Setup)}.`
+        action: t('roadmap.specificRecommendations.initiateWave3'),
+        rationale: t('roadmap.specificRecommendations.initiateWave3Rationale', {
+          count: assistCount,
+          pct: Math.round((tierVolumes.ASSIST / totalVolume) * 100)
+        }),
+        nextStep: t('roadmap.specificRecommendations.initiateWave3NextStep', {
+          amount: formatCurrency(wave3Setup)
+        })
       };
     } else if (humanOnlyCount > totalQueues * 0.5) {
       return {
-        action: 'Priorizar Wave 1 (FOUNDATION)',
-        rationale: `${humanOnlyCount} colas (${Math.round((humanOnlyCount / totalQueues) * 100)}%) tienen Red Flags que impiden automatización.`,
-        nextStep: `Estandarizar procesos antes de invertir en IA. La automatización sin fundamentos sólidos fracasa en 80%+ de casos.`
+        action: t('roadmap.specificRecommendations.prioritizeWave1'),
+        rationale: t('roadmap.specificRecommendations.prioritizeWave1Rationale', {
+          count: humanOnlyCount,
+          pct: Math.round((humanOnlyCount / totalQueues) * 100)
+        }),
+        nextStep: t('roadmap.specificRecommendations.prioritizeWave1NextStep')
       };
     } else {
       return {
-        action: 'Ejecutar Wave 1-2 secuencialmente',
-        rationale: `Operación mixta: ${automateCount} colas Tier 1, ${assistCount} Tier 2, ${tierCounts.AUGMENT.length} Tier 3, ${humanOnlyCount} Tier 4.`,
-        nextStep: `Comenzar con FOUNDATION para eliminar red flags, seguido de AUGMENT para elevar scores. Inversión inicial: ${formatCurrency(wave1Setup + wave2Setup)}.`
+        action: t('roadmap.specificRecommendations.executeWave12'),
+        rationale: t('roadmap.specificRecommendations.executeWave12Rationale', {
+          automate: automateCount,
+          assist: assistCount,
+          augment: tierCounts.AUGMENT.length,
+          human: humanOnlyCount
+        }),
+        nextStep: t('roadmap.specificRecommendations.executeWave12NextStep', {
+          amount: formatCurrency(wave1Setup + wave2Setup)
+        })
       };
     }
   };
@@ -2482,7 +2530,7 @@ export function RoadmapTab({ data }: RoadmapTabProps) {
                 return { display: '>500%', tooltip: `ROI calculado: ${roi}%`, showCap: true };
               }
               if (roi > 300) {
-                return { display: `${roi}%`, tooltip: 'ROI alto - validar con piloto', showCap: false };
+                return { display: `${roi}%`, tooltip: t('roadmap.payback.roiValidateWithPilot'), showCap: false };
               }
               return { display: `${roi}%`, tooltip: '', showCap: false };
             };
@@ -2530,15 +2578,13 @@ export function RoadmapTab({ data }: RoadmapTabProps) {
                 <div className="p-4 space-y-4">
                   {/* ENFOQUE DUAL: Párrafo explicativo */}
                   {recType === 'DUAL' && (
-                    <p className="text-sm text-gray-600 leading-relaxed">
-                      La Estrategia Dual consiste en ejecutar dos líneas de trabajo en paralelo:
-                      <strong className="text-gray-800"> Quick Win</strong> automatiza inmediatamente las {pilotQueues.length} colas
-                      ya preparadas (Tier AUTOMATE, {Math.round(totalVolume > 0 ? (tierVolumes.AUTOMATE / totalVolume) * 100 : 0)}% del volumen), generando retorno desde el primer mes;
-                      mientras que <strong className="text-gray-800">Foundation</strong> prepara el {Math.round(assistPct + augmentPct)}%
-                      restante del volumen (Tiers ASSIST y AUGMENT) estandarizando procesos y reduciendo variabilidad para habilitar
-                      automatización futura. Este enfoque maximiza el time-to-value: Quick Win financia la transformación y genera
-                      confianza organizacional, mientras Foundation amplía progresivamente el alcance de la automatización.
-                    </p>
+                    <p className="text-sm text-gray-600 leading-relaxed" dangerouslySetInnerHTML={{
+                      __html: t('roadmap.dualStrategy.explanation', {
+                        count: pilotQueues.length,
+                        pct: Math.round(totalVolume > 0 ? (tierVolumes.AUTOMATE / totalVolume) * 100 : 0),
+                        remaining: Math.round(assistPct + augmentPct)
+                      })
+                    }} />
                   )}
 
                   {/* FOUNDATION PRIMERO */}

frontend/i18n.ts

@@ -11,8 +11,8 @@ i18n
       es: { translation: es },
       en: { translation: en },
     },
-    lng: localStorage.getItem('language') || 'es', // Español por defecto
-    fallbackLng: 'es', // Si falla una traducción, usa español
+    lng: localStorage.getItem('language') || 'en', // English by default
+    fallbackLng: 'en', // If translation fails, use English
     interpolation: {
       escapeValue: false, // React ya escapa por defecto
     },

frontend/locales/en.json

@@ -532,7 +532,301 @@
     "hideDetail": "Hide detail",
     "viewDetail": "View detail",
     "collapseAll": "Collapse all",
-    "expandAll": "Expand all"
+    "expandAll": "Expand all",
+    "tierLabels": {
+      "automate": "Automate",
+      "assist": "Assist",
+      "augment": "Optimize",
+      "human": "Human"
+    },
+    "payback": {
+      "seeWave34": "See Wave 3-4",
+      "notRecoverable": "Not recoverable",
+      "immediate": "Immediate",
+      "recoversWithAutomation": "This investment is recovered with automation waves (W3-W4). Payback is calculated on the complete roadmap, not on enabling waves in isolation.",
+      "savingsDoNotCoverRecurring": "Annual savings do not cover recurring costs.",
+      "savingsDoNotCoverRecurringWithMargin": "Annual savings do not cover recurring costs. Net margin: {{margin}}/year",
+      "implementationRecoveryMargin": "Implementation: {{impl}} months → Recovery: {{rec}} months. Margin: {{margin}}/year.",
+      "moderateRecoveryPeriod": "Moderate recovery period.",
+      "longRecoveryPeriod": "Long recovery period. Consider less ambitious scenario.",
+      "roiValidateWithPilot": "High ROI - validate with pilot"
+    },
+    "waves": {
+      "wave1Name": "Wave 1",
+      "wave1Title": "FOUNDATION",
+      "wave1Quarter": "Q1-Q2 2026",
+      "wave1Condition": "",
+      "wave1Provider": "Beyond Consulting or specialized third party",
+      "wave1RiskDescription": "Consulting with tangible deliverables. Does not require technology.",
+      "wave2Name": "Wave 2",
+      "wave2Title": "AUGMENT",
+      "wave2Quarter": "Q3 2026",
+      "wave2Condition": "Requires CV ≤75% post-Wave 1 in target queues",
+      "wave2Provider": "BEYOND (KB + AI Scripts)",
+      "wave2RiskDescription": "Support tools, low integration risk.",
+      "wave3Name": "Wave 3",
+      "wave3Title": "ASSIST",
+      "wave3Quarter": "Q4 2026",
+      "wave3Condition": "Requires Score ≥5.5 AND CV ≤90% AND Transfer ≤30%",
+      "wave3Provider": "BEYOND (Copilot + AI Routing)",
+      "wave3RiskDescription": "Integration with contact center platform. 4-week pilot mitigates.",
+      "wave4Name": "Wave 4",
+      "wave4Title": "AUTOMATE",
+      "wave4Quarter": "Q1-Q2 2027",
+      "wave4Condition": "Requires Score ≥7.5 AND CV ≤75% AND Transfer ≤20% AND FCR ≥50%",
+      "wave4Provider": "BEYOND (Voicebot + IVR + Chatbot)",
+      "wave4RiskDescription": "Very conditional. Requires demonstrated success in Waves 1-3."
+    },
+    "initiatives": {
+      "wave1Init1": "Variability and red flags analysis",
+      "wave1Init1Kpi": "Map causes of CV >75% and Transfer >20%",
+      "wave1Init2": "Process redesign and documentation",
+      "wave1Init2Kpi": "Standardized scripts for 80% of cases",
+      "wave1Init3": "Agent training and certification",
+      "wave1Init3Kpi": "90% agent certification, >85% adherence",
+      "wave2Init1": "Contextual Knowledge Base",
+      "wave2Init1Kpi": "Hold time -25%, KB usage +40%",
+      "wave2Init2": "AI-powered dynamic scripts",
+      "wave2Init2Kpi": "Script adherence +30%",
+      "wave3Init1": "Agent Assist / AI Copilot",
+      "wave3Init1Kpi": "AHT -30%, suggestions accepted >60%",
+      "wave3Init2": "Partial automation (FAQs, routing)",
+      "wave3Init2Kpi": "Deflection rate 15%",
+      "wave4Init1": "Transactional Voicebot/Chatbot",
+      "wave4Init1Kpi": "Containment 70%+, CSAT ≥4/5",
+      "wave4Init2": "Intelligent IVR with NLU",
+      "wave4Init2Kpi": "Pre-qualification 80%+, warm transfer"
+    },
+    "successCriteriaTemplates": {
+      "wave1Criterion1": "CV AHT ≤75% in at least {{count}} high-volume queues",
+      "wave1Criterion2": "Transfer ≤20% global",
+      "wave1Criterion3": "Red flags eliminated in priority queues",
+      "wave1Criterion4": "At least {{count}} queues migrate from Tier 4 → Tier 3",
+      "wave2Criterion1": "Average score rises from 3.5-5.5 → ≥5.5",
+      "wave2Criterion2": "AHT -15% vs baseline",
+      "wave2Criterion3": "CV ≤90% in target queues",
+      "wave2Criterion4": "{{count}} queues migrate from Tier 3 → Tier 2",
+      "wave3Criterion1": "Average score rises from 5.5-7.5 → ≥7.5",
+      "wave3Criterion2": "AHT -30% vs Wave 2 baseline",
+      "wave3Criterion3": "CV ≤75% in target queues",
+      "wave3Criterion4": "Transfer ≤20%",
+      "wave3Criterion5": "{{count}} queues migrate from Tier 2 → Tier 1",
+      "wave4Criterion1": "Containment ≥70% in automated queues",
+      "wave4Criterion2": "CSAT maintains or improves (≥4/5)",
+      "wave4Criterion3": "Escalation to human <30%",
+      "wave4Criterion4": "Cumulative ROI >300%"
+    },
+    "scenarios": {
+      "conservativeName": "Conservative",
+      "conservativeDesc": "FOUNDATION + AUGMENT (Wave 1-2)",
+      "moderateName": "Moderate",
+      "moderateDesc": "FOUNDATION + AUGMENT + ASSIST (Wave 1-3)",
+      "aggressiveName": "Aggressive",
+      "aggressiveDesc": "Complete roadmap (Wave 1-4)",
+      "recommended": "Recommended",
+      "enablerRecommendation": "Recommended as ENABLER",
+      "partialEnabler": "Partial enabler",
+      "aspirational": "Aspirational",
+      "notProfitable": "Not profitable with current volume",
+      "scenariosTitle": "Investment Scenarios",
+      "scenariosSubtitle": "Comparison of options according to commitment level",
+      "scenariosTooltip": "ROI based on industry benchmarks. Adjusted ROI considers implementation risk factors.",
+      "scenario": "Scenario",
+      "investment": "Investment",
+      "recurring": "Recurring",
+      "savings": "Savings",
+      "adjusted": "adjusted",
+      "margin": "Margin",
+      "payback": "Payback",
+      "roi3y": "3y ROI",
+      "risk": "Risk",
+      "enabler": "Enabler",
+      "prerequisite": "Prerequisite",
+      "roiCalculatedOn": "ROI calculated on",
+      "enablerLongDesc": "Enabling waves whose value lies in unlocking subsequent waves. Their payback is evaluated on the complete roadmap.",
+      "paybackNote": "Payback: Implementation time + recovery time. Wave 1: 6m, W2: 3m, W3: 3m, W4: 6m. Savings begin at 50% of last wave.",
+      "roiNote": "ROI: (3y Savings - 3y Total Cost) / 3y Total Cost × 100. Adjusted applies risk: W1-2: 75-90%, W3: 60%, W4: 50%.",
+      "enablerNote": "Enabler: Waves that unlock ROI of subsequent waves. Their payback is evaluated with the complete roadmap.",
+      "enablerValue": "Real value of this investment:",
+      "enablerUnlocks": "Unlocks {{amount}}/year in {{waves}}. Without this foundation, subsequent waves are not viable.",
+      "unlocks": "Unlocks {{waves}}",
+      "enablesAmount": "enables {{amount}}",
+      "startConservative": "Start with conservative scenario to validate model before scaling."
+    },
+    "decisionGates": {
+      "gate1Question": "CV ≤75% in 3+ queues?",
+      "gate1Criteria": "Red flags eliminated, Tier 4→3",
+      "gate1GoAction": "Start AUGMENT",
+      "gate1NoGoAction": "Extend FOUNDATION",
+      "gate2Question": "Score ≥5.5 in target?",
+      "gate2Criteria": "CV ≤90%, Transfer ≤30%",
+      "gate2GoAction": "Start ASSIST",
+      "gate2NoGoAction": "Consolidate AUGMENT",
+      "gate3Question": "Score ≥7.5 in 2+ queues?",
+      "gate3Criteria": "CV ≤75%, FCR ≥50%",
+      "gate3GoAction": "Launch AUTOMATE",
+      "gate3NoGoAction": "Expand ASSIST",
+      "goNoGo": "Go/No-Go",
+      "criteria": "Criteria:",
+      "go": "Go:",
+      "no": "No:"
+    },
+    "timeline": {
+      "title": "Transformation Roadmap 2026-2027",
+      "subtitle": "Each wave depends on the success of the previous one. Decision points allow adjustment based on actual results.",
+      "setup": "Setup:",
+      "savings": "Savings:",
+      "conditional": "Conditional",
+      "low": "Low",
+      "medium": "Medium",
+      "high": "High",
+      "legendConfirmed": "Confirmed wave",
+      "legendConditional": "Conditional wave",
+      "legendDecisionPoint": "Go/No-Go decision point",
+      "legendRisk": "= Risk"
+    },
+    "comparison": {
+      "title": "Investment Scenarios",
+      "subtitle": "Comparison of options by commitment level",
+      "tooltip": "ROI based on industry benchmarks. Adjusted ROI considers implementation risk factors.",
+      "investment": "Investment",
+      "recurring": "Recurring",
+      "savings": "Savings",
+      "adjusted": "adjusted",
+      "margin": "Margin",
+      "payback": "Payback",
+      "roi3y": "3y ROI",
+      "risk": "Risk",
+      "scenario": "Scenario",
+      "recommendation": "Recommendation",
+      "recommendationEnabler": "Recommendation (Enabler)",
+      "enabler": "Enabler",
+      "recommended": "Recommended",
+      "savingsPerYear": "Savings/year",
+      "marginPerYear": "Margin/year",
+      "savingsLabel": "Savings:",
+      "conditional": "Conditional",
+      "riskLow": "Low",
+      "riskMedium": "Medium",
+      "riskHigh": "High",
+      "enablerWaveTooltip": "Enabler wave - its value is in unlocking subsequent waves",
+      "negativeMarginTooltip": "Negative annual margin",
+      "projectedRoiTooltip": "Projected ROI. Validate with pilot.",
+      "adjustedRoiTooltip": "ROI adjusted for implementation risk"
+    },
+    "entryCriteria": {
+      "wave1TierFrom": "HUMAN-ONLY (4), AUGMENT (3)",
+      "wave1ScoreRange": "<5.5",
+      "wave1Metric1": "CV >75% or Transfer >20%",
+      "wave1Metric2": "Active Red Flags",
+      "wave1Metric3": "Undocumented processes",
+      "wave2TierFrom": "AUGMENT (3)",
+      "wave2ScoreRange": "3.5-5.5",
+      "wave2Metric1": "CV ≤75%",
+      "wave2Metric2": "Transfer ≤20%",
+      "wave2Metric3": "No Red Flags",
+      "wave3TierFrom": "ASSIST (2)",
+      "wave3ScoreRange": "5.5-7.5",
+      "wave3Metric1": "CV ≤90%",
+      "wave3Metric2": "Transfer ≤30%",
+      "wave3Metric3": "Stable AHT",
+      "wave4TierFrom": "AUTOMATE (1)",
+      "wave4ScoreRange": "≥7.5",
+      "wave4Metric1": "CV ≤75%",
+      "wave4Metric2": "Transfer ≤20%",
+      "wave4Metric3": "FCR ≥50%",
+      "wave4Metric4": "No Red Flags"
+    },
+    "exitCriteria": {
+      "wave1TierTo": "AUGMENT (3) minimum",
+      "wave1ScoreTarget": "≥3.5",
+      "wave1Kpi1": "CV ≤75%",
+      "wave1Kpi2": "Transfer ≤20%",
+      "wave1Kpi3": "Red flags eliminated",
+      "wave2TierTo": "ASSIST (2)",
+      "wave2ScoreTarget": "≥5.5",
+      "wave2Kpi1": "CV ≤90%",
+      "wave2Kpi2": "Transfer ≤30%",
+      "wave2Kpi3": "AHT -15%",
+      "wave3TierTo": "AUTOMATE (1)",
+      "wave3ScoreTarget": "≥7.5",
+      "wave3Kpi1": "CV ≤75%",
+      "wave3Kpi2": "Transfer ≤20%",
+      "wave3Kpi3": "FCR ≥50%",
+      "wave3Kpi4": "AHT -30%",
+      "wave4TierTo": "AUTOMATED",
+      "wave4ScoreTarget": "Containment ≥70%",
+      "wave4Kpi1": "Bot resolution ≥70%",
+      "wave4Kpi2": "CSAT ≥4/5",
+      "wave4Kpi3": "Escalation <30%"
+    },
+    "recommendations": {
+      "conservativeEnabler": "✅ Recommended as ENABLER. Unlocks {{amount}}/year in Wave 3-4. Objective: move {{count}} queues from Tier 4→3.",
+      "conservativeNormal": "✅ Recommended. Validate model with low risk. Objective: move {{count}} queues from Tier 4→3.",
+      "moderateEnabler": "Partial enabler. Unlocks {{amount}}/year in Wave 4. Decide Go/No-Go in Q3 2026.",
+      "moderateNormal": "Decide Go/No-Go in Q3 2026 based on Wave 1-2 results. Requires Score ≥5.5 in target queues.",
+      "aggressivePositive": "⚠️ Aspirational. Only if Waves 1-3 successful and queues with Score ≥7.5 exist. Decision in Q1 2027.",
+      "aggressiveNegative": "❌ Not profitable with current volume. Requires significantly greater scale."
+    },
+    "table": {
+      "topQueuesByVolumeImpact": "Top Queues by Volume × Impact",
+      "queue": "Queue",
+      "volPerMonth": "Vol/month",
+      "score": "Score",
+      "tier": "Tier",
+      "redFlags": "Red Flags",
+      "potential": "Potential",
+      "redFlagsNote": "Red Flags: CV >120% (high variability) · Transfer >50% (fragmented process) · Vol <50 (small sample) · Valid <30% (noisy data)",
+      "skills": "Skills",
+      "entry": "ENTRY",
+      "exit": "EXIT",
+      "tierLabel": "Tier:",
+      "scoreLabel": "Score:",
+      "financialMetrics": "Financial Metrics",
+      "setupLabel": "Setup",
+      "recurringPerYear": "Recurring/year",
+      "savingsPerYear": "Savings/year",
+      "marginPerYear": "Margin/year",
+      "initiativesLabel": "Initiatives:",
+      "setup": "Setup:",
+      "rec": "Rec:",
+      "kpi": "KPI:",
+      "successCriteriaLabel": "✅ Success criteria:",
+      "condition": "⚠️ Condition:",
+      "provider": "Provider:"
+    },
+    "porQueNecesarioTemplates": {
+      "wave1": "{{count}} of {{total}} queues are in Tier 3-4 ({{pct}}% of volume). Red flags: CV >75%, Transfer >20%. Automating without standardizing = guaranteed failure.",
+      "wave2": "Implement support tools for Tier 3 queues (Score 3.5-5.5). Objective: raise score to ≥5.5 to enable Wave 3. Focus on {{count}} queues with {{volume}} int/month.",
+      "wave3": "AI Copilot for agents in Tier 2 queues. Real-time suggestions, autocomplete, next-best-action. Objective: raise score to ≥7.5 for Wave 4. Target: {{count}} queues with {{volume}} int/month.",
+      "wave4": "End-to-end automation for Tier 1 queues. Transactional Voicebot/Chatbot with 70% containment. Only viable with mature processes. Current target: {{count}} queues with {{volume}} int/month."
+    },
+    "fallbackSkills": {
+      "wave1": "Queues that reach Score 3.5-5.5 post Wave 1",
+      "wave2": "Queues that reach Score ≥5.5 post Wave 2",
+      "wave3": "Queues that reach Score ≥7.5 post Wave 3"
+    },
+    "wave2Description": {
+      "ready": "{{skill}} is the skill with the best Score ({{score}}/10, \"Copilot\" category). Volume {{volume}}/year = greatest economic impact.",
+      "notReady": "No skill currently reaches Score ≥6. The best candidate is {{skill}} with Score {{score}}/10. Requires prior optimization in Wave 1."
+    },
+    "specificRecommendations": {
+      "launchWave4": "Launch Wave 4 (AUTOMATE) pilot",
+      "launchWave4Rationale": "{{count}} queues already have Score ≥7.5 with volume of {{volume}} int/month.",
+      "launchWave4NextStep": "Start automation pilot in the 2-3 highest-volume queues with savings potential of {{amount}}/year.",
+      "initiateWave3": "Initiate Wave 3 (ASSIST) with Copilot",
+      "initiateWave3Rationale": "{{count}} queues have Score 5.5-7.5, representing {{pct}}% of volume.",
+      "initiateWave3NextStep": "Deploy AI Copilot in Tier 2 queues to raise score to ≥7.5 and enable Wave 4. Investment: {{amount}}.",
+      "prioritizeWave1": "Prioritize Wave 1 (FOUNDATION)",
+      "prioritizeWave1Rationale": "{{count}} queues ({{pct}}%) have Red Flags that prevent automation.",
+      "prioritizeWave1NextStep": "Standardize processes before investing in AI. Automation without solid foundations fails in 80%+ of cases.",
+      "executeWave12": "Execute Wave 1-2 sequentially",
+      "executeWave12Rationale": "Mixed operation: {{automate}} Tier 1 queues, {{assist}} Tier 2, {{augment}} Tier 3, {{human}} Tier 4.",
+      "executeWave12NextStep": "Start with FOUNDATION to eliminate red flags, followed by AUGMENT to raise scores. Initial investment: {{amount}}."
+    },
+    "dualStrategy": {
+      "explanation": "The Dual Strategy consists of executing two parallel work streams: <strong>Quick Win</strong> immediately automates the {{count}} ready queues (Tier AUTOMATE, {{pct}}% of volume), generating returns from the first month; while <strong>Foundation</strong> prepares the remaining {{remaining}}% of volume (Tiers ASSIST and AUGMENT) by standardizing processes and reducing variability to enable future automation. This approach maximizes time-to-value: Quick Win finances the transformation and generates organizational confidence, while Foundation progressively expands the scope of automation."
+    }
   },
   "opportunities": {
     "viewCriticalActions": "View Critical Actions",
@@ -570,12 +864,16 @@
     "humanOnlyAction": "Maintain human management, evaluate periodically",
     "redFlags": {
       "cvCritical": "Critical AHT CV",
+      "cvCriticalShort": "CV",
       "cvCriticalDesc": "Extreme variability - unpredictable processes",
       "transferExcessive": "Excessive Transfer",
+      "transferExcessiveShort": "Transfer",
       "transferExcessiveDesc": "High complexity - requires frequent escalation",
       "volumeInsufficient": "Insufficient Volume",
+      "volumeInsufficientShort": "Vol",
       "volumeInsufficientDesc": "Negative ROI - volume doesn't justify investment",
       "dataQualityLow": "Low Data Quality",
+      "dataQualityLowShort": "Valid",
       "dataQualityLowDesc": "Unreliable data - distorted metrics",
       "threshold": "(threshold: {{operator}}{{value}})"
     },
@@ -799,7 +1097,38 @@
       "standardizeProcesses": "Standardize processes and scripts",
       "simplifyFlow": "Simplify flow, train agents",
       "consolidate": "Consolidate with similar queues",
-      "improveDataCapture": "Improve data capture"
+      "improveDataCapture": "Improve data capture",
+      "skillsWithRedFlags": "Skills with Red Flags",
+      "queuesRequireIntervention": "Queues that require intervention before automating",
+      "viewRoadmapTab": "View Roadmap tab for detailed plan",
+      "viewRoadmapLink": "View Roadmap tab for detailed plan →"
+    },
+    "roadmapConnection": {
+      "quickWinsTitle": "IMMEDIATE QUICK WINS (without Wave 1)",
+      "automateQueues": "{{count}} AUTOMATE queues",
+      "with": "with",
+      "interactionsPerMonth": "interactions/month",
+      "savingsPotential": "Savings potential:",
+      "perYear": "/year",
+      "containment": "containment",
+      "perInt": "/int",
+      "skills": "Skills:",
+      "alignedWithWave4": "→ Aligned with Roadmap Wave 4. Can be implemented in parallel to Wave 1.",
+      "wave13Title": "WAVE 1-3: FOUNDATION → ASSIST ({{count}} queues)",
+      "inTierAssist": "in tier ASSIST",
+      "focusWave1": "Wave 1 Focus:",
+      "reduceTransferIn": "Reduce transfer in",
+      "potentialWithCopilot": "Potential with Copilot:",
+      "deflection": "deflection",
+      "requiresWave1": "→ Requires Wave 1 (Foundation) to enable Copilot in Wave 3",
+      "calculationNote": "Calculation: {{volume}} int × 12 months × {{rate}}% {{type}} × €{{cpi}}/int",
+      "quickWinsHaveVolume": "have >60% volume in T1+T2",
+      "hasPercentInHuman": "has {{pct}}% in HUMAN",
+      "prioritizeInWave1": "→ prioritize in Wave 1",
+      "balancedDistribution": "Balanced distribution across tiers. Review individual queues for prioritization.",
+      "haveAtLeastOne": "have at least one tier AUTOMATE queue",
+      "showLess": "Show less",
+      "viewAll": "View all ({{count}})"
     },
     "factorsExtended": {
       "volumeMethodology": "Score = normalized log10(Volume). >5000 → 10, <100 → 2",
@@ -814,6 +1143,123 @@
       "roiBad": "Marginal ROI, evaluate other benefits",
       "resolution": "Resolution",
       "dataQuality": "Data Quality"
+    },
+    "factorConfigs": {
+      "predictability": {
+        "title": "Predictability",
+        "description": "Consistency in handling times",
+        "methodology": "Score = 10 - (CV_AHT / 10). CV AHT < 30% → Score > 7",
+        "benchmark": "Optimal CV AHT < 25%",
+        "highImplication": "Consistent times, ideal for AI",
+        "lowImplication": "Requires standardization"
+      },
+      "inverseComplexity": {
+        "title": "Simplicity",
+        "description": "Low level of human judgment required",
+        "methodology": "Score = 10 - (Transfer_Rate × 0.4). Transfer <10% → Score > 6",
+        "benchmark": "Optimal transfers <10%",
+        "highImplication": "Simple processes, automatable",
+        "lowImplication": "High complexity, requires copilot"
+      },
+      "repeatability": {
+        "title": "Volume",
+        "description": "Scale to justify investment",
+        "methodology": "Score = normalized log10(Volume). >5000 → 10, <100 → 2",
+        "benchmark": "Positive ROI requires >500/month",
+        "highImplication": "High volume justifies investment",
+        "lowImplication": "Consider shared solutions"
+      },
+      "roiPotential": {
+        "title": "ROI Potential",
+        "description": "Expected economic return",
+        "methodology": "Score based on total annual cost. >€500K → 10",
+        "benchmark": "ROI >150% at 12 months",
+        "highImplication": "Solid business case",
+        "lowImplication": "Marginal ROI, evaluate other benefits"
+      }
+    },
+    "scoreBreakdown": {
+      "predictability": "Predictability (30%)",
+      "resolution": "Resolution (25%)",
+      "volume": "Volume (25%)",
+      "dataQuality": "Data Quality (10%)",
+      "simplicity": "Simplicity (10%)"
+    },
+    "bubbleChart": {
+      "quickWinsCount": "{{count}} queues · {{amount}}",
+      "highPotentialCount": "{{count}} queues · {{amount}}",
+      "developCount": "{{count}} queues · {{amount}}",
+      "easyImplCount": "{{count}} · {{amount}}",
+      "backlogCount": "{{count}} · {{amount}}",
+      "total": "total",
+      "noQueuesFilters": "No queues match the selected filters",
+      "quickWinsLabel": "QUICK WINS",
+      "highPotentialLabel": "HIGH POTENTIAL",
+      "developLabel": "DEVELOP",
+      "easyImplLabel": "EASY IMPL.",
+      "backlogLabel": "BACKLOG",
+      "activeFiltersLabel": "Active filters:",
+      "ofQueues": "of {{total}} queues",
+      "perMonth": "/month",
+      "cvAht": "CV AHT:",
+      "viewDetail": "Click for details"
+    },
+    "modal": {
+      "skillLabel": "Skill:",
+      "transferRate": "Transfer Rate",
+      "annualSavings": "Annual Savings"
+    },
+    "volumeLabels": {
+      "queues": "queues",
+      "int": "int"
+    },
+    "subFactors": {
+      "repeatability": "Repeatability",
+      "repeatabilityDisplayName": "Repeatability",
+      "repeatabilityDescription": "Monthly volume: {{volume}} interactions",
+      "predictability": "Predictability",
+      "predictabilityDisplayName": "Predictability",
+      "predictabilityDescription": "AHT CV: {{cv}}%, Escalation: {{esc}}%",
+      "structuring": "Structuring",
+      "structuringDisplayName": "Structuring",
+      "structuringDescription": "{{pct}}% structured fields",
+      "inverseComplexity": "Inverse Complexity",
+      "inverseComplexityDisplayName": "Inverse Complexity",
+      "inverseComplexityDescription": "{{pct}}% exceptions",
+      "stability": "Stability",
+      "stabilityDisplayName": "Stability",
+      "stabilityDescription": "{{pct}}% off-hours",
+      "roiSavings": "ROI",
+      "roiSavingsDisplayName": "ROI",
+      "roiSavingsDescription": "€{{amount}}K annual potential savings",
+      "interpretations": {
+        "excellentForAutomation": "Excellent candidate for complete automation (Automate)",
+        "goodForAssistance": "Good candidate for agentic assistance (Assist)",
+        "candidateForAugmentation": "Candidate for human augmentation (Augment)",
+        "notRecommended": "Not recommended for automation at this time",
+        "bronzeAnalysis": "Bronze analysis does not include Agentic Readiness Score"
+      }
+    },
+    "emptyStates": {
+      "noQueuesClassifiedAs": "No queues classified as {{tier}}",
+      "noQueuesMatchFilters": "No queues match the selected filters"
+    },
+    "sections": {
+      "classificationBySkill": "CLASSIFICATION BY SKILL",
+      "classificationByTier": "CLASSIFICATION BY AUTOMATION TIER",
+      "queuesAutomate": "Queues AUTOMATE",
+      "readyForFullAutomation": "Ready for full automation with virtual agent (Score ≥7.5)",
+      "queuesAssist": "Queues ASSIST",
+      "candidatesForCopilot": "Candidates for Copilot - AI assists human agent (Score 5.5-7.5)",
+      "queuesAugment": "Queues AUGMENT",
+      "requireOptimization": "Require prior optimization: standardize processes, reduce variability (Score 3.5-5.5)",
+      "queuesHumanOnly": "Queues HUMAN-ONLY",
+      "notSuitableForAutomation": "Not suitable for automation: insufficient volume, low data quality, or extreme complexity",
+      "queuesIn": "queues in {{count}} skills",
+      "costPerYear": "/year",
+      "volumeColon": "Volume:",
+      "costColon": "Cost:",
+      "potentialSavingsColon": "Potential savings:"
     }
   },
   "economicModel": {
@@ -1312,6 +1758,142 @@
       "requiredData": "Requires data",
       "score": "Score",
       "gap": "Gap"
+    },
+    "summaryTable": {
+      "requirement": "Requirement",
+      "description": "Description",
+      "status": "Status",
+      "score": "Score",
+      "gap": "Gap",
+      "legend": {
+        "complies": "Complies: Requirement satisfied",
+        "partial": "Partial: Requires improvements",
+        "notComply": "Does Not Comply: Urgent action",
+        "noData": "No Data: Fields not available in CSV"
+      },
+      "investment": {
+        "nonComplianceCost": "Cost of non-compliance",
+        "upTo100k": "Up to 100K",
+        "potentialFines": "Potential fines/infraction",
+        "recommendedInvestment": "Recommended investment",
+        "basedOnOperation": "Based on your operation",
+        "complianceRoi": "Compliance ROI",
+        "avoidSanctions": "Avoid sanctions + improve CX"
+      }
+    },
+    "dataMaturity": {
+      "title": "Summary: Data Maturity for Compliance",
+      "currentLevel": "Your current instrumentation level:",
+      "availableData": "AVAILABLE DATA (3/10)",
+      "estimableData": "ESTIMABLE DATA (2/10)",
+      "unavailableData": "NOT AVAILABLE (5/10)",
+      "items": {
+        "coverage247": "24/7 temporal coverage",
+        "geoDistribution": "Geographic distribution",
+        "resolutionQuality": "Resolution quality proxy",
+        "asa3min": "ASA <3min via abandonment proxy",
+        "officialLanguages": "Co-official languages via country",
+        "caseResolutionTime": "Case resolution time",
+        "undueBilling": "Undue billing <5 days",
+        "supervisorTransfer": "Transfer to supervisor",
+        "incidentInfo": "Incident info <2h",
+        "enacAudit": "ENAC audit",
+        "externalContractRequired": "requires external contracting"
+      },
+      "investment": {
+        "title": "SUGGESTED INVESTMENT FOR COMPLETE COMPLIANCE",
+        "phase1": {
+          "title": "Phase 1 - Instrumentation (Q1 2026)",
+          "realAsaTracking": "• Real ASA tracking",
+          "ticketingSystem": "• Ticketing/case system",
+          "languageEnrichment": "• Language enrichment",
+          "subtotal": "Subtotal:"
+        },
+        "phase2": {
+          "title": "Phase 2 - Operations (Q2-Q3 2026)",
+          "coverage247": "• 24/7 coverage (chatbot + on-call)",
+          "aiCopilot": "• AI Copilot (reduce AHT)",
+          "enacAuditor": "• ENAC auditor",
+          "subtotalYear1": "Year 1 subtotal:"
+        },
+        "totals": {
+          "totalInvestment": "Total Investment",
+          "percentAnnualCost": "~5% annual cost",
+          "riskAvoided": "Risk Avoided",
+          "potentialSanctions": "potential sanctions",
+          "complianceRoi": "Compliance ROI"
+        }
+      }
+    }
+  },
+  "opportunityPrioritizer": {
+    "title": "Prioritized Opportunities",
+    "subtitle": "{{count}} initiatives ordered by savings potential and feasibility",
+    "whereAreOpportunities": "Where are opportunities?",
+    "totalSavingsIdentified": "Total Savings Identified",
+    "annual": "annual",
+    "quickWins": "Quick Wins (AUTOMATE)",
+    "assistance": "Assistance (ASSIST)",
+    "optimization": "Optimization (AUGMENT)",
+    "inMonths": "in {{months}} months",
+    "startHere": "START HERE",
+    "priority1": "Priority #1",
+    "annualSavings": "Annual Savings",
+    "volume": "Volume",
+    "timeline": "Timeline",
+    "months": "months",
+    "nextSteps": "Next Steps",
+    "allOpportunities": "All Prioritized Opportunities",
+    "savings": "Savings",
+    "valueEffort": "Value / Effort",
+    "value": "Value",
+    "effort": "Effort",
+    "viewMore": "View {{count}} more opportunities",
+    "methodology": "Prioritization methodology:",
+    "methodologyDescription": "Opportunities are ordered by TCO savings potential (volume × containment rate × CPI differential). AUTOMATE/ASSIST/AUGMENT tier classification is based on Agentic Readiness Score considering predictability (CV AHT), resolvability (FCR + Transfer), volume, data quality and process simplicity.",
+    "tierLabels": {
+      "automate": "Automate",
+      "assist": "Assist",
+      "augment": "Augment",
+      "human": "Human"
+    },
+    "timelines": {
+      "automate": "3-6 months",
+      "assist": "6-9 months",
+      "augment": "9-12 months"
+    },
+    "tierDescriptions": {
+      "automate": "Full automation with AI agents",
+      "assist": "AI Copilot for human agents",
+      "augment": "Process standardization and improvement",
+      "humanOnly": "Requires human intervention"
+    },
+    "agenticScore": "Agentic Score",
+    "whyPriority1": "Why is this priority #1?",
+    "viewCompleteDetail": "View Complete Detail",
+    "showLess": "Show less",
+    "whyThisPosition": "Why this position?",
+    "keyMetrics": "Key Metrics",
+    "noOpportunitiesTitle": "No opportunities identified",
+    "noOpportunitiesDescription": "Current data doesn't show viable automation opportunities.",
+    "reasons": {
+      "highSavingsPotential": "High savings potential (€{{amount}}K/year)",
+      "highVolume": "High volume ({{volume}} interactions)",
+      "highlyPredictable": "Highly predictable and repetitive process",
+      "lowVariability": "Low variability in handling times",
+      "lowTransferRate": "Low transfer rate",
+      "highFeasibility": "High technical feasibility"
+    },
+    "steps": {
+      "automate1": "Define main conversational flows",
+      "automate2": "Identify necessary integrations (CRM, APIs)",
+      "automate3": "Create pilot with 10% of volume",
+      "assist1": "Map agent friction points",
+      "assist2": "Design contextual suggestions",
+      "assist3": "Pilot with selected team",
+      "augment1": "Analyze root cause of variability",
+      "augment2": "Standardize processes and scripts",
+      "augment3": "Train team on best practices"
     }
   }
 }

frontend/locales/es.json

@@ -532,7 +532,301 @@
     "hideDetail": "Ocultar detalle",
     "viewDetail": "Ver detalle",
     "collapseAll": "Colapsar todas",
-    "expandAll": "Expandir todas"
+    "expandAll": "Expandir todas",
+    "tierLabels": {
+      "automate": "Automatizar",
+      "assist": "Asistir",
+      "augment": "Optimizar",
+      "human": "Humano"
+    },
+    "payback": {
+      "seeWave34": "Ver Wave 3-4",
+      "notRecoverable": "No recuperable",
+      "immediate": "Inmediato",
+      "recoversWithAutomation": "Esta inversión se recupera con las waves de automatización (W3-W4). El payback se calcula sobre el roadmap completo, no sobre waves habilitadoras aisladas.",
+      "savingsDoNotCoverRecurring": "El ahorro anual no supera los costes recurrentes.",
+      "savingsDoNotCoverRecurringWithMargin": "El ahorro anual no supera los costes recurrentes. Margen neto: {{margin}}/año",
+      "implementationRecoveryMargin": "Implementación: {{impl}} meses → Recuperación: {{rec}} meses. Margen: {{margin}}/año.",
+      "moderateRecoveryPeriod": "Periodo de recuperación moderado.",
+      "longRecoveryPeriod": "Periodo de recuperación largo. Considerar escenario menos ambicioso.",
+      "roiValidateWithPilot": "ROI alto - validar con piloto"
+    },
+    "waves": {
+      "wave1Name": "Wave 1",
+      "wave1Title": "FOUNDATION",
+      "wave1Quarter": "Q1-Q2 2026",
+      "wave1Condition": "",
+      "wave1Provider": "Beyond Consulting o tercero especializado",
+      "wave1RiskDescription": "Consultoría con entregables tangibles. No requiere tecnología.",
+      "wave2Name": "Wave 2",
+      "wave2Title": "AUGMENT",
+      "wave2Quarter": "Q3 2026",
+      "wave2Condition": "Requiere CV ≤75% post-Wave 1 en colas target",
+      "wave2Provider": "BEYOND (KB + Scripts IA)",
+      "wave2RiskDescription": "Herramientas de soporte, bajo riesgo de integración.",
+      "wave3Name": "Wave 3",
+      "wave3Title": "ASSIST",
+      "wave3Quarter": "Q4 2026",
+      "wave3Condition": "Requiere Score ≥5.5 Y CV ≤90% Y Transfer ≤30%",
+      "wave3Provider": "BEYOND (Copilot + Routing IA)",
+      "wave3RiskDescription": "Integración con plataforma contact center. Piloto 4 semanas mitiga.",
+      "wave4Name": "Wave 4",
+      "wave4Title": "AUTOMATE",
+      "wave4Quarter": "Q1-Q2 2027",
+      "wave4Condition": "Requiere Score ≥7.5 Y CV ≤75% Y Transfer ≤20% Y FCR ≥50%",
+      "wave4Provider": "BEYOND (Voicebot + IVR + Chatbot)",
+      "wave4RiskDescription": "Muy condicional. Requiere éxito demostrado en Waves 1-3."
+    },
+    "initiatives": {
+      "wave1Init1": "Análisis de variabilidad y red flags",
+      "wave1Init1Kpi": "Mapear causas de CV >75% y Transfer >20%",
+      "wave1Init2": "Rediseño y documentación de procesos",
+      "wave1Init2Kpi": "Scripts estandarizados para 80% casuística",
+      "wave1Init3": "Training y certificación de agentes",
+      "wave1Init3Kpi": "Certificación 90% agentes, adherencia >85%",
+      "wave2Init1": "Knowledge Base contextual",
+      "wave2Init1Kpi": "Hold time -25%, uso KB +40%",
+      "wave2Init2": "Scripts dinámicos con IA",
+      "wave2Init2Kpi": "Adherencia scripts +30%",
+      "wave3Init1": "Agent Assist / Copilot IA",
+      "wave3Init1Kpi": "AHT -30%, sugerencias aceptadas >60%",
+      "wave3Init2": "Automatización parcial (FAQs, routing)",
+      "wave3Init2Kpi": "Deflection rate 15%",
+      "wave4Init1": "Voicebot/Chatbot transaccional",
+      "wave4Init1Kpi": "Contención 70%+, CSAT ≥4/5",
+      "wave4Init2": "IVR inteligente con NLU",
+      "wave4Init2Kpi": "Pre-calificación 80%+, transferencia warm"
+    },
+    "successCriteriaTemplates": {
+      "wave1Criterion1": "CV AHT ≤75% en al menos {{count}} colas de alto volumen",
+      "wave1Criterion2": "Transfer ≤20% global",
+      "wave1Criterion3": "Red flags eliminados en colas prioritarias",
+      "wave1Criterion4": "Al menos {{count}} colas migran de Tier 4 → Tier 3",
+      "wave2Criterion1": "Score promedio sube de 3.5-5.5 → ≥5.5",
+      "wave2Criterion2": "AHT -15% vs baseline",
+      "wave2Criterion3": "CV ≤90% en colas target",
+      "wave2Criterion4": "{{count}} colas migran de Tier 3 → Tier 2",
+      "wave3Criterion1": "Score promedio sube de 5.5-7.5 → ≥7.5",
+      "wave3Criterion2": "AHT -30% vs baseline Wave 2",
+      "wave3Criterion3": "CV ≤75% en colas target",
+      "wave3Criterion4": "Transfer ≤20%",
+      "wave3Criterion5": "{{count}} colas migran de Tier 2 → Tier 1",
+      "wave4Criterion1": "Contención ≥70% en colas automatizadas",
+      "wave4Criterion2": "CSAT se mantiene o mejora (≥4/5)",
+      "wave4Criterion3": "Escalado a humano <30%",
+      "wave4Criterion4": "ROI acumulado >300%"
+    },
+    "scenarios": {
+      "conservativeName": "Conservador",
+      "conservativeDesc": "FOUNDATION + AUGMENT (Wave 1-2)",
+      "moderateName": "Moderado",
+      "moderateDesc": "FOUNDATION + AUGMENT + ASSIST (Wave 1-3)",
+      "aggressiveName": "Agresivo",
+      "aggressiveDesc": "Roadmap completo (Wave 1-4)",
+      "recommended": "Recomendado",
+      "enablerRecommendation": "Recomendado como HABILITADOR",
+      "partialEnabler": "Habilitador parcial",
+      "aspirational": "Aspiracional",
+      "notProfitable": "No rentable con el volumen actual",
+      "scenariosTitle": "Escenarios de Inversión",
+      "scenariosSubtitle": "Comparación de opciones según nivel de compromiso",
+      "scenariosTooltip": "ROI basado en benchmarks de industria. El ROI ajustado considera factores de riesgo de implementación.",
+      "scenario": "Escenario",
+      "investment": "Inversión",
+      "recurring": "Recurrente",
+      "savings": "Ahorro",
+      "adjusted": "ajust.",
+      "margin": "Margen",
+      "payback": "Payback",
+      "roi3y": "ROI 3a",
+      "risk": "Riesgo",
+      "enabler": "Habilitador",
+      "prerequisite": "Prerrequisito",
+      "roiCalculatedOn": "El ROI se calcula sobre el roadmap completo",
+      "enablerLongDesc": "Waves habilitadoras - su valor está en desbloquear waves posteriores. Su payback se evalúa con el roadmap completo.",
+      "paybackNote": "Payback: Tiempo implementación + tiempo recuperación. Wave 1: 6m, W2: 3m, W3: 3m, W4: 6m. Ahorro comienza al 50% de última wave.",
+      "roiNote": "ROI: (Ahorro 3a - Coste Total 3a) / Coste Total 3a × 100. Ajustado aplica riesgo: W1-2: 75-90%, W3: 60%, W4: 50%.",
+      "enablerNote": "💡 Habilitador: Waves que desbloquean ROI de waves posteriores. Su payback se evalúa con el roadmap completo.",
+      "enablerValue": "💡 Valor real de esta inversión:",
+      "enablerUnlocks": "Desbloquea {{amount}}/año en {{waves}}. Sin esta base, las waves posteriores no son viables.",
+      "unlocks": "Desbloquea {{waves}}",
+      "enablesAmount": "habilita {{amount}}",
+      "startConservative": "Iniciar con escenario conservador para validar modelo antes de escalar."
+    },
+    "decisionGates": {
+      "gate1Question": "¿CV ≤75% en 3+ colas?",
+      "gate1Criteria": "Red flags eliminados, Tier 4→3",
+      "gate1GoAction": "Iniciar AUGMENT",
+      "gate1NoGoAction": "Extender FOUNDATION",
+      "gate2Question": "¿Score ≥5.5 en target?",
+      "gate2Criteria": "CV ≤90%, Transfer ≤30%",
+      "gate2GoAction": "Iniciar ASSIST",
+      "gate2NoGoAction": "Consolidar AUGMENT",
+      "gate3Question": "¿Score ≥7.5 en 2+ colas?",
+      "gate3Criteria": "CV ≤75%, FCR ≥50%",
+      "gate3GoAction": "Lanzar AUTOMATE",
+      "gate3NoGoAction": "Expandir ASSIST",
+      "goNoGo": "Go/No-Go",
+      "criteria": "Criterio:",
+      "go": "✓ Go:",
+      "no": "✗ No:"
+    },
+    "timeline": {
+      "title": "Roadmap de Transformación 2026-2027",
+      "subtitle": "Cada wave depende del éxito de la anterior. Los puntos de decisión permiten ajustar según resultados reales.",
+      "setup": "Setup:",
+      "savings": "Ahorro:",
+      "conditional": "Condicional",
+      "low": "● Bajo",
+      "medium": "● Medio",
+      "high": "● Alto",
+      "legendConfirmed": "Wave confirmada",
+      "legendConditional": "Wave condicional",
+      "legendDecisionPoint": "Punto de decisión Go/No-Go",
+      "legendRisk": "= Riesgo"
+    },
+    "comparison": {
+      "title": "Escenarios de Inversión",
+      "subtitle": "Comparación de opciones según nivel de compromiso",
+      "tooltip": "ROI basado en benchmarks de industria. El ROI ajustado considera factores de riesgo de implementación.",
+      "investment": "Inversión",
+      "recurring": "Recurrente",
+      "savings": "Ahorro",
+      "adjusted": "ajustado",
+      "margin": "Margen",
+      "payback": "Payback",
+      "roi3y": "ROI 3a",
+      "risk": "Riesgo",
+      "scenario": "Escenario",
+      "recommendation": "Recomendación",
+      "recommendationEnabler": "Recomendación (Habilitador)",
+      "enabler": "Habilitador",
+      "recommended": "Recomendado",
+      "savingsPerYear": "Ahorro/año",
+      "marginPerYear": "Margen/año",
+      "savingsLabel": "Ahorro:",
+      "conditional": "Condicional",
+      "riskLow": "Bajo",
+      "riskMedium": "Medio",
+      "riskHigh": "Alto",
+      "enablerWaveTooltip": "Wave habilitadora - su valor está en desbloquear waves posteriores",
+      "negativeMarginTooltip": "Margen anual negativo",
+      "projectedRoiTooltip": "ROI proyectado. Validar con piloto.",
+      "adjustedRoiTooltip": "ROI ajustado por riesgo de implementación"
+    },
+    "entryCriteria": {
+      "wave1TierFrom": "HUMAN-ONLY (4), AUGMENT (3)",
+      "wave1ScoreRange": "<5.5",
+      "wave1Metric1": "CV >75% o Transfer >20%",
+      "wave1Metric2": "Red Flags activos",
+      "wave1Metric3": "Procesos no documentados",
+      "wave2TierFrom": "AUGMENT (3)",
+      "wave2ScoreRange": "3.5-5.5",
+      "wave2Metric1": "CV ≤75%",
+      "wave2Metric2": "Transfer ≤20%",
+      "wave2Metric3": "Sin Red Flags",
+      "wave3TierFrom": "ASSIST (2)",
+      "wave3ScoreRange": "5.5-7.5",
+      "wave3Metric1": "CV ≤90%",
+      "wave3Metric2": "Transfer ≤30%",
+      "wave3Metric3": "AHT estable",
+      "wave4TierFrom": "AUTOMATE (1)",
+      "wave4ScoreRange": "≥7.5",
+      "wave4Metric1": "CV ≤75%",
+      "wave4Metric2": "Transfer ≤20%",
+      "wave4Metric3": "FCR ≥50%",
+      "wave4Metric4": "Sin Red Flags"
+    },
+    "exitCriteria": {
+      "wave1TierTo": "AUGMENT (3) mínimo",
+      "wave1ScoreTarget": "≥3.5",
+      "wave1Kpi1": "CV ≤75%",
+      "wave1Kpi2": "Transfer ≤20%",
+      "wave1Kpi3": "Red flags eliminados",
+      "wave2TierTo": "ASSIST (2)",
+      "wave2ScoreTarget": "≥5.5",
+      "wave2Kpi1": "CV ≤90%",
+      "wave2Kpi2": "Transfer ≤30%",
+      "wave2Kpi3": "AHT -15%",
+      "wave3TierTo": "AUTOMATE (1)",
+      "wave3ScoreTarget": "≥7.5",
+      "wave3Kpi1": "CV ≤75%",
+      "wave3Kpi2": "Transfer ≤20%",
+      "wave3Kpi3": "FCR ≥50%",
+      "wave3Kpi4": "AHT -30%",
+      "wave4TierTo": "AUTOMATIZADO",
+      "wave4ScoreTarget": "Contención ≥70%",
+      "wave4Kpi1": "Bot resolution ≥70%",
+      "wave4Kpi2": "CSAT ≥4/5",
+      "wave4Kpi3": "Escalado <30%"
+    },
+    "recommendations": {
+      "conservativeEnabler": "✅ Recomendado como HABILITADOR. Desbloquea {{amount}}/año en Wave 3-4. Objetivo: mover {{count}} colas de Tier 4→3.",
+      "conservativeNormal": "✅ Recomendado. Validar modelo con riesgo bajo. Objetivo: mover {{count}} colas de Tier 4→3.",
+      "moderateEnabler": "Habilitador parcial. Desbloquea {{amount}}/año en Wave 4. Decidir Go/No-Go en Q3 2026.",
+      "moderateNormal": "Decidir Go/No-Go en Q3 2026 basado en resultados Wave 1-2. Requiere Score ≥5.5 en colas target.",
+      "aggressivePositive": "⚠️ Aspiracional. Solo si Waves 1-3 exitosas y hay colas con Score ≥7.5. Decisión en Q1 2027.",
+      "aggressiveNegative": "❌ No rentable con el volumen actual. Requiere escala significativamente mayor."
+    },
+    "table": {
+      "topQueuesByVolumeImpact": "Top Colas por Volumen × Impacto",
+      "queue": "Cola",
+      "volPerMonth": "Vol/mes",
+      "score": "Score",
+      "tier": "Tier",
+      "redFlags": "Red Flags",
+      "potential": "Potencial",
+      "redFlagsNote": "Red Flags: CV >120% (alta variabilidad) · Transfer >50% (proceso fragmentado) · Vol <50 (muestra pequeña) · Valid <30% (datos ruidosos)",
+      "skills": "Skills",
+      "entry": "ENTRADA",
+      "exit": "SALIDA",
+      "tierLabel": "Tier:",
+      "scoreLabel": "Score:",
+      "financialMetrics": "Métricas Financieras",
+      "setupLabel": "Setup",
+      "recurringPerYear": "Recurrente/año",
+      "savingsPerYear": "Ahorro/año",
+      "marginPerYear": "Margen/año",
+      "initiativesLabel": "Iniciativas:",
+      "setup": "Setup:",
+      "rec": "Rec:",
+      "kpi": "KPI:",
+      "successCriteriaLabel": "✅ Criterios de éxito:",
+      "condition": "⚠️ Condición:",
+      "provider": "Proveedor:"
+    },
+    "porQueNecesarioTemplates": {
+      "wave1": "{{count}} de {{total}} colas están en Tier 3-4 ({{pct}}% del volumen). Red flags: CV >75%, Transfer >20%. Automatizar sin estandarizar = fracaso garantizado.",
+      "wave2": "Implementar herramientas de soporte para colas Tier 3 (Score 3.5-5.5). Objetivo: elevar score a ≥5.5 para habilitar Wave 3. Foco en {{count}} colas con {{volume}} int/mes.",
+      "wave3": "Copilot IA para agentes en colas Tier 2. Sugerencias en tiempo real, autocompletado, next-best-action. Objetivo: elevar score a ≥7.5 para Wave 4. Target: {{count}} colas con {{volume}} int/mes.",
+      "wave4": "Automatización end-to-end para colas Tier 1. Voicebot/Chatbot transaccional con 70% contención. Solo viable con procesos maduros. Target actual: {{count}} colas con {{volume}} int/mes."
+    },
+    "fallbackSkills": {
+      "wave1": "Colas que alcancen Score 3.5-5.5 post Wave 1",
+      "wave2": "Colas que alcancen Score ≥5.5 post Wave 2",
+      "wave3": "Colas que alcancen Score ≥7.5 post Wave 3"
+    },
+    "wave2Description": {
+      "ready": "{{skill}} es el skill con mejor Score ({{score}}/10, categoría \"Copilot\"). Volumen {{volume}}/año = mayor impacto económico.",
+      "notReady": "Ningún skill alcanza actualmente Score ≥6. El mejor candidato es {{skill}} con Score {{score}}/10. Requiere optimización previa en Wave 1."
+    },
+    "specificRecommendations": {
+      "launchWave4": "Lanzar Wave 4 (AUTOMATE) en piloto",
+      "launchWave4Rationale": "{{count}} colas ya tienen Score ≥7.5 con volumen de {{volume}} int/mes.",
+      "launchWave4NextStep": "Iniciar piloto de automatización en las 2-3 colas de mayor volumen con ahorro potencial de {{amount}}/año.",
+      "initiateWave3": "Iniciar Wave 3 (ASSIST) con Copilot",
+      "initiateWave3Rationale": "{{count}} colas tienen Score 5.5-7.5, representando {{pct}}% del volumen.",
+      "initiateWave3NextStep": "Desplegar Copilot IA en colas Tier 2 para elevar score a ≥7.5 y habilitar Wave 4. Inversión: {{amount}}.",
+      "prioritizeWave1": "Priorizar Wave 1 (FOUNDATION)",
+      "prioritizeWave1Rationale": "{{count}} colas ({{pct}}%) tienen Red Flags que impiden automatización.",
+      "prioritizeWave1NextStep": "Estandarizar procesos antes de invertir en IA. La automatización sin fundamentos sólidos fracasa en 80%+ de casos.",
+      "executeWave12": "Ejecutar Wave 1-2 secuencialmente",
+      "executeWave12Rationale": "Operación mixta: {{automate}} colas Tier 1, {{assist}} Tier 2, {{augment}} Tier 3, {{human}} Tier 4.",
+      "executeWave12NextStep": "Comenzar con FOUNDATION para eliminar red flags, seguido de AUGMENT para elevar scores. Inversión inicial: {{amount}}."
+    },
+    "dualStrategy": {
+      "explanation": "La Estrategia Dual consiste en ejecutar dos líneas de trabajo en paralelo: <strong>Quick Win</strong> automatiza inmediatamente las {{count}} colas ya preparadas (Tier AUTOMATE, {{pct}}% del volumen), generando retorno desde el primer mes; mientras que <strong>Foundation</strong> prepara el {{remaining}}% restante del volumen (Tiers ASSIST y AUGMENT) estandarizando procesos y reduciendo variabilidad para habilitar automatización futura. Este enfoque maximiza el time-to-value: Quick Win financia la transformación y genera confianza organizacional, mientras Foundation amplía progresivamente el alcance de la automatización."
+    }
   },
   "opportunities": {
     "viewCriticalActions": "Ver Acciones Críticas",
@@ -570,12 +864,16 @@
     "humanOnlyAction": "Mantener gestión humana, evaluar periódicamente",
     "redFlags": {
       "cvCritical": "CV AHT Crítico",
+      "cvCriticalShort": "CV",
       "cvCriticalDesc": "Variabilidad extrema - procesos impredecibles",
       "transferExcessive": "Transfer Excesivo",
+      "transferExcessiveShort": "Transfer",
       "transferExcessiveDesc": "Alta complejidad - requiere escalado frecuente",
       "volumeInsufficient": "Volumen Insuficiente",
+      "volumeInsufficientShort": "Vol",
       "volumeInsufficientDesc": "ROI negativo - volumen no justifica inversión",
       "dataQualityLow": "Calidad Datos Baja",
+      "dataQualityLowShort": "Valid",
       "dataQualityLowDesc": "Datos poco fiables - métricas distorsionadas",
       "threshold": "(umbral: {{operator}}{{value}})"
     },
@@ -669,10 +967,17 @@
       "strategicSkill": "Queue Skill (Estratégico)",
       "volume": "Volumen",
       "volumePerMonth": "int/mes",
+      "aht": "AHT",
+      "cv": "CV",
+      "fcr": "FCR",
       "ahtAvg": "AHT Prom.",
       "cvAvg": "CV Prom.",
+      "avgAht": "AHT Prom.",
+      "avgCv": "CV Prom.",
       "savingsPotential": "Ahorro Potencial",
+      "potentialSavings": "Ahorro Potencial",
       "dominantTier": "Tier Dom.",
+      "tier": "Tier",
       "transfer": "Transfer",
       "redFlags": "Red Flags",
       "savingsPerMonth": "Ahorro/mes",
@@ -694,6 +999,9 @@
       "tierAutoAssist": "(Tier AUTOMATE + ASSIST)",
       "interactions": "interacciones",
       "queuesAnalyzed": "colas analizadas",
+      "volumeInIndividualQueues": "del volumen está en colas individuales",
+      "balancedDistribution": "Distribución equilibrada entre tiers. Revisar colas individuales para priorización.",
+      "clickToExpand": "Click en un skill para ver el detalle de colas individuales",
       "interpretation": "Interpretación:",
       "interpretationText": "El {{pct}}% representa el volumen de interacciones automatizables (AUTOMATE + ASSIST). Solo el {{queuePct}}% de las colas ({{count}} de {{total}}) son AUTOMATE, pero concentran {{volumePct}}% del volumen total. Esto indica pocas colas de alto volumen automatizables - oportunidad concentrada en Quick Wins de alto impacto.",
       "inSkills": "en {{count}} skills",
@@ -708,6 +1016,27 @@
       "activeFilters": "Filtros activos:",
       "of": "de"
     },
+    "emptyStates": {
+      "noQueuesClassifiedAs": "No hay colas clasificadas como {{tier}}",
+      "noQueuesMatchFilters": "No hay colas que cumplan los filtros seleccionados"
+    },
+    "sections": {
+      "classificationBySkill": "CLASIFICACIÓN POR SKILL",
+      "classificationByTier": "CLASIFICACIÓN POR TIER DE AUTOMATIZACIÓN",
+      "queuesAutomate": "Colas AUTOMATE",
+      "readyForFullAutomation": "Listas para automatización completa con agente virtual (Score ≥7.5)",
+      "queuesAssist": "Colas ASSIST",
+      "candidatesForCopilot": "Candidatas a Copilot - IA asiste al agente humano (Score 5.5-7.5)",
+      "queuesAugment": "Colas AUGMENT",
+      "requireOptimization": "Requieren optimización previa: estandarizar procesos, reducir variabilidad (Score 3.5-5.5)",
+      "queuesHumanOnly": "Colas HUMAN-ONLY",
+      "notSuitableForAutomation": "No aptas para automatización: volumen insuficiente, calidad de datos baja o complejidad extrema",
+      "queuesIn": "colas en {{count}} skills",
+      "costPerYear": "/año",
+      "volumeColon": "Volumen:",
+      "costColon": "Coste:",
+      "potentialSavingsColon": "Ahorro potencial:"
+    },
     "opportunityMap": {
       "title": "Mapa de Oportunidades",
       "subtitle": "Tamaño = Volumen · Color = Tier · Posición = Score vs Ahorro TCO",
@@ -799,7 +1128,38 @@
       "standardizeProcesses": "Estandarizar procesos y scripts",
       "simplifyFlow": "Simplificar flujo, capacitar agentes",
       "consolidate": "Consolidar con colas similares",
-      "improveDataCapture": "Mejorar captura de datos"
+      "improveDataCapture": "Mejorar captura de datos",
+      "skillsWithRedFlags": "Skills con Red Flags",
+      "queuesRequireIntervention": "Colas que requieren intervención antes de automatizar",
+      "viewRoadmapTab": "Ver pestaña Roadmap para plan detallado",
+      "viewRoadmapLink": "Ver pestaña Roadmap para plan detallado →"
+    },
+    "roadmapConnection": {
+      "quickWinsTitle": "QUICK WINS INMEDIATOS (sin Wave 1)",
+      "automateQueues": "{{count}} colas AUTOMATE",
+      "with": "con",
+      "interactionsPerMonth": "interacciones/mes",
+      "savingsPotential": "Ahorro potencial:",
+      "perYear": "/año",
+      "containment": "contención",
+      "perInt": "/int",
+      "skills": "Skills:",
+      "alignedWithWave4": "→ Alineado con Wave 4 del Roadmap. Pueden implementarse en paralelo a Wave 1.",
+      "wave13Title": "WAVE 1-3: FOUNDATION → ASSIST ({{count}} colas)",
+      "inTierAssist": "en tier ASSIST",
+      "focusWave1": "Foco Wave 1:",
+      "reduceTransferIn": "Reducir transfer en",
+      "potentialWithCopilot": "Potencial con Copilot:",
+      "deflection": "deflection",
+      "requiresWave1": "→ Requiere Wave 1 (Foundation) para habilitar Copilot en Wave 3",
+      "calculationNote": "Cálculo: {{volume}} int × 12 meses × {{rate}}% {{type}} × €{{cpi}}/int",
+      "quickWinsHaveVolume": "tienen >60% volumen en T1+T2",
+      "hasPercentInHuman": "tiene {{pct}}% en HUMAN",
+      "prioritizeInWave1": "→ priorizar en Wave 1",
+      "balancedDistribution": "Distribución equilibrada entre tiers. Revisar colas individuales para priorización.",
+      "haveAtLeastOne": "tienen al menos una cola tier AUTOMATE",
+      "showLess": "Mostrar menos",
+      "viewAll": "Ver todos ({{count}})"
     },
     "factorsExtended": {
       "volumeMethodology": "Score = log10(Volumen) normalizado. >5000 → 10, <100 → 2",
@@ -814,6 +1174,102 @@
       "roiBad": "ROI marginal, evaluar otros beneficios",
       "resolution": "Resolutividad",
       "dataQuality": "Calidad Datos"
+    },
+    "factorConfigs": {
+      "predictability": {
+        "title": "Predictibilidad",
+        "description": "Consistencia en tiempos de gestión",
+        "methodology": "Score = 10 - (CV_AHT / 10). CV AHT < 30% → Score > 7",
+        "benchmark": "CV AHT óptimo < 25%",
+        "highImplication": "Tiempos consistentes, ideal para IA",
+        "lowImplication": "Requiere estandarización"
+      },
+      "inverseComplexity": {
+        "title": "Simplicidad",
+        "description": "Bajo nivel de juicio humano requerido",
+        "methodology": "Score = 10 - (Tasa_Transfer × 0.4). Transfer <10% → Score > 6",
+        "benchmark": "Transferencias óptimas <10%",
+        "highImplication": "Procesos simples, automatizables",
+        "lowImplication": "Alta complejidad, requiere copilot"
+      },
+      "repeatability": {
+        "title": "Volumen",
+        "description": "Escala para justificar inversión",
+        "methodology": "Score = log10(Volumen) normalizado. >5000 → 10, <100 → 2",
+        "benchmark": "ROI positivo requiere >500/mes",
+        "highImplication": "Alto volumen justifica inversión",
+        "lowImplication": "Considerar soluciones compartidas"
+      },
+      "roiPotential": {
+        "title": "ROI Potencial",
+        "description": "Retorno económico esperado",
+        "methodology": "Score basado en coste anual total. >€500K → 10",
+        "benchmark": "ROI >150% a 12 meses",
+        "highImplication": "Caso de negocio sólido",
+        "lowImplication": "ROI marginal, evaluar otros beneficios"
+      }
+    },
+    "scoreBreakdown": {
+      "predictability": "Predictibilidad (30%)",
+      "resolution": "Resolutividad (25%)",
+      "volume": "Volumen (25%)",
+      "dataQuality": "Calidad Datos (10%)",
+      "simplicity": "Simplicidad (10%)"
+    },
+    "bubbleChart": {
+      "quickWinsCount": "{{count}} colas · {{amount}}",
+      "highPotentialCount": "{{count}} colas · {{amount}}",
+      "developCount": "{{count}} colas · {{amount}}",
+      "easyImplCount": "{{count}} · {{amount}}",
+      "backlogCount": "{{count}} · {{amount}}",
+      "total": "total",
+      "noQueuesFilters": "No hay colas que cumplan los filtros seleccionados",
+      "quickWinsLabel": "QUICK WINS",
+      "highPotentialLabel": "ALTO POTENCIAL",
+      "developLabel": "DESARROLLAR",
+      "easyImplLabel": "FÁCIL IMPL.",
+      "backlogLabel": "BACKLOG",
+      "activeFiltersLabel": "Filtros activos:",
+      "ofQueues": "de {{total}} colas",
+      "perMonth": "/mes",
+      "cvAht": "CV AHT:",
+      "viewDetail": "Click para ver detalle"
+    },
+    "modal": {
+      "skillLabel": "Skill:",
+      "transferRate": "Transfer Rate",
+      "annualSavings": "Ahorro Anual"
+    },
+    "volumeLabels": {
+      "queues": "colas",
+      "int": "int"
+    },
+    "subFactors": {
+      "repeatability": "Repetitividad",
+      "repeatabilityDisplayName": "Repetitividad",
+      "repeatabilityDescription": "Volumen mensual: {{volume}} interacciones",
+      "predictability": "Predictibilidad",
+      "predictabilityDisplayName": "Predictibilidad",
+      "predictabilityDescription": "CV AHT: {{cv}}%, Escalación: {{esc}}%",
+      "structuring": "Estructuración",
+      "structuringDisplayName": "Estructuración",
+      "structuringDescription": "{{pct}}% de campos estructurados",
+      "inverseComplexity": "Complejidad Inversa",
+      "inverseComplexityDisplayName": "Complejidad Inversa",
+      "inverseComplexityDescription": "{{pct}}% de excepciones",
+      "stability": "Estabilidad",
+      "stabilityDisplayName": "Estabilidad",
+      "stabilityDescription": "{{pct}}% fuera de horario",
+      "roiSavings": "ROI",
+      "roiSavingsDisplayName": "ROI",
+      "roiSavingsDescription": "€{{amount}}K ahorro potencial anual",
+      "interpretations": {
+        "excellentForAutomation": "Excelente candidato para automatización completa (Automate)",
+        "goodForAssistance": "Buen candidato para asistencia agéntica (Assist)",
+        "candidateForAugmentation": "Candidato para augmentación humana (Augment)",
+        "notRecommended": "No recomendado para automatización en este momento",
+        "bronzeAnalysis": "Análisis Bronze no incluye Agentic Readiness Score"
+      }
     }
   },
   "economicModel": {
@@ -1312,6 +1768,142 @@
       "requiredData": "Requiere datos",
       "score": "Score",
       "gap": "Gap"
+    },
+    "summaryTable": {
+      "requirement": "Requisito",
+      "description": "Descripción",
+      "status": "Estado",
+      "score": "Score",
+      "gap": "Gap",
+      "legend": {
+        "complies": "Cumple: Requisito satisfecho",
+        "partial": "Parcial: Requiere mejoras",
+        "notComply": "No Cumple: Acción urgente",
+        "noData": "Sin Datos: Campos no disponibles en CSV"
+      },
+      "investment": {
+        "nonComplianceCost": "Coste de no cumplimiento",
+        "upTo100k": "Hasta 100K",
+        "potentialFines": "Multas potenciales/infracción",
+        "recommendedInvestment": "Inversión recomendada",
+        "basedOnOperation": "Basada en tu operación",
+        "complianceRoi": "ROI de cumplimiento",
+        "avoidSanctions": "Evitar sanciones + mejora CX"
+      }
+    },
+    "dataMaturity": {
+      "title": "Resumen: Madurez de Datos para Compliance",
+      "currentLevel": "Tu nivel actual de instrumentación:",
+      "availableData": "DATOS DISPONIBLES (3/10)",
+      "estimableData": "DATOS ESTIMABLES (2/10)",
+      "unavailableData": "NO DISPONIBLES (5/10)",
+      "items": {
+        "coverage247": "Cobertura temporal 24/7",
+        "geoDistribution": "Distribución geográfica",
+        "resolutionQuality": "Calidad resolución proxy",
+        "asa3min": "ASA <3min vía proxy abandono",
+        "officialLanguages": "Lenguas cooficiales vía país",
+        "caseResolutionTime": "Tiempo resolución casos",
+        "undueBilling": "Cobros indebidos <5 días",
+        "supervisorTransfer": "Transfer a supervisor",
+        "incidentInfo": "Info incidencias <2h",
+        "enacAudit": "Auditoría ENAC",
+        "externalContractRequired": "requiere contratación externa"
+      },
+      "investment": {
+        "title": "INVERSIÓN SUGERIDA PARA COMPLIANCE COMPLETO",
+        "phase1": {
+          "title": "Fase 1 - Instrumentación (Q1 2026)",
+          "realAsaTracking": "• Tracking ASA real",
+          "ticketingSystem": "• Sistema ticketing/casos",
+          "languageEnrichment": "• Enriquecimiento lenguas",
+          "subtotal": "Subtotal:"
+        },
+        "phase2": {
+          "title": "Fase 2 - Operaciones (Q2-Q3 2026)",
+          "coverage247": "• Cobertura 24/7 (chatbot + on-call)",
+          "aiCopilot": "• Copilot IA (reducir AHT)",
+          "enacAuditor": "• Auditor ENAC",
+          "subtotalYear1": "Subtotal año 1:"
+        },
+        "totals": {
+          "totalInvestment": "Inversión Total",
+          "percentAnnualCost": "~5% coste anual",
+          "riskAvoided": "Riesgo Evitado",
+          "potentialSanctions": "sanciones potenciales",
+          "complianceRoi": "ROI Compliance"
+        }
+      }
+    }
+  },
+  "opportunityPrioritizer": {
+    "title": "Oportunidades Priorizadas",
+    "subtitle": "{{count}} iniciativas ordenadas por potencial de ahorro y factibilidad",
+    "whereAreOpportunities": "¿Dónde están las oportunidades?",
+    "totalSavingsIdentified": "Ahorro Total Identificado",
+    "annual": "anuales",
+    "quickWins": "Quick Wins (AUTOMATE)",
+    "assistance": "Asistencia (ASSIST)",
+    "optimization": "Optimización (AUGMENT)",
+    "inMonths": "en {{count}} meses",
+    "startHere": "EMPIEZA AQUÍ",
+    "priority1": "Prioridad #1",
+    "annualSavings": "Ahorro Anual",
+    "volume": "Volumen",
+    "timeline": "Timeline",
+    "months": "meses",
+    "nextSteps": "Próximos Pasos",
+    "allOpportunities": "Todas las Oportunidades Priorizadas",
+    "savings": "Ahorro",
+    "valueEffort": "Valor / Esfuerzo",
+    "value": "Valor",
+    "effort": "Esfuerzo",
+    "viewMore": "Ver {{count}} oportunidades más",
+    "methodology": "Metodología de priorización:",
+    "methodologyDescription": "Las oportunidades se ordenan por potencial de ahorro TCO (volumen × tasa de contención × diferencial CPI). La clasificación de tier AUTOMATE/ASSIST/AUGMENT se basa en el Agentic Readiness Score considerando predictibilidad (CV AHT), resolvibilidad (FCR + Transfer), volumen, calidad de datos y simplicidad del proceso.",
+    "tierLabels": {
+      "automate": "Automatizar",
+      "assist": "Asistir",
+      "augment": "Aumentar",
+      "human": "Humano"
+    },
+    "timelines": {
+      "automate": "3-6 meses",
+      "assist": "6-9 meses",
+      "augment": "9-12 meses"
+    },
+    "tierDescriptions": {
+      "automate": "Automatización completa con agentes IA",
+      "assist": "Copilot IA para agentes humanos",
+      "augment": "Estandarización y mejora de procesos",
+      "humanOnly": "Requiere intervención humana"
+    },
+    "agenticScore": "Puntuación Agéntica",
+    "whyPriority1": "¿Por qué es la prioridad #1?",
+    "viewCompleteDetail": "Ver Detalle Completo",
+    "showLess": "Mostrar menos",
+    "whyThisPosition": "¿Por qué esta posición?",
+    "keyMetrics": "Métricas Clave",
+    "noOpportunitiesTitle": "No hay oportunidades identificadas",
+    "noOpportunitiesDescription": "Los datos actuales no muestran oportunidades de automatización viables.",
+    "reasons": {
+      "highSavingsPotential": "Alto ahorro potencial (€{{amount}}K/año)",
+      "highVolume": "Alto volumen ({{volume}} interacciones)",
+      "highlyPredictable": "Proceso altamente predecible y repetitivo",
+      "lowVariability": "Baja variabilidad en tiempos de gestión",
+      "lowTransferRate": "Baja tasa de transferencias",
+      "highFeasibility": "Alta factibilidad técnica"
+    },
+    "steps": {
+      "automate1": "Definir flujos conversacionales principales",
+      "automate2": "Identificar integraciones necesarias (CRM, APIs)",
+      "automate3": "Crear piloto con 10% del volumen",
+      "assist1": "Mapear puntos de fricción del agente",
+      "assist2": "Diseñar sugerencias contextuales",
+      "assist3": "Piloto con equipo seleccionado",
+      "augment1": "Analizar causa raíz de variabilidad",
+      "augment2": "Estandarizar procesos y scripts",
+      "augment3": "Capacitar equipo en mejores prácticas"
     }
   }
 }

frontend/utils/agenticReadinessV2.ts

@@ -1,20 +1,20 @@
 /**
  * Agentic Readiness Score v2.0
- * Algoritmo basado en metodología de 6 dimensiones con normalización continua
+ * Algorithm based on 6-dimension methodology with continuous normalization
  */
 
 import type { TierKey, SubFactor, AgenticReadinessResult, CustomerSegment } from '../types';
 import { AGENTIC_READINESS_WEIGHTS, AGENTIC_READINESS_THRESHOLDS } from '../constants';
 
 export interface AgenticReadinessInput {
-  // Datos básicos (SILVER)
+  // Basic data (SILVER)
   volumen_mes: number;
   aht_values: number[];
   escalation_rate: number;
   cpi_humano: number;
   volumen_anual: number;
-  
-  // Datos avanzados (GOLD)
+
+  // Advanced data (GOLD)
   structured_fields_pct?: number;
   exception_rate?: number;
   hourly_distribution?: number[];
@@ -22,27 +22,27 @@ export interface AgenticReadinessInput {
   csat_values?: number[];
   motivo_contacto_entropy?: number;
   resolucion_entropy?: number;
-  
+
   // Tier
   tier: TierKey;
 }
 
 /**
- * SUB-FACTOR 1: REPETITIVIDAD (25%)
- * Basado en volumen mensual con normalización logística
+ * SUB-FACTOR 1: REPEATABILITY (25%)
+ * Based on monthly volume with logistic normalization
  */
-function calculateRepetitividadScore(volumen_mes: number): SubFactor {
+function calculateRepeatabilityScore(volumen_mes: number): SubFactor {
   const { k, x0 } = AGENTIC_READINESS_THRESHOLDS.repetitividad;
-  
-  // Función logística: score = 10 / (1 + exp(-k * (volumen - x0)))
+
+  // Logistic function: score = 10 / (1 + exp(-k * (volume - x0)))
   const score = 10 / (1 + Math.exp(-k * (volumen_mes - x0)));
-  
+
   return {
-    name: 'repetitividad',
-    displayName: 'Repetitividad',
+    name: 'repeatability',
+    displayName: 'Repeatability',
     score: Math.round(score * 10) / 10,
     weight: AGENTIC_READINESS_WEIGHTS.repetitividad,
-    description: `Volumen mensual: ${volumen_mes} interacciones`,
+    description: `Monthly volume: ${volumen_mes} interactions`,
     details: {
       volumen_mes,
       threshold_medio: x0
@@ -51,58 +51,58 @@ function calculateRepetitividadScore(volumen_mes: number): SubFactor {
 }
 
 /**
- * SUB-FACTOR 2: PREDICTIBILIDAD (20%)
- * Basado en variabilidad AHT + tasa de escalación + variabilidad input/output
+ * SUB-FACTOR 2: PREDICTABILITY (20%)
+ * Based on AHT variability + escalation rate + input/output variability
  */
-function calculatePredictibilidadScore(
+function calculatePredictabilityScore(
   aht_values: number[],
   escalation_rate: number,
   motivo_contacto_entropy?: number,
   resolucion_entropy?: number
 ): SubFactor {
   const thresholds = AGENTIC_READINESS_THRESHOLDS.predictibilidad;
-  
-  // 1. VARIABILIDAD AHT (40%)
+
+  // 1. AHT VARIABILITY (40%)
   const aht_mean = aht_values.reduce((a, b) => a + b, 0) / aht_values.length;
   const aht_variance = aht_values.reduce((sum, val) => sum + Math.pow(val - aht_mean, 2), 0) / aht_values.length;
   const aht_std = Math.sqrt(aht_variance);
   const cv_aht = aht_std / aht_mean;
-  
-  // Normalizar CV a escala 0-10
-  const score_aht = Math.max(0, Math.min(10, 
+
+  // Normalize CV to 0-10 scale
+  const score_aht = Math.max(0, Math.min(10,
     10 * (1 - (cv_aht - thresholds.cv_aht_excellent) / (thresholds.cv_aht_poor - thresholds.cv_aht_excellent))
   ));
-  
-  // 2. TASA DE ESCALACIÓN (30%)
-  const score_escalacion = Math.max(0, Math.min(10, 
+
+  // 2. ESCALATION RATE (30%)
+  const score_escalacion = Math.max(0, Math.min(10,
     10 * (1 - escalation_rate / thresholds.escalation_poor)
   ));
-  
-  // 3. VARIABILIDAD INPUT/OUTPUT (30%)
+
+  // 3. INPUT/OUTPUT VARIABILITY (30%)
   let score_variabilidad: number;
   if (motivo_contacto_entropy !== undefined && resolucion_entropy !== undefined) {
-    // Alta entropía input + Baja entropía output = BUENA para automatización
+    // High input entropy + Low output entropy = GOOD for automation
     const input_normalized = Math.min(motivo_contacto_entropy / 3.0, 1.0);
     const output_normalized = Math.min(resolucion_entropy / 3.0, 1.0);
     score_variabilidad = 10 * (input_normalized * (1 - output_normalized));
   } else {
-    // Si no hay datos de entropía, usar promedio de AHT y escalación
+    // If no entropy data, use average of AHT and escalation
     score_variabilidad = (score_aht + score_escalacion) / 2;
   }
-  
-  // PONDERACIÓN FINAL
-  const predictibilidad = (
+
+  // FINAL WEIGHTING
+  const predictabilidad = (
     0.40 * score_aht +
     0.30 * score_escalacion +
     0.30 * score_variabilidad
   );
-  
+
   return {
-    name: 'predictibilidad',
-    displayName: 'Predictibilidad',
-    score: Math.round(predictibilidad * 10) / 10,
+    name: 'predictability',
+    displayName: 'Predictability',
+    score: Math.round(predictabilidad * 10) / 10,
     weight: AGENTIC_READINESS_WEIGHTS.predictibilidad,
-    description: `CV AHT: ${(cv_aht * 100).toFixed(1)}%, Escalación: ${(escalation_rate * 100).toFixed(1)}%`,
+    description: `AHT CV: ${(cv_aht * 100).toFixed(1)}%, Escalation: ${(escalation_rate * 100).toFixed(1)}%`,
     details: {
       cv_aht: Math.round(cv_aht * 1000) / 1000,
       escalation_rate,
@@ -114,18 +114,18 @@ function calculatePredictibilidadScore(
 }
 
 /**
- * SUB-FACTOR 3: ESTRUCTURACIÓN (15%)
- * Porcentaje de campos estructurados vs texto libre
+ * SUB-FACTOR 3: STRUCTURING (15%)
+ * Percentage of structured fields vs free text
  */
-function calculateEstructuracionScore(structured_fields_pct: number): SubFactor {
+function calculateStructuringScore(structured_fields_pct: number): SubFactor {
   const score = structured_fields_pct * 10;
-  
+
   return {
-    name: 'estructuracion',
-    displayName: 'Estructuración',
+    name: 'structuring',
+    displayName: 'Structuring',
     score: Math.round(score * 10) / 10,
     weight: AGENTIC_READINESS_WEIGHTS.estructuracion,
-    description: `${(structured_fields_pct * 100).toFixed(0)}% de campos estructurados`,
+    description: `${(structured_fields_pct * 100).toFixed(0)}% structured fields`,
     details: {
       structured_fields_pct
     }
@@ -133,21 +133,21 @@ function calculateEstructuracionScore(structured_fields_pct: number): SubFactor
 }
 
 /**
- * SUB-FACTOR 4: COMPLEJIDAD INVERSA (15%)
- * Basado en tasa de excepciones
+ * SUB-FACTOR 4: INVERSE COMPLEXITY (15%)
+ * Based on exception rate
  */
-function calculateComplejidadInversaScore(exception_rate: number): SubFactor {
-  // Menor tasa de excepciones → Mayor score
-  // < 5% → Excelente (score 10)
-  // > 30% → Muy complejo (score 0)
+function calculateInverseComplexityScore(exception_rate: number): SubFactor {
+  // Lower exception rate → Higher score
+  // < 5% → Excellent (score 10)
+  // > 30% → Very complex (score 0)
   const score_excepciones = Math.max(0, Math.min(10, 10 * (1 - exception_rate / 0.30)));
-  
+
   return {
-    name: 'complejidad_inversa',
-    displayName: 'Complejidad Inversa',
+    name: 'inverseComplexity',
+    displayName: 'Inverse Complexity',
     score: Math.round(score_excepciones * 10) / 10,
     weight: AGENTIC_READINESS_WEIGHTS.complejidad_inversa,
-    description: `${(exception_rate * 100).toFixed(1)}% de excepciones`,
+    description: `${(exception_rate * 100).toFixed(1)}% exceptions`,
     details: {
       exception_rate
     }
@@ -155,15 +155,15 @@ function calculateComplejidadInversaScore(exception_rate: number): SubFactor {
 }
 
 /**
- * SUB-FACTOR 5: ESTABILIDAD (10%)
- * Basado en distribución horaria y % llamadas fuera de horas
+ * SUB-FACTOR 5: STABILITY (10%)
+ * Based on hourly distribution and % off-hours calls
  */
-function calculateEstabilidadScore(
+function calculateStabilityScore(
   hourly_distribution: number[],
   off_hours_pct: number
 ): SubFactor {
-  // 1. UNIFORMIDAD DISTRIBUCIÓN HORARIA (60%)
-  // Calcular entropía de Shannon
+  // 1. HOURLY DISTRIBUTION UNIFORMITY (60%)
+  // Calculate Shannon entropy
   const total = hourly_distribution.reduce((a, b) => a + b, 0);
   let score_uniformidad = 0;
   let entropy_normalized = 0;
@@ -175,23 +175,23 @@ function calculateEstabilidadScore(
     entropy_normalized = entropy / max_entropy;
     score_uniformidad = entropy_normalized * 10;
   }
-  
-  // 2. % LLAMADAS FUERA DE HORAS (40%)
-  // Más llamadas fuera de horas → Mayor necesidad agentes → Mayor score
+
+  // 2. % OFF-HOURS CALLS (40%)
+  // More off-hours calls → Higher agent need → Higher score
   const score_off_hours = Math.min(10, (off_hours_pct / 0.30) * 10);
-  
-  // PONDERACIÓN
+
+  // WEIGHTING
   const estabilidad = (
     0.60 * score_uniformidad +
     0.40 * score_off_hours
   );
-  
+
   return {
-    name: 'estabilidad',
-    displayName: 'Estabilidad',
+    name: 'stability',
+    displayName: 'Stability',
     score: Math.round(estabilidad * 10) / 10,
     weight: AGENTIC_READINESS_WEIGHTS.estabilidad,
-    description: `${(off_hours_pct * 100).toFixed(1)}% fuera de horario`,
+    description: `${(off_hours_pct * 100).toFixed(1)}% off-hours`,
     details: {
       entropy_normalized: Math.round(entropy_normalized * 1000) / 1000,
       off_hours_pct,
@@ -203,7 +203,7 @@ function calculateEstabilidadScore(
 
 /**
  * SUB-FACTOR 6: ROI (15%)
- * Basado en ahorro potencial anual
+ * Based on annual potential savings
  */
 function calculateROIScore(
   volumen_anual: number,
@@ -211,17 +211,17 @@ function calculateROIScore(
   automation_savings_pct: number = 0.70
 ): SubFactor {
   const ahorro_anual = volumen_anual * cpi_humano * automation_savings_pct;
-  
-  // Normalización logística
+
+  // Logistic normalization
   const { k, x0 } = AGENTIC_READINESS_THRESHOLDS.roi;
   const score = 10 / (1 + Math.exp(-k * (ahorro_anual - x0)));
-  
+
   return {
     name: 'roi',
     displayName: 'ROI',
     score: Math.round(score * 10) / 10,
     weight: AGENTIC_READINESS_WEIGHTS.roi,
-    description: `€${(ahorro_anual / 1000).toFixed(0)}K ahorro potencial anual`,
+    description: `€${(ahorro_anual / 1000).toFixed(0)}K annual potential savings`,
     details: {
       ahorro_anual: Math.round(ahorro_anual),
       volumen_anual,
@@ -232,98 +232,98 @@ function calculateROIScore(
 }
 
 /**
- * AJUSTE POR DISTRIBUCIÓN CSAT (Opcional, ±10%)
- * Distribución normal → Proceso estable
+ * CSAT DISTRIBUTION ADJUSTMENT (Optional, ±10%)
+ * Normal distribution → Stable process
  */
 function calculateCSATDistributionAdjustment(csat_values: number[]): number {
-  // Test de normalidad simplificado (basado en skewness y kurtosis)
+  // Simplified normality test (based on skewness and kurtosis)
   const n = csat_values.length;
   const mean = csat_values.reduce((a, b) => a + b, 0) / n;
   const variance = csat_values.reduce((sum, val) => sum + Math.pow(val - mean, 2), 0) / n;
   const std = Math.sqrt(variance);
-  
+
   // Skewness
   const skewness = csat_values.reduce((sum, val) => sum + Math.pow((val - mean) / std, 3), 0) / n;
-  
+
   // Kurtosis
   const kurtosis = csat_values.reduce((sum, val) => sum + Math.pow((val - mean) / std, 4), 0) / n;
-  
-  // Normalidad: skewness cercano a 0, kurtosis cercano a 3
+
+  // Normality: skewness close to 0, kurtosis close to 3
   const skewness_score = Math.max(0, 1 - Math.abs(skewness));
   const kurtosis_score = Math.max(0, 1 - Math.abs(kurtosis - 3) / 3);
   const normality_score = (skewness_score + kurtosis_score) / 2;
-  
-  // Ajuste: +5% si muy normal, -5% si muy anormal
+
+  // Adjustment: +5% if very normal, -5% if very abnormal
   const adjustment = 1 + ((normality_score - 0.5) * 0.10);
-  
+
   return adjustment;
 }
 
 /**
- * ALGORITMO COMPLETO (Tier GOLD)
+ * COMPLETE ALGORITHM (Tier GOLD)
  */
 export function calculateAgenticReadinessScoreGold(data: AgenticReadinessInput): AgenticReadinessResult {
   const sub_factors: SubFactor[] = [];
-  
-  // 1. REPETITIVIDAD
-  sub_factors.push(calculateRepetitividadScore(data.volumen_mes));
-  
-  // 2. PREDICTIBILIDAD
-  sub_factors.push(calculatePredictibilidadScore(
+
+  // 1. REPEATABILITY
+  sub_factors.push(calculateRepeatabilityScore(data.volumen_mes));
+
+  // 2. PREDICTABILITY
+  sub_factors.push(calculatePredictabilityScore(
     data.aht_values,
     data.escalation_rate,
     data.motivo_contacto_entropy,
     data.resolucion_entropy
   ));
-  
-  // 3. ESTRUCTURACIÓN
-  sub_factors.push(calculateEstructuracionScore(data.structured_fields_pct || 0.5));
-  
-  // 4. COMPLEJIDAD INVERSA
-  sub_factors.push(calculateComplejidadInversaScore(data.exception_rate || 0.15));
-  
-  // 5. ESTABILIDAD
-  sub_factors.push(calculateEstabilidadScore(
+
+  // 3. STRUCTURING
+  sub_factors.push(calculateStructuringScore(data.structured_fields_pct || 0.5));
+
+  // 4. INVERSE COMPLEXITY
+  sub_factors.push(calculateInverseComplexityScore(data.exception_rate || 0.15));
+
+  // 5. STABILITY
+  sub_factors.push(calculateStabilityScore(
     data.hourly_distribution || Array(24).fill(1),
     data.off_hours_pct || 0.2
   ));
-  
+
   // 6. ROI
   sub_factors.push(calculateROIScore(
     data.volumen_anual,
     data.cpi_humano
   ));
-  
-  // PONDERACIÓN BASE
+
+  // BASE WEIGHTING
   const agentic_readiness_base = sub_factors.reduce(
     (sum, factor) => sum + (factor.score * factor.weight),
     0
   );
-  
-  // AJUSTE POR DISTRIBUCIÓN CSAT (Opcional)
+
+  // CSAT DISTRIBUTION ADJUSTMENT (Optional)
   let agentic_readiness_final = agentic_readiness_base;
   if (data.csat_values && data.csat_values.length > 10) {
     const adjustment = calculateCSATDistributionAdjustment(data.csat_values);
     agentic_readiness_final = agentic_readiness_base * adjustment;
   }
-  
-  // Limitar a rango 0-10
+
+  // Limit to 0-10 range
   agentic_readiness_final = Math.max(0, Math.min(10, agentic_readiness_final));
-  
-  // Interpretación
+
+  // Interpretation
   let interpretation = '';
   let confidence: 'high' | 'medium' | 'low' = 'high';
-  
+
   if (agentic_readiness_final >= 8) {
-    interpretation = 'Excelente candidato para automatización completa (Automate)';
+    interpretation = 'Excellent candidate for complete automation (Automate)';
   } else if (agentic_readiness_final >= 5) {
-    interpretation = 'Buen candidato para asistencia agéntica (Assist)';
+    interpretation = 'Good candidate for agentic assistance (Assist)';
   } else if (agentic_readiness_final >= 3) {
-    interpretation = 'Candidato para augmentación humana (Augment)';
+    interpretation = 'Candidate for human augmentation (Augment)';
   } else {
-    interpretation = 'No recomendado para automatización en este momento';
+    interpretation = 'Not recommended for automation at this time';
   }
-  
+
   return {
     score: Math.round(agentic_readiness_final * 10) / 10,
     sub_factors,
@@ -334,45 +334,45 @@ export function calculateAgenticReadinessScoreGold(data: AgenticReadinessInput):
 }
 
 /**
- * ALGORITMO SIMPLIFICADO (Tier SILVER)
+ * SIMPLIFIED ALGORITHM (Tier SILVER)
  */
 export function calculateAgenticReadinessScoreSilver(data: AgenticReadinessInput): AgenticReadinessResult {
   const sub_factors: SubFactor[] = [];
-  
-  // 1. REPETITIVIDAD (30%)
-  const repetitividad = calculateRepetitividadScore(data.volumen_mes);
-  repetitividad.weight = 0.30;
-  sub_factors.push(repetitividad);
-  
-  // 2. PREDICTIBILIDAD SIMPLIFICADA (30%)
-  const predictibilidad = calculatePredictibilidadScore(
+
+  // 1. REPEATABILITY (30%)
+  const repeatability = calculateRepeatabilityScore(data.volumen_mes);
+  repeatability.weight = 0.30;
+  sub_factors.push(repeatability);
+
+  // 2. SIMPLIFIED PREDICTABILITY (30%)
+  const predictability = calculatePredictabilityScore(
     data.aht_values,
     data.escalation_rate
   );
-  predictibilidad.weight = 0.30;
-  sub_factors.push(predictibilidad);
-  
+  predictability.weight = 0.30;
+  sub_factors.push(predictability);
+
   // 3. ROI (40%)
   const roi = calculateROIScore(data.volumen_anual, data.cpi_humano);
   roi.weight = 0.40;
   sub_factors.push(roi);
-  
-  // PONDERACIÓN SIMPLIFICADA
+
+  // SIMPLIFIED WEIGHTING
   const agentic_readiness = sub_factors.reduce(
     (sum, factor) => sum + (factor.score * factor.weight),
     0
   );
-  
-  // Interpretación
+
+  // Interpretation
   let interpretation = '';
   if (agentic_readiness >= 7) {
-    interpretation = 'Buen candidato para automatización';
+    interpretation = 'Good candidate for automation';
   } else if (agentic_readiness >= 4) {
-    interpretation = 'Candidato para asistencia agéntica';
+    interpretation = 'Candidate for agentic assistance';
   } else {
-    interpretation = 'Requiere análisis más profundo (considerar GOLD)';
+    interpretation = 'Requires deeper analysis (consider GOLD)';
   }
-  
+
   return {
     score: Math.round(agentic_readiness * 10) / 10,
     sub_factors,
@@ -383,7 +383,7 @@ export function calculateAgenticReadinessScoreSilver(data: AgenticReadinessInput
 }
 
 /**
- * FUNCIÓN PRINCIPAL - Selecciona algoritmo según tier
+ * MAIN FUNCTION - Selects algorithm based on tier
  */
 export function calculateAgenticReadinessScore(data: AgenticReadinessInput): AgenticReadinessResult {
   if (data.tier === 'gold') {
@@ -391,13 +391,13 @@ export function calculateAgenticReadinessScore(data: AgenticReadinessInput): Age
   } else if (data.tier === 'silver') {
     return calculateAgenticReadinessScoreSilver(data);
   } else {
-    // BRONZE: Sin Agentic Readiness
+    // BRONZE: No Agentic Readiness
     return {
       score: 0,
       sub_factors: [],
       tier: 'bronze',
       confidence: 'low',
-      interpretation: 'Análisis Bronze no incluye Agentic Readiness Score'
+      interpretation: 'Bronze analysis does not include Agentic Readiness Score'
     };
   }
 }

frontend/utils/backendMapper.ts

@@ -23,13 +23,13 @@ function safeNumber(value: any, fallback = 0): number {
 function normalizeAhtMetric(ahtSeconds: number): number {
   if (!Number.isFinite(ahtSeconds) || ahtSeconds <= 0) return 0;
 
-  // Ajusta estos números si ves que tus AHTs reales son muy distintos
-  const MIN_AHT = 300;  // AHT muy bueno
-  const MAX_AHT = 1000; // AHT muy malo
+  // Adjust these numbers if your actual AHTs are very different
+  const MIN_AHT = 300;  // Very good AHT
+  const MAX_AHT = 1000; // Very bad AHT
 
   const clamped = Math.max(MIN_AHT, Math.min(MAX_AHT, ahtSeconds));
-  const ratio = (clamped - MIN_AHT) / (MAX_AHT - MIN_AHT); // 0 (mejor) -> 1 (peor)
-  const score = 100 - ratio * 100; // 100 (mejor) -> 0 (peor)
+  const ratio = (clamped - MIN_AHT) / (MAX_AHT - MIN_AHT); // 0 (better) -> 1 (worse)
+  const score = 100 - ratio * 100; // 100 (better) -> 0 (worse)
 
   return Math.round(score);
 }
@@ -74,7 +74,7 @@ function getTopLabel(
   return String(labels[maxIdx]);
 }
 
-// ==== Helpers para distribución horaria (desde heatmap_24x7) ====
+// ==== Helpers for hourly distribution (from heatmap_24x7) ====
 
 function computeHourlyFromHeatmap(heatmap24x7: any): number[] {
   if (!Array.isArray(heatmap24x7) || !heatmap24x7.length) {
@@ -146,7 +146,7 @@ function mapAgenticReadiness(
         description:
           value?.reason ||
           value?.details?.description ||
-          'Sub-factor calculado a partir de KPIs agregados.',
+          'Sub-factor calculated from aggregated KPIs.',
         details: value?.details || {},
       };
     }
@@ -156,7 +156,7 @@ function mapAgenticReadiness(
 
   const interpretation =
     classification?.description ||
-    `Puntuación de preparación agentic: ${score.toFixed(1)}/10`;
+    `Agentic readiness score: ${score.toFixed(1)}/10`;
 
   const computedCount = Object.values(sub_scores).filter(
     (s: any) => s?.computed
@@ -176,7 +176,7 @@ function mapAgenticReadiness(
   };
 }
 
-// ==== Volumetría (dimensión + KPIs) ====
+// ==== Volumetry (dimension + KPIs) ====
 
 function buildVolumetryDimension(
   raw: BackendRawResults
@@ -216,13 +216,13 @@ function buildVolumetryDimension(
   const topChannel = getTopLabel(volumeByChannel?.labels, channelValues);
   const topSkill = getTopLabel(skillLabels, skillValues);
 
-  // Heatmap 24x7 -> distribución horaria
+  // Heatmap 24x7 -> hourly distribution
   const heatmap24x7 = volumetry?.heatmap_24x7;
   const hourly = computeHourlyFromHeatmap(heatmap24x7);
   const offHoursPct = hourly.length ? calcOffHoursPct(hourly) : 0;
   const peakHours = hourly.length ? findPeakHours(hourly) : [];
 
-  console.log('📊 Volumetría backend (mapper):', {
+  console.log('📊 Backend volumetry (mapper):', {
     volumetry,
     volumeByChannel,
     volumeBySkill,
@@ -240,21 +240,21 @@ function buildVolumetryDimension(
 
   if (totalVolume > 0) {
     extraKpis.push({
-      label: 'Volumen total (backend)',
+      label: 'Total volume (backend)',
       value: totalVolume.toLocaleString('es-ES'),
     });
   }
 
   if (numChannels > 0) {
     extraKpis.push({
-      label: 'Canales analizados',
+      label: 'Channels analyzed',
       value: String(numChannels),
     });
   }
 
   if (numSkills > 0) {
     extraKpis.push({
-      label: 'Skills analizadas',
+      label: 'Skills analyzed',
       value: String(numSkills),
     });
 
@@ -271,14 +271,14 @@ function buildVolumetryDimension(
 
   if (topChannel) {
     extraKpis.push({
-      label: 'Canal principal',
+      label: 'Main channel',
       value: topChannel,
     });
   }
 
   if (topSkill) {
     extraKpis.push({
-      label: 'Skill principal',
+      label: 'Main skill',
       value: topSkill,
     });
   }
@@ -287,28 +287,28 @@ function buildVolumetryDimension(
     return { dimension: undefined, extraKpis };
   }
 
-  // Calcular ratio pico/valle para evaluar concentración de demanda
+  // Calculate peak/valley ratio to evaluate demand concentration
   const validHourly = hourly.filter(v => v > 0);
   const maxHourly = validHourly.length > 0 ? Math.max(...validHourly) : 0;
   const minHourly = validHourly.length > 0 ? Math.min(...validHourly) : 1;
   const peakValleyRatio = minHourly > 0 ? maxHourly / minHourly : 1;
   console.log(`⏰ Hourly distribution (backend path): total=${totalVolume}, peak=${maxHourly}, valley=${minHourly}, ratio=${peakValleyRatio.toFixed(2)}`);
 
-  // Score basado en:
-  // - % fuera de horario (>30% penaliza)
-  // - Ratio pico/valle (>3x penaliza)
-  // NO penalizar por tener volumen alto
+  // Score based on:
+  // - % off-hours (>30% penalty)
+  // - Peak/valley ratio (>3x penalty)
+  // DO NOT penalize for having high volume
   let score = 100;
 
-  // Penalización por fuera de horario
+  // Penalty for off-hours
   const offHoursPctValue = offHoursPct * 100;
   if (offHoursPctValue > 30) {
-    score -= Math.min(40, (offHoursPctValue - 30) * 2); // -2 pts por cada % sobre 30%
+    score -= Math.min(40, (offHoursPctValue - 30) * 2); // -2 pts per %  over30%
   } else if (offHoursPctValue > 20) {
-    score -= (offHoursPctValue - 20); // -1 pt por cada % entre 20-30%
+    score -= (offHoursPctValue - 20); // -1 pt per % between 20-30%
   }
 
-  // Penalización por ratio pico/valle alto
+  // Penalty for high peak/valley ratio
   if (peakValleyRatio > 5) {
     score -= 30;
   } else if (peakValleyRatio > 3) {
@@ -321,32 +321,32 @@ function buildVolumetryDimension(
 
   const summaryParts: string[] = [];
   summaryParts.push(
-    `${totalVolume.toLocaleString('es-ES')} interacciones analizadas.`
+    `${totalVolume.toLocaleString('es-ES')} interactions analyzed.`
   );
   summaryParts.push(
-    `${(offHoursPct * 100).toFixed(0)}% fuera de horario laboral (8-19h).`
+    `${(offHoursPct * 100).toFixed(0)}% outside business hours (8-19h).`
   );
   if (peakValleyRatio > 2) {
     summaryParts.push(
-      `Ratio pico/valle: ${peakValleyRatio.toFixed(1)}x - alta concentración de demanda.`
+      `Peak/valley ratio: ${peakValleyRatio.toFixed(1)}x - high demand concentration.`
     );
   }
   if (topSkill) {
-    summaryParts.push(`Skill principal: ${topSkill}.`);
+    summaryParts.push(`Main skill: ${topSkill}.`);
   }
 
-  // Métrica principal accionable: % fuera de horario
+  // Main actionable metric: % off-hours
   const dimension: DimensionAnalysis = {
     id: 'volumetry_distribution',
     name: 'volumetry_distribution',
-    title: 'Volumetría y distribución de demanda',
+    title: 'Volumetry and demand distribution',
     score,
     percentile: undefined,
     summary: summaryParts.join(' '),
     kpi: {
-      label: 'Fuera de horario',
+      label: 'Off-hours',
       value: `${(offHoursPct * 100).toFixed(0)}%`,
-      change: peakValleyRatio > 2 ? `Pico/valle: ${peakValleyRatio.toFixed(1)}x` : undefined,
+      change: peakValleyRatio > 2 ? `Peak/valley: ${peakValleyRatio.toFixed(1)}x` : undefined,
       changeType: offHoursPct > 0.3 ? 'negative' : offHoursPct > 0.2 ? 'neutral' : 'positive'
     },
     icon: BarChartHorizontal,
@@ -362,7 +362,7 @@ function buildVolumetryDimension(
   return { dimension, extraKpis };
 }
 
-// ==== Eficiencia Operativa (v3.2 - con segmentación horaria) ====
+// ==== Operational Efficiency (v3.2 - with hourly segmentation) ====
 
 function buildOperationalEfficiencyDimension(
   raw: BackendRawResults,
@@ -371,25 +371,25 @@ function buildOperationalEfficiencyDimension(
   const op = raw?.operational_performance;
   if (!op) return undefined;
 
-  // AHT Global
+  // Global AHT
   const ahtP50 = safeNumber(op.aht_distribution?.p50, 0);
   const ahtP90 = safeNumber(op.aht_distribution?.p90, 0);
   const ratioGlobal = ahtP90 > 0 && ahtP50 > 0 ? ahtP90 / ahtP50 : safeNumber(op.aht_distribution?.p90_p50_ratio, 1.5);
 
-  // AHT Horario Laboral (8-19h) - estimación basada en distribución
-  // Asumimos que el AHT en horario laboral es ligeramente menor (más eficiente)
-  const ahtBusinessHours = Math.round(ahtP50 * 0.92); // ~8% más eficiente en horario laboral
-  const ratioBusinessHours = ratioGlobal * 0.85; // Menor variabilidad en horario laboral
+  // Business Hours AHT (8-19h) - estimation based on distribution
+  // We assume that AHT during business hours is slightly lower (more efficient)
+  const ahtBusinessHours = Math.round(ahtP50 * 0.92); // ~8% more efficient during business hours
+  const ratioBusinessHours = ratioGlobal * 0.85; // Lower variability during business hours
 
-  // Determinar si la variabilidad se reduce fuera de horario
+  // Determine if variability reduces outside hours
   const variabilityReduction = ratioGlobal - ratioBusinessHours;
   const variabilityInsight = variabilityReduction > 0.3
-    ? 'La variabilidad se reduce significativamente en horario laboral.'
+    ? 'Variability significantly reduces during business hours.'
     : variabilityReduction > 0.1
-    ? 'La variabilidad se mantiene similar en ambos horarios.'
-    : 'La variabilidad es consistente independientemente del horario.';
+    ? 'Variability remains similar in both schedules.'
+    : 'Variability is consistent regardless of schedule.';
 
-  // Score basado en escala definida:
+  // Score based on defined scale:
   // <1.5 = 100pts, 1.5-2.0 = 70pts, 2.0-2.5 = 50pts, 2.5-3.0 = 30pts, >3.0 = 20pts
   let score: number;
   if (ratioGlobal < 1.5) {
@@ -404,9 +404,9 @@ function buildOperationalEfficiencyDimension(
     score = 20;
   }
 
-  // Summary con segmentación
-  let summary = `AHT Global: ${Math.round(ahtP50)}s (P50), ratio ${ratioGlobal.toFixed(2)}. `;
-  summary += `AHT Horario Laboral (8-19h): ${ahtBusinessHours}s (P50), ratio ${ratioBusinessHours.toFixed(2)}. `;
+  // Summary with segmentation
+  let summary = `Global AHT: ${Math.round(ahtP50)}s (P50), ratio ${ratioGlobal.toFixed(2)}. `;
+  summary += `Business Hours AHT (8-19h): ${ahtBusinessHours}s (P50), ratio ${ratioBusinessHours.toFixed(2)}. `;
   summary += variabilityInsight;
 
   // KPI principal: AHT P50 (industry standard for operational efficiency)
@@ -420,7 +420,7 @@ function buildOperationalEfficiencyDimension(
   const dimension: DimensionAnalysis = {
     id: 'operational_efficiency',
     name: 'operational_efficiency',
-    title: 'Eficiencia Operativa',
+    title: 'Operational Efficiency',
     score,
     percentile: undefined,
     summary,
@@ -431,7 +431,7 @@ function buildOperationalEfficiencyDimension(
   return dimension;
 }
 
-// ==== Efectividad & Resolución (v3.2 - enfocada en FCR Técnico) ====
+// ==== Effectiveness & Resolution (v3.2 - focused on Technical FCR) ====
 
 function buildEffectivenessResolutionDimension(
   raw: BackendRawResults
@@ -439,20 +439,20 @@ function buildEffectivenessResolutionDimension(
   const op = raw?.operational_performance;
   if (!op) return undefined;
 
-  // FCR Técnico = 100 - transfer_rate (comparable con benchmarks de industria)
-  // Usamos escalation_rate que es la tasa de transferencias
+  // Technical FCR = 100 - transfer_rate (comparable with industry benchmarks)
+  // We use escalation_rate which is the transfer rate
   const escalationRate = safeNumber(op.escalation_rate, NaN);
   const abandonmentRate = safeNumber(op.abandonment_rate, 0);
 
-  // FCR Técnico: 100 - tasa de transferencia
+  // Technical FCR: 100 - tasa de transferencia
   const fcrRate = Number.isFinite(escalationRate) && escalationRate >= 0
     ? Math.max(0, Math.min(100, 100 - escalationRate))
-    : 70; // valor por defecto benchmark aéreo
+    : 70; // default airline benchmark value
 
-  // Tasa de transferencia (complemento del FCR Técnico)
+  // Transfer rate (complement of Technical FCR)
   const transferRate = Number.isFinite(escalationRate) ? escalationRate : 100 - fcrRate;
 
-  // Score basado en FCR Técnico (benchmark sector aéreo: 85-90%)
+  // Score based on Technical FCR (benchmark airline sector: 85-90%)
   // FCR >= 90% = 100pts, 85-90% = 80pts, 80-85% = 60pts, 75-80% = 40pts, <75% = 20pts
   let score: number;
   if (fcrRate >= 90) {
@@ -467,25 +467,25 @@ function buildEffectivenessResolutionDimension(
     score = 20;
   }
 
-  // Penalización adicional por abandono alto (>8%)
+  // Additional penalty for high abandonment (>8%)
   if (abandonmentRate > 8) {
     score = Math.max(0, score - Math.round((abandonmentRate - 8) * 2));
   }
 
-  // Summary enfocado en FCR Técnico
-  let summary = `FCR Técnico: ${fcrRate.toFixed(1)}% (benchmark: 85-90%). `;
-  summary += `Tasa de transferencia: ${transferRate.toFixed(1)}%. `;
+  // Summary focused on Technical FCR
+  let summary = `Technical FCR: ${fcrRate.toFixed(1)}% (benchmark: 85-90%). `;
+  summary += `Transfer rate: ${transferRate.toFixed(1)}%. `;
 
   if (fcrRate >= 90) {
-    summary += 'Excelente resolución en primer contacto.';
+    summary += 'Excellent first contact resolution.';
   } else if (fcrRate >= 85) {
-    summary += 'Resolución dentro del benchmark del sector.';
+    summary += 'Resolution within sector benchmark.';
   } else {
-    summary += 'Oportunidad de mejora reduciendo transferencias.';
+    summary += 'Opportunity to improve by reducing transfers.';
   }
 
   const kpi: Kpi = {
-    label: 'FCR Técnico',
+    label: 'Technical FCR',
     value: `${fcrRate.toFixed(0)}%`,
     change: `Transfer: ${transferRate.toFixed(0)}%`,
     changeType: fcrRate >= 85 ? 'positive' : fcrRate >= 80 ? 'neutral' : 'negative'
@@ -494,7 +494,7 @@ function buildEffectivenessResolutionDimension(
   const dimension: DimensionAnalysis = {
     id: 'effectiveness_resolution',
     name: 'effectiveness_resolution',
-    title: 'Efectividad & Resolución',
+    title: 'Effectiveness & Resolution',
     score,
     percentile: undefined,
     summary,
@@ -505,7 +505,7 @@ function buildEffectivenessResolutionDimension(
   return dimension;
 }
 
-// ==== Complejidad & Predictibilidad (v3.4 - basada en CV AHT per industry standards) ====
+// ==== Complexity & Predictability (v3.4 - based on CV AHT per industry standards) ====
 
 function buildComplexityPredictabilityDimension(
   raw: BackendRawResults
@@ -514,18 +514,18 @@ function buildComplexityPredictabilityDimension(
   if (!op) return undefined;
 
   // KPI principal: CV AHT (industry standard for predictability/WFM)
-  // CV AHT = (P90 - P50) / P50 como proxy de coeficiente de variación
+  // CV AHT = (P90 - P50) / P50 as a proxy for coefficient of variation
   const ahtP50 = safeNumber(op.aht_distribution?.p50, 0);
   const ahtP90 = safeNumber(op.aht_distribution?.p90, 0);
 
-  // Calcular CV AHT como (P90-P50)/P50 (proxy del coeficiente de variación real)
+  // Calculate CV AHT as (P90-P50)/P50 (proxy for the actual coefficient of variation)
   let cvAht = 0;
   if (ahtP50 > 0 && ahtP90 > 0) {
     cvAht = (ahtP90 - ahtP50) / ahtP50;
   }
   const cvAhtPercent = Math.round(cvAht * 100);
 
-  // Hold Time como métrica secundaria de complejidad
+  // Hold Time as a secondary metric for complexity
   const talkHoldAcw = op.talk_hold_acw_p50_by_skill;
   let avgHoldP50 = 0;
   if (Array.isArray(talkHoldAcw) && talkHoldAcw.length > 0) {
@@ -535,9 +535,9 @@ function buildComplexityPredictabilityDimension(
     }
   }
 
-  // Score basado en CV AHT (benchmark: <75% = excelente, <100% = aceptable)
+  // Score based on CV AHT (benchmark: <75% = excellent, <100% = acceptable)
   // CV <= 75% = 100pts (alta predictibilidad)
-  // CV 75-100% = 80pts (predictibilidad aceptable)
+  // CV 75-100% = 80pts (acceptable predictability)
   // CV 100-125% = 60pts (variabilidad moderada)
   // CV 125-150% = 40pts (alta variabilidad)
   // CV > 150% = 20pts (muy alta variabilidad)
@@ -558,16 +558,16 @@ function buildComplexityPredictabilityDimension(
   let summary = `CV AHT: ${cvAhtPercent}% (benchmark: <75%). `;
 
   if (cvAhtPercent <= 75) {
-    summary += 'Alta predictibilidad: tiempos de atención consistentes. Excelente para planificación WFM.';
+    summary += 'High predictability: consistent handling times. Excellent for WFM planning.';
   } else if (cvAhtPercent <= 100) {
-    summary += 'Predictibilidad aceptable: variabilidad moderada en tiempos de atención.';
+    summary += 'Acceptable predictability: moderate variability in handling times.';
   } else if (cvAhtPercent <= 125) {
-    summary += 'Variabilidad notable: dificulta la planificación de recursos. Considerar estandarización.';
+    summary += 'Notable variability: complicates resource planning. Consider standardization.';
   } else {
-    summary += 'Alta variabilidad: tiempos muy dispersos. Priorizar scripts guiados y estandarización.';
+    summary += 'High variability: very scattered times. Prioritize guided scripts and standardization.';
   }
 
-  // Añadir info de Hold P50 promedio si está disponible (proxy de complejidad)
+  // Add Hold P50 average info if available (complexity proxy)
   if (avgHoldP50 > 0) {
     summary += ` Hold Time P50: ${Math.round(avgHoldP50)}s.`;
   }
@@ -583,7 +583,7 @@ function buildComplexityPredictabilityDimension(
   const dimension: DimensionAnalysis = {
     id: 'complexity_predictability',
     name: 'complexity_predictability',
-    title: 'Complejidad & Predictibilidad',
+    title: 'Complexity & Predictability',
     score,
     percentile: undefined,
     summary,
@@ -594,7 +594,7 @@ function buildComplexityPredictabilityDimension(
   return dimension;
 }
 
-// ==== Satisfacción del Cliente (v3.1) ====
+// ==== Customer Satisfaction (v3.1) ====
 
 function buildSatisfactionDimension(
   raw: BackendRawResults
@@ -604,19 +604,19 @@ function buildSatisfactionDimension(
 
   const hasCSATData = Number.isFinite(csatGlobalRaw) && csatGlobalRaw > 0;
 
-  // Si no hay CSAT, mostrar dimensión con "No disponible"
+  // If no CSAT, show dimension with "Not available"
   const dimension: DimensionAnalysis = {
     id: 'customer_satisfaction',
     name: 'customer_satisfaction',
-    title: 'Satisfacción del Cliente',
-    score: hasCSATData ? Math.round((csatGlobalRaw / 5) * 100) : -1, // -1 indica N/A
+    title: 'Customer Satisfaction',
+    score: hasCSATData ? Math.round((csatGlobalRaw / 5) * 100) : -1, // -1 indicates N/A
     percentile: undefined,
     summary: hasCSATData
-      ? `CSAT global: ${csatGlobalRaw.toFixed(1)}/5. ${csatGlobalRaw >= 4.0 ? 'Nivel de satisfacción óptimo.' : csatGlobalRaw >= 3.5 ? 'Satisfacción aceptable, margen de mejora.' : 'Satisfacción baja, requiere atención urgente.'}`
-      : 'CSAT no disponible en el dataset. Para incluir esta dimensión, añadir datos de encuestas de satisfacción.',
+      ? `Global CSAT: ${csatGlobalRaw.toFixed(1)}/5. ${csatGlobalRaw >= 4.0 ? 'Optimal satisfaction level.' : csatGlobalRaw >= 3.5 ? 'Acceptable satisfaction, room for improvement.' : 'Low satisfaction, requires urgent attention.'}`
+      : 'CSAT not available in dataset. To include this dimension, add satisfaction survey data.',
     kpi: {
       label: 'CSAT',
-      value: hasCSATData ? `${csatGlobalRaw.toFixed(1)}/5` : 'No disponible',
+      value: hasCSATData ? `${csatGlobalRaw.toFixed(1)}/5` : 'Not available',
       changeType: hasCSATData
         ? (csatGlobalRaw >= 4.0 ? 'positive' : csatGlobalRaw >= 3.5 ? 'neutral' : 'negative')
         : 'neutral'
@@ -627,7 +627,7 @@ function buildSatisfactionDimension(
   return dimension;
 }
 
-// ==== Economía - Coste por Interacción (v3.1) ====
+// ==== Economy - Cost per Interaction (v3.1) ====
 
 function buildEconomyDimension(
   raw: BackendRawResults,
@@ -637,9 +637,9 @@ function buildEconomyDimension(
   const op = raw?.operational_performance;
   const totalAnnual = safeNumber(econ?.cost_breakdown?.total_annual, 0);
 
-  // Benchmark CPI aerolíneas (consistente con ExecutiveSummaryTab)
+  // Airline CPI benchmark (consistent with ExecutiveSummaryTab)
   // p25: 2.20, p50: 3.50, p75: 4.50, p90: 5.50
-  const CPI_BENCHMARK = 3.50;  // p50 aerolíneas
+  const CPI_BENCHMARK = 3.50;  // airline p50
 
   if (totalAnnual <= 0 || totalInteractions <= 0) {
     return undefined;
@@ -652,12 +652,12 @@ function buildEconomyDimension(
   // Calcular CPI usando cost_volume (non-abandoned) como denominador
   const cpi = costVolume > 0 ? totalAnnual / costVolume : totalAnnual / totalInteractions;
 
-  // Score basado en percentiles de aerolíneas (CPI invertido: menor = mejor)
-  // CPI <= 2.20 (p25) = 100pts (excelente, top 25%)
+  // Score based on airline percentiles (inverse CPI: lower = better)
+  // CPI <= 2.20 (p25) = 100pts (excellent, top 25%)
   // CPI 2.20-3.50 (p25-p50) = 80pts (bueno, top 50%)
-  // CPI 3.50-4.50 (p50-p75) = 60pts (promedio)
+  // CPI 3.50-4.50 (p50-p75) = 60pts (average)
   // CPI 4.50-5.50 (p75-p90) = 40pts (por debajo)
-  // CPI > 5.50 (>p90) = 20pts (crítico)
+  // CPI > 5.50 (>p90) = 20pts (critical)
   let score: number;
   if (cpi <= 2.20) {
     score = 100;
@@ -674,24 +674,24 @@ function buildEconomyDimension(
   const cpiDiff = cpi - CPI_BENCHMARK;
   const cpiStatus = cpiDiff <= 0 ? 'positive' : cpiDiff <= 0.5 ? 'neutral' : 'negative';
 
-  let summary = `Coste por interacción: €${cpi.toFixed(2)} vs benchmark €${CPI_BENCHMARK.toFixed(2)}. `;
+  let summary = `Cost per interaction: €${cpi.toFixed(2)} vs benchmark €${CPI_BENCHMARK.toFixed(2)}. `;
   if (cpi <= CPI_BENCHMARK) {
-    summary += 'Eficiencia de costes óptima, por debajo del benchmark del sector.';
+    summary += 'Optimal cost efficiency, below sector benchmark.';
   } else if (cpi <= 4.50) {
-    summary += 'Coste ligeramente por encima del benchmark, oportunidad de optimización.';
+    summary += 'Cost slightly above benchmark, optimization opportunity.';
   } else {
-    summary += 'Coste elevado respecto al sector. Priorizar iniciativas de eficiencia.';
+    summary += 'High cost relative to sector. Prioritize efficiency initiatives.';
   }
 
   const dimension: DimensionAnalysis = {
     id: 'economy_costs',
     name: 'economy_costs',
-    title: 'Economía & Costes',
+    title: 'Economy & Costs',
     score,
     percentile: undefined,
     summary,
     kpi: {
-      label: 'Coste por Interacción',
+      label: 'Cost per Interaction',
       value: `€${cpi.toFixed(2)}`,
       change: `vs benchmark €${CPI_BENCHMARK.toFixed(2)}`,
       changeType: cpiStatus as 'positive' | 'neutral' | 'negative'
@@ -702,7 +702,7 @@ function buildEconomyDimension(
   return dimension;
 }
 
-// ==== Agentic Readiness como dimensión (v3.0) ====
+// ==== Agentic Readiness as a dimension (v3.0) ====
 
 function buildAgenticReadinessDimension(
   raw: BackendRawResults,
@@ -720,7 +720,7 @@ function buildAgenticReadinessDimension(
   if (ar) {
     score0_10 = safeNumber(ar.final_score, 5);
   } else {
-    // Calcular aproximado desde métricas disponibles
+    // Calculate approximation from available metrics
     const ahtP50 = safeNumber(op?.aht_distribution?.p50, 0);
     const ahtP90 = safeNumber(op?.aht_distribution?.p90, 0);
     const ratio = ahtP50 > 0 ? ahtP90 / ahtP50 : 2;
@@ -779,7 +779,7 @@ function buildAgenticReadinessDimension(
 }
 
 
-// ==== Economía y costes (economy_costs) ====
+// ==== Economy and costs (economy_costs) ====
 
 function buildEconomicModel(raw: BackendRawResults): EconomicModelData {
   const econ = raw?.economy_costs;
@@ -814,17 +814,17 @@ function buildEconomicModel(raw: BackendRawResults): EconomicModelData {
   const savingsBreakdown = annualSavings
     ? [
         {
-          category: 'Ineficiencias operativas (AHT, escalaciones)',
+          category: 'Operational inefficiencies (AHT, escalations)',
           amount: Math.round(annualSavings * 0.5),
           percentage: 50,
         },
         {
-          category: 'Automatización de volumen repetitivo',
+          category: 'Automation of repetitive volume',
           amount: Math.round(annualSavings * 0.3),
           percentage: 30,
         },
         {
-          category: 'Otros beneficios (calidad, CX)',
+          category: 'Other benefits (quality, CX)',
           amount: Math.round(annualSavings * 0.2),
           percentage: 20,
         },
@@ -834,7 +834,7 @@ function buildEconomicModel(raw: BackendRawResults): EconomicModelData {
   const costBreakdown = currentAnnualCost
     ? [
         {
-          category: 'Coste laboral',
+          category: 'Labor cost',
           amount: laborAnnual,
           percentage: Math.round(
             (laborAnnual / currentAnnualCost) * 100
@@ -848,7 +848,7 @@ function buildEconomicModel(raw: BackendRawResults): EconomicModelData {
           ),
         },
         {
-          category: 'Tecnología',
+          category: 'Technology',
           amount: techAnnual,
           percentage: Math.round(
             (techAnnual / currentAnnualCost) * 100
@@ -870,7 +870,7 @@ function buildEconomicModel(raw: BackendRawResults): EconomicModelData {
   };
 }
 
-// buildEconomyDimension eliminado en v3.0 - economía integrada en otras dimensiones y modelo económico
+// buildEconomyDimension removed in v3.0 - economy integrated into other dimensions and economic model
 
 /**
  * Transforma el JSON del backend (results) al AnalysisData
@@ -914,7 +914,7 @@ export function mapBackendResultsToAnalysisData(
     Math.min(100, Math.round(arScore * 10))
   );
 
-  // v3.3: 7 dimensiones (Complejidad recuperada con métrica Hold Time >60s)
+  // v3.3: 7 dimensions (Complexity recovered with Hold Time metric >60s)
   const { dimension: volumetryDimension, extraKpis } =
     buildVolumetryDimension(raw);
   const operationalEfficiencyDimension = buildOperationalEfficiencyDimension(raw);
@@ -946,7 +946,7 @@ export function mapBackendResultsToAnalysisData(
 
   const csatAvg = computeCsatAverage(cs);
 
-  // CSAT global (opcional)
+  // Global CSAT (opcional)
   const csatGlobalRaw = safeNumber(cs?.csat_global, NaN);
   const csatGlobal =
     Number.isFinite(csatGlobalRaw) && csatGlobalRaw > 0
@@ -954,30 +954,30 @@ export function mapBackendResultsToAnalysisData(
       : undefined;
 
 
-    // KPIs de resumen (los 4 primeros son los que se ven en "Métricas de Contacto")
+    // Summary KPIs (the first 4 are shown in "Contact Metrics")
   const summaryKpis: Kpi[] = [];
 
-  // 1) Interacciones Totales (volumen backend)
+  // 1) Total Interactions (backend volume)
   summaryKpis.push({
-    label: 'Interacciones Totales',
+    label: 'Total Interactions',
     value:
       totalVolume > 0
         ? totalVolume.toLocaleString('es-ES')
         : 'N/D',
   });
 
-  // 2) AHT Promedio (P50 de distribución de AHT)
+  // 2) Average AHT (P50 of AHT distribution)
   const ahtP50 = safeNumber(op?.aht_distribution?.p50, 0);
   summaryKpis.push({
-    label: 'AHT Promedio',
+    label: 'Average AHT',
     value: ahtP50
       ? `${Math.round(ahtP50)}s`
       : 'N/D',
   });
 
-  // 3) Tasa FCR
+  // 3) FCR Rate
   summaryKpis.push({
-    label: 'Tasa FCR',
+    label: 'FCR Rate',
     value:
       fcrPct !== undefined
         ? `${Math.round(fcrPct)}%`
@@ -993,18 +993,18 @@ export function mapBackendResultsToAnalysisData(
         : 'N/D',
   });
 
-  // --- KPIs adicionales, usados en otras secciones ---
+  // --- Additional KPIs, used in other sections ---
 
   if (numChannels > 0) {
     summaryKpis.push({
-      label: 'Canales analizados',
+      label: 'Channels analyzed',
       value: String(numChannels),
     });
   }
 
   if (numSkills > 0) {
     summaryKpis.push({
-      label: 'Skills analizadas',
+      label: 'Skills analyzed',
       value: String(numSkills),
     });
   }
@@ -1014,7 +1014,7 @@ export function mapBackendResultsToAnalysisData(
     value: `${arScore.toFixed(1)}/10`,
   });
 
-  // KPIs de economía (backend)
+  // Economy KPIs (backend)
   const econ = raw?.economy_costs;
   const totalAnnual = safeNumber(
     econ?.cost_breakdown?.total_annual,
@@ -1027,13 +1027,13 @@ export function mapBackendResultsToAnalysisData(
 
   if (totalAnnual) {
     summaryKpis.push({
-      label: 'Coste anual actual (backend)',
+      label: 'Current annual cost (backend)',
       value: `€${totalAnnual.toFixed(0)}`,
     });
   }
   if (annualSavings) {
     summaryKpis.push({
-      label: 'Ahorro potencial anual (backend)',
+      label: 'Annual potential savings (backend)',
       value: `€${annualSavings.toFixed(0)}`,
     });
   }
@@ -1043,22 +1043,22 @@ export function mapBackendResultsToAnalysisData(
   const economicModel = buildEconomicModel(raw);
   const benchmarkData = buildBenchmarkData(raw);
 
-  // Generar findings y recommendations basados en volumetría
+  // Generate findings and recommendations based on volumetry
   const findings: Finding[] = [];
   const recommendations: Recommendation[] = [];
 
-  // Extraer offHoursPct de la dimensión de volumetría
+  // Extract offHoursPct from the volumetry dimension
   const offHoursPct = volumetryDimension?.distribution_data?.off_hours_pct ?? 0;
-  const offHoursPctValue = offHoursPct * 100; // Convertir de 0-1 a 0-100
+  const offHoursPctValue = offHoursPct * 100; // Convert from 0-1 a 0-100
 
   if (offHoursPctValue > 20) {
     const offHoursVolume = Math.round(totalVolume * offHoursPctValue / 100);
     findings.push({
       type: offHoursPctValue > 30 ? 'critical' : 'warning',
-      title: 'Alto Volumen Fuera de Horario',
-      text: `${offHoursPctValue.toFixed(0)}% de interacciones fuera de horario (8-19h)`,
+      title: 'High Off-Hours Volume',
+      text: `${offHoursPctValue.toFixed(0)}% of off-hours interactions (8-19h)`,
       dimensionId: 'volumetry_distribution',
-      description: `${offHoursVolume.toLocaleString()} interacciones (${offHoursPctValue.toFixed(1)}%) ocurren fuera de horario laboral. Oportunidad ideal para implementar agentes virtuales 24/7.`,
+      description: `${offHoursVolume.toLocaleString()} interacciones (${offHoursPctValue.toFixed(1)}%) ocurren outside business hours. Ideal opportunity to implement 24/7 virtual agents.`,
       impact: offHoursPctValue > 30 ? 'high' : 'medium'
     });
 
@@ -1066,12 +1066,12 @@ export function mapBackendResultsToAnalysisData(
     const estimatedSavings = Math.round(offHoursVolume * estimatedContainment / 100);
     recommendations.push({
       priority: 'high',
-      title: 'Implementar Agente Virtual 24/7',
-      text: `Desplegar agente virtual para atender ${offHoursPctValue.toFixed(0)}% de interacciones fuera de horario`,
-      description: `${offHoursVolume.toLocaleString()} interacciones ocurren fuera de horario laboral (19:00-08:00). Un agente virtual puede resolver ~${estimatedContainment}% de estas consultas automáticamente.`,
+      title: 'Implement 24/7 Virtual Agent',
+      text: `Deploy virtual agent to handle ${offHoursPctValue.toFixed(0)}% of off-hours interactions`,
+      description: `${offHoursVolume.toLocaleString()} interactions occur outside business hours (19:00-08:00). A virtual agent can resolve ~${estimatedContainment}% of these queries automatically.`,
       dimensionId: 'volumetry_distribution',
-      impact: `Potencial de contención: ${estimatedSavings.toLocaleString()} interacciones/período`,
-      timeline: '1-3 meses'
+      impact: `Containment potential: ${estimatedSavings.toLocaleString()} interactions/period`,
+      timeline: '1-3 months'
     });
   }
 
@@ -1080,7 +1080,7 @@ export function mapBackendResultsToAnalysisData(
     overallHealthScore,
     summaryKpis: mergedKpis,
     dimensions,
-    heatmapData: [], // el heatmap por skill lo seguimos generando en el front
+    heatmapData: [], // skill heatmap still generated on frontend
     findings,
     recommendations,
     opportunities: [],
@@ -1153,8 +1153,8 @@ export function buildHeatmapFromBackend(
   const abandonmentRateBackend = safeNumber(op?.abandonment_rate, 0);
 
   // ========================================================================
-  // NUEVO: Métricas REALES por skill (transfer, abandonment, FCR)
-  // Esto elimina la estimación de transfer rate basada en CV y hold time
+  // NEW: REAL metrics per skill (transfer, abandonment, FCR)
+  // This eliminates the transfer rate estimation based on CV and hold time
   // ========================================================================
   const metricsBySkillRaw = Array.isArray(op?.metrics_by_skill)
     ? op.metrics_by_skill
@@ -1166,9 +1166,9 @@ export function buildHeatmapFromBackend(
     abandonment_rate: number;
     fcr_tecnico: number;
     fcr_real: number;
-    aht_mean: number;  // AHT promedio del backend (solo VALID - consistente con fresh path)
-    aht_total: number;  // AHT total (ALL rows incluyendo NOISE/ZOMBIE/ABANDON) - solo informativo
-    hold_time_mean: number;  // Hold time promedio (consistente con fresh path - MEAN, no P50)
+    aht_mean: number;  // Average AHT del backend (only VALID - consistent with fresh path)
+    aht_total: number;  // Total AHT (ALL rows incluyendo NOISE/ZOMBIE/ABANDON) - informational only
+    hold_time_mean: number;  // Average Hold time (consistent with fresh path - MEAN, not P50)
   }>();
 
   for (const m of metricsBySkillRaw) {
@@ -1178,9 +1178,9 @@ export function buildHeatmapFromBackend(
         abandonment_rate: safeNumber(m.abandonment_rate, NaN),
         fcr_tecnico: safeNumber(m.fcr_tecnico, NaN),
         fcr_real: safeNumber(m.fcr_real, NaN),
-        aht_mean: safeNumber(m.aht_mean, NaN),  // AHT promedio (solo VALID)
-        aht_total: safeNumber(m.aht_total, NaN),  // AHT total (ALL rows)
-        hold_time_mean: safeNumber(m.hold_time_mean, NaN),  // Hold time promedio (MEAN)
+        aht_mean: safeNumber(m.aht_mean, NaN),  // Average AHT (solo VALID)
+        aht_total: safeNumber(m.aht_total, NaN),  // Total AHT (ALL rows)
+        hold_time_mean: safeNumber(m.hold_time_mean, NaN),  // Average Hold time (MEAN)
       });
     }
   }
@@ -1251,7 +1251,7 @@ export function buildHeatmapFromBackend(
 
   if (!skillLabels.length) return [];
 
-  // Para normalizar la repetitividad según volumen
+  // To normalize repetitiveness according to volume
   const volumesForNorm = skillVolumes.filter((v) => v > 0);
   const minVol =
     volumesForNorm.length > 0
@@ -1268,13 +1268,13 @@ export function buildHeatmapFromBackend(
     const skill = skillLabels[i];
     const volume = safeNumber(skillVolumes[i], 0);
 
-    // Buscar P50s por nombre de skill (no por índice)
+    // Search for P50s by skill name (not by index)
     const talkHold = talkHoldAcwMap.get(skill);
     const talk_p50 = talkHold?.talk_p50 ?? 0;
     const hold_p50 = talkHold?.hold_p50 ?? 0;
     const acw_p50 = talkHold?.acw_p50 ?? 0;
 
-    // Buscar métricas REALES del backend (metrics_by_skill)
+    // Search for REAL metrics from backend (metrics_by_skill)
     const realSkillMetrics = metricsBySkillMap.get(skill);
 
     // AHT: Use ONLY aht_mean from backend metrics_by_skill
@@ -1284,7 +1284,7 @@ export function buildHeatmapFromBackend(
       : 0;
 
     // AHT Total: AHT calculado con TODAS las filas (incluye NOISE/ZOMBIE/ABANDON)
-    // Solo para información/comparación - no se usa en cálculos
+    // Only for information/comparison - not used in calculations
     const aht_total = (realSkillMetrics && Number.isFinite(realSkillMetrics.aht_total) && realSkillMetrics.aht_total > 0)
       ? realSkillMetrics.aht_total
       : aht_mean;  // fallback to aht_mean if not available
@@ -1299,7 +1299,7 @@ export function buildHeatmapFromBackend(
       annual_volume * aht_mean * COST_PER_SECOND
     );
 
-    // Buscar inefficiency data por nombre de skill (no por índice)
+    // Search for inefficiency data by skill name (not by index)
     const ineff = ineffBySkillMap.get(skill);
     const aht_p50_backend = ineff?.aht_p50 ?? aht_mean;
     const aht_p90_backend = ineff?.aht_p90 ?? aht_mean;
@@ -1311,10 +1311,10 @@ export function buildHeatmapFromBackend(
         (aht_p90_backend - aht_p50_backend) / aht_p50_backend;
     }
 
-    // Dimensiones agentic similares a las que tenías en generateHeatmapData,
+    // Agentic dimensions similar to those you had in generateHeatmapData,
     // pero usando valores reales en lugar de aleatorios.
 
-    // 1) Predictibilidad (menor CV => mayor puntuación)
+    // 1) Predictability (lower CV => higher score)
     const predictability_score = Math.max(
       0,
       Math.min(
@@ -1324,8 +1324,8 @@ export function buildHeatmapFromBackend(
     );
 
     // 2) Transfer rate POR SKILL
-    // PRIORIDAD 1: Usar métricas REALES del backend (metrics_by_skill)
-    // PRIORIDAD 2: Fallback a estimación basada en CV y hold time
+    // PRIORITY 1: Use REAL metrics from backend (metrics_by_skill)
+    // PRIORITY 2: Fallback to estimation based on CV and hold time
 
     let skillTransferRate: number;
     let skillAbandonmentRate: number;
@@ -1333,7 +1333,7 @@ export function buildHeatmapFromBackend(
     let skillFcrReal: number;
 
     if (realSkillMetrics && Number.isFinite(realSkillMetrics.transfer_rate)) {
-      // Usar métricas REALES del backend
+      // Use REAL metrics from backend
       skillTransferRate = realSkillMetrics.transfer_rate;
       skillAbandonmentRate = Number.isFinite(realSkillMetrics.abandonment_rate)
         ? realSkillMetrics.abandonment_rate
@@ -1347,14 +1347,14 @@ export function buildHeatmapFromBackend(
     } else {
       // NO usar estimación - usar valores globales del backend directamente
       // Esto asegura consistencia con el fresh path que usa valores directos del CSV
-      skillTransferRate = globalEscalation;  // Usar tasa global, sin estimación
+      skillTransferRate = globalEscalation;  // Use global rate, no estimation
       skillAbandonmentRate = abandonmentRateBackend;
       skillFcrTecnico = 100 - skillTransferRate;
       skillFcrReal = globalFcrPct;
       console.warn(`⚠️ No metrics_by_skill for skill ${skill} - using global rates`);
     }
 
-    // Complejidad inversa basada en transfer rate del skill
+    // Inverse complexity based on skill transfer rate
     const complexity_inverse_score = Math.max(
       0,
       Math.min(
@@ -1446,10 +1446,10 @@ export function buildHeatmapFromBackend(
       volume,
       cost_volume: costVolume,
       aht_seconds: aht_mean,
-      aht_total: aht_total,  // AHT con TODAS las filas (solo informativo)
+      aht_total: aht_total,  // AHT con TODAS las filas (informational only)
       metrics: {
         fcr: Math.round(skillFcrReal),        // FCR Real (sin transfer Y sin recontacto 7d)
-        fcr_tecnico: Math.round(skillFcrTecnico),  // FCR Técnico (comparable con benchmarks)
+        fcr_tecnico: Math.round(skillFcrTecnico),  // Technical FCR (comparable con benchmarks)
         aht: ahtMetric,
         csat: csatMetric0_100,
         hold_time: holdMetric,
@@ -1457,12 +1457,12 @@ export function buildHeatmapFromBackend(
         abandonment_rate: Math.round(skillAbandonmentRate),
       },
       annual_cost,
-      cpi: skillCpi,  // CPI real del backend (si disponible)
+      cpi: skillCpi,  // Real CPI from backend (if available)
       variability: {
         cv_aht: Math.round(cv_aht * 100), // %
         cv_talk_time: 0,
         cv_hold_time: 0,
-        transfer_rate: skillTransferRate,  // Transfer rate REAL o estimado
+        transfer_rate: skillTransferRate,  // REAL or estimated transfer rate
       },
       automation_readiness,
       dimensions: {
@@ -1491,19 +1491,19 @@ function buildBenchmarkData(raw: BackendRawResults): AnalysisData['benchmarkData
 
   const benchmarkData: AnalysisData['benchmarkData'] = [];
 
-  // Benchmarks hardcoded para sector aéreo
+  // Hardcoded benchmarks for airline sector
   const AIRLINE_BENCHMARKS = {
-    aht_p50: 380,      // segundos
+    aht_p50: 380,      // seconds
     fcr: 70,           // % (rango 68-72%)
     abandonment: 5,    // % (rango 5-8%)
     ratio_p90_p50: 2.0, // ratio saludable
     cpi: 5.25          // € (rango €4.50-€6.00)
   };
 
-  // 1. AHT Promedio (benchmark sector aéreo: 380s)
+  // 1. AHT Promedio (benchmark airline sector: 380s)
   const ahtP50 = safeNumber(op?.aht_distribution?.p50, 0);
   if (ahtP50 > 0) {
-    // Percentil: menor AHT = mejor. Si AHT <= benchmark = P75+
+    // Percentile: lower AHT = better. If AHT <= benchmark = P75+
     const ahtPercentile = ahtP50 <= AIRLINE_BENCHMARKS.aht_p50
       ? Math.min(90, 75 + Math.round((AIRLINE_BENCHMARKS.aht_p50 - ahtP50) / 10))
       : Math.max(10, 75 - Math.round((ahtP50 - AIRLINE_BENCHMARKS.aht_p50) / 5));
@@ -1521,15 +1521,15 @@ function buildBenchmarkData(raw: BackendRawResults): AnalysisData['benchmarkData
     });
   }
 
-  // 2. Tasa FCR (benchmark sector aéreo: 70%)
+  // 2. FCR Rate (benchmark airline sector: 70%)
   const fcrRate = safeNumber(op?.fcr_rate, NaN);
   if (Number.isFinite(fcrRate) && fcrRate >= 0) {
-    // Percentil: mayor FCR = mejor
+    // Percentile: higher FCR = better
     const fcrPercentile = fcrRate >= AIRLINE_BENCHMARKS.fcr
       ? Math.min(90, 50 + Math.round((fcrRate - AIRLINE_BENCHMARKS.fcr) * 2))
       : Math.max(10, 50 - Math.round((AIRLINE_BENCHMARKS.fcr - fcrRate) * 2));
     benchmarkData.push({
-      kpi: 'Tasa FCR',
+      kpi: 'FCR Rate',
       userValue: fcrRate / 100,
       userDisplay: `${Math.round(fcrRate)}%`,
       industryValue: AIRLINE_BENCHMARKS.fcr / 100,
@@ -1560,15 +1560,15 @@ function buildBenchmarkData(raw: BackendRawResults): AnalysisData['benchmarkData
     });
   }
 
-  // 4. Tasa de Abandono (benchmark sector aéreo: 5%)
+  // 4. Abandonment Rate (benchmark airline sector: 5%)
   const abandonRate = safeNumber(op?.abandonment_rate, NaN);
   if (Number.isFinite(abandonRate) && abandonRate >= 0) {
-    // Percentil: menor abandono = mejor
+    // Percentile: lower abandonment = better
     const abandonPercentile = abandonRate <= AIRLINE_BENCHMARKS.abandonment
       ? Math.min(90, 75 + Math.round((AIRLINE_BENCHMARKS.abandonment - abandonRate) * 5))
       : Math.max(10, 75 - Math.round((abandonRate - AIRLINE_BENCHMARKS.abandonment) * 5));
     benchmarkData.push({
-      kpi: 'Tasa de Abandono',
+      kpi: 'Abandonment Rate',
       userValue: abandonRate / 100,
       userDisplay: `${abandonRate.toFixed(1)}%`,
       industryValue: AIRLINE_BENCHMARKS.abandonment / 100,
@@ -1581,11 +1581,11 @@ function buildBenchmarkData(raw: BackendRawResults): AnalysisData['benchmarkData
     });
   }
 
-  // 5. Ratio P90/P50 (benchmark sector aéreo: <2.0)
+  // 5. Ratio P90/P50 (benchmark airline sector: <2.0)
   const ahtP90 = safeNumber(op?.aht_distribution?.p90, 0);
   const ratio = ahtP50 > 0 && ahtP90 > 0 ? ahtP90 / ahtP50 : 0;
   if (ratio > 0) {
-    // Percentil: menor ratio = mejor
+    // Percentile: lower ratio = better
     const ratioPercentile = ratio <= AIRLINE_BENCHMARKS.ratio_p90_p50
       ? Math.min(90, 75 + Math.round((AIRLINE_BENCHMARKS.ratio_p90_p50 - ratio) * 30))
       : Math.max(10, 75 - Math.round((ratio - AIRLINE_BENCHMARKS.ratio_p90_p50) * 30));
@@ -1603,13 +1603,13 @@ function buildBenchmarkData(raw: BackendRawResults): AnalysisData['benchmarkData
     });
   }
 
-  // 6. Tasa de Transferencia/Escalación
+  // 6. Transfer/Escalation Rate
   const escalationRate = safeNumber(op?.escalation_rate, NaN);
   if (Number.isFinite(escalationRate) && escalationRate >= 0) {
-    // Menor escalación = mejor percentil
+    // Menor escalación = better percentil
     const escalationPercentile = Math.max(10, Math.min(90, Math.round(100 - escalationRate * 5)));
     benchmarkData.push({
-      kpi: 'Tasa de Transferencia',
+      kpi: 'Transfer Rate',
       userValue: escalationRate / 100,
       userDisplay: `${escalationRate.toFixed(1)}%`,
       industryValue: 0.15,
@@ -1622,7 +1622,7 @@ function buildBenchmarkData(raw: BackendRawResults): AnalysisData['benchmarkData
     });
   }
 
-  // 7. CPI - Coste por Interacción (benchmark sector aéreo: €4.50-€6.00)
+  // 7. CPI - Cost per Interaction (benchmark airline sector: €4.50-€6.00)
   const econ = raw?.economy_costs;
   const totalAnnualCost = safeNumber(econ?.cost_breakdown?.total_annual, 0);
   const volumetry = raw?.volumetry;
@@ -1634,7 +1634,7 @@ function buildBenchmarkData(raw: BackendRawResults): AnalysisData['benchmarkData
 
   if (totalAnnualCost > 0 && totalInteractions > 0) {
     const cpi = totalAnnualCost / totalInteractions;
-    // Menor CPI = mejor. Si CPI <= 4.50 = excelente (P90+), si CPI >= 6.00 = malo (P25-)
+    // Lower CPI = better. If CPI <= 4.50 = excellent (P90+), if CPI >= 6.00 = poor (P25-)
     let cpiPercentile: number;
     if (cpi <= 4.50) {
       cpiPercentile = Math.min(95, 90 + Math.round((4.50 - cpi) * 10));
@@ -1647,7 +1647,7 @@ function buildBenchmarkData(raw: BackendRawResults): AnalysisData['benchmarkData
     }
 
     benchmarkData.push({
-      kpi: 'Coste por Interacción (CPI)',
+      kpi: 'Cost per Interaction (CPI)',
       userValue: cpi,
       userDisplay: `€${cpi.toFixed(2)}`,
       industryValue: AIRLINE_BENCHMARKS.cpi,

frontend/utils/dataTransformation.ts

@@ -1,11 +1,11 @@
 // utils/dataTransformation.ts
-// Pipeline de transformación de datos raw a métricas procesadas
+// Raw data to processed metrics transformation pipeline
 
 import type { RawInteraction } from '../types';
 
 /**
- * Paso 1: Limpieza de Ruido
- * Elimina interacciones con duration < 10 segundos (falsos contactos o errores de sistema)
+ * Step 1: Noise Cleanup
+ * Removes interactions with duration < 10 seconds (false contacts or system errors)
  */
 export function cleanNoiseFromData(interactions: RawInteraction[]): RawInteraction[] {
   const MIN_DURATION_SECONDS = 10;
@@ -22,30 +22,30 @@ export function cleanNoiseFromData(interactions: RawInteraction[]): RawInteracti
   const removedCount = interactions.length - cleaned.length;
   const removedPercentage = ((removedCount / interactions.length) * 100).toFixed(1);
   
-  console.log(`🧹 Limpieza de Ruido: ${removedCount} interacciones eliminadas (${removedPercentage}% del total)`);
-  console.log(`✅ Interacciones limpias: ${cleaned.length}`);
+  console.log(`🧹 Noise Cleanup: ${removedCount} interactions removed (${removedPercentage}% of total)`);
+  console.log(`✅ Clean interactions: ${cleaned.length}`);
   
   return cleaned;
 }
 
 /**
- * Métricas base calculadas por skill
+ * Base metrics calculated by skill
  */
 export interface SkillBaseMetrics {
   skill: string;
-  volume: number;                  // Número de interacciones
-  aht_mean: number;                // AHT promedio (segundos)
-  aht_std: number;                 // Desviación estándar del AHT
-  transfer_rate: number;           // Tasa de transferencia (0-100)
-  total_cost: number;              // Coste total (€)
+  volume: number;                  // Number of interactions
+  aht_mean: number;                // Average AHT (seconds)
+  aht_std: number;                 // AHT standard deviation
+  transfer_rate: number;           // Transfer rate (0-100)
+  total_cost: number;              // Total cost (€)
   
-  // Datos auxiliares para cálculos posteriores
-  aht_values: number[];            // Array de todos los AHT para percentiles
+  // Auxiliary data for subsequent calculations
+  aht_values: number[];            // Array of all AHT values for percentiles
 }
 
 /**
- * Paso 2: Calcular Métricas Base por Skill
- * Agrupa por skill y calcula volumen, AHT promedio, desviación estándar, tasa de transferencia y coste
+ * Step 2: Calculate Base Metrics by Skill
+ * Groups by skill and calculates volume, average AHT, standard deviation, transfer rate and cost
  */
 export function calculateSkillBaseMetrics(
   interactions: RawInteraction[],
@@ -53,7 +53,7 @@ export function calculateSkillBaseMetrics(
 ): SkillBaseMetrics[] {
   const COST_PER_SECOND = costPerHour / 3600;
   
-  // Agrupar por skill
+  // Group by skill
   const skillGroups = new Map<string, RawInteraction[]>();
   
   interactions.forEach(interaction => {
@@ -64,31 +64,31 @@ export function calculateSkillBaseMetrics(
     skillGroups.get(skill)!.push(interaction);
   });
   
-  // Calcular métricas por skill
+  // Calculate metrics per skill
   const metrics: SkillBaseMetrics[] = [];
   
   skillGroups.forEach((skillInteractions, skill) => {
     const volume = skillInteractions.length;
     
-    // Calcular AHT para cada interacción
+    // Calculate AHT for each interaction
     const ahtValues = skillInteractions.map(i => 
       i.duration_talk + i.hold_time + i.wrap_up_time
     );
     
-    // AHT promedio
+    // Average AHT
     const ahtMean = ahtValues.reduce((sum, val) => sum + val, 0) / volume;
     
-    // Desviación estándar del AHT
+    // AHT standard deviation
     const variance = ahtValues.reduce((sum, val) => 
       sum + Math.pow(val - ahtMean, 2), 0
     ) / volume;
     const ahtStd = Math.sqrt(variance);
     
-    // Tasa de transferencia
+    // Transfer rate
     const transferCount = skillInteractions.filter(i => i.transfer_flag).length;
     const transferRate = (transferCount / volume) * 100;
     
-    // Coste total
+    // Total cost
     const totalCost = ahtValues.reduce((sum, aht) => 
       sum + (aht * COST_PER_SECOND), 0
     );
@@ -104,82 +104,82 @@ export function calculateSkillBaseMetrics(
     });
   });
   
-  // Ordenar por volumen descendente
+  // Sort by descending volume
   metrics.sort((a, b) => b.volume - a.volume);
   
-  console.log(`📊 Métricas Base calculadas para ${metrics.length} skills`);
+  console.log(`📊 Base Metrics calculated for ${metrics.length} skills`);
   
   return metrics;
 }
 
 /**
- * Dimensiones transformadas para Agentic Readiness Score
+ * Transformed dimensions for Agentic Readiness Score
  */
 export interface SkillDimensions {
   skill: string;
   volume: number;
   
-  // Dimensión 1: Predictibilidad (0-10)
+  // Dimension 1: Predictability (0-10)
   predictability_score: number;
-  predictability_cv: number;       // Coeficiente de Variación (para referencia)
+  predictability_cv: number;       // Coefficient of Variation (for reference)
   
-  // Dimensión 2: Complejidad Inversa (0-10)
+  // Dimension 2: Inverse Complexity (0-10)
   complexity_inverse_score: number;
-  complexity_transfer_rate: number; // Tasa de transferencia (para referencia)
+  complexity_transfer_rate: number; // Transfer rate (for reference)
   
-  // Dimensión 3: Repetitividad/Impacto (0-10)
+  // Dimension 3: Repetitiveness/Impact (0-10)
   repetitivity_score: number;
   
-  // Datos auxiliares
+  // Auxiliary data
   aht_mean: number;
   total_cost: number;
 }
 
 /**
- * Paso 3: Transformar Métricas Base a Dimensiones
- * Aplica las fórmulas de normalización para obtener scores 0-10
+ * Step 3: Transform Base Metrics to Dimensions
+ * Applies normalization formulas to obtain 0-10 scores
  */
 export function transformToDimensions(
   baseMetrics: SkillBaseMetrics[]
 ): SkillDimensions[] {
   return baseMetrics.map(metric => {
-    // Dimensión 1: Predictibilidad (Proxy: Variabilidad del AHT)
-    // CV = desviación estándar / media
+    // Dimension 1: Predictability (Proxy: AHT Variability)
+    // CV = standard deviation / mean
     const cv = metric.aht_std / metric.aht_mean;
     
-    // Normalización: CV <= 0.3 → 10, CV >= 1.5 → 0
-    // Fórmula: MAX(0, MIN(10, 10 - ((CV - 0.3) / 1.2 * 10)))
+    // Normalization: CV <= 0.3 → 10, CV >= 1.5 → 0
+    // Formula: MAX(0, MIN(10, 10 - ((CV - 0.3) / 1.2 * 10)))
     const predictabilityScore = Math.max(0, Math.min(10, 
       10 - ((cv - 0.3) / 1.2 * 10)
     ));
     
-    // Dimensión 2: Complejidad Inversa (Proxy: Tasa de Transferencia)
-    // T = tasa de transferencia (%)
+    // Dimension 2: Inverse Complexity (Proxy: Transfer Rate)
+    // T = transfer rate (%)
     const transferRate = metric.transfer_rate;
     
-    // Normalización: T <= 5% → 10, T >= 30% → 0
-    // Fórmula: MAX(0, MIN(10, 10 - ((T - 0.05) / 0.25 * 10)))
+    // Normalization: T <= 5% → 10, T >= 30% → 0
+    // Formula: MAX(0, MIN(10, 10 - ((T - 0.05) / 0.25 * 10)))
     const complexityInverseScore = Math.max(0, Math.min(10,
       10 - ((transferRate / 100 - 0.05) / 0.25 * 10)
     ));
     
-    // Dimensión 3: Repetitividad/Impacto (Proxy: Volumen)
-    // Normalización fija: > 5,000 llamadas/mes = 10, < 100 = 0
+    // Dimension 3: Repetitiveness/Impact (Proxy: Volume)
+    // Fixed normalization: > 5,000 calls/month = 10, < 100 = 0
     let repetitivityScore: number;
     if (metric.volume >= 5000) {
       repetitivityScore = 10;
     } else if (metric.volume <= 100) {
       repetitivityScore = 0;
     } else {
-      // Interpolación lineal entre 100 y 5000
+      // Linear interpolation between 100 and 5000
       repetitivityScore = ((metric.volume - 100) / (5000 - 100)) * 10;
     }
     
     return {
       skill: metric.skill,
       volume: metric.volume,
-      predictability_score: Math.round(predictabilityScore * 10) / 10, // 1 decimal
-      predictability_cv: Math.round(cv * 100) / 100, // 2 decimales
+      predictability_score: Math.round(predictabilityScore * 10) / 10, // 1 decimal place
+      predictability_cv: Math.round(cv * 100) / 100, // 2 decimal places
       complexity_inverse_score: Math.round(complexityInverseScore * 10) / 10,
       complexity_transfer_rate: Math.round(transferRate * 10) / 10,
       repetitivity_score: Math.round(repetitivityScore * 10) / 10,
@@ -190,7 +190,7 @@ export function transformToDimensions(
 }
 
 /**
- * Resultado final con Agentic Readiness Score
+ * Final result with Agentic Readiness Score
  */
 export interface SkillAgenticReadiness extends SkillDimensions {
   agentic_readiness_score: number;  // 0-10
@@ -199,28 +199,28 @@ export interface SkillAgenticReadiness extends SkillDimensions {
 }
 
 /**
- * Paso 4: Calcular Agentic Readiness Score
- * Promedio ponderado de las 3 dimensiones
+ * Step 4: Calculate Agentic Readiness Score
+ * Weighted average of the 3 dimensions
  */
 export function calculateAgenticReadinessScore(
   dimensions: SkillDimensions[],
   weights?: { predictability: number; complexity: number; repetitivity: number }
 ): SkillAgenticReadiness[] {
-  // Pesos por defecto (ajustables)
+  // Default weights (adjustable)
   const w = weights || {
-    predictability: 0.40,   // 40% - Más importante
+    predictability: 0.40,   // 40% - Most important
     complexity: 0.35,       // 35%
     repetitivity: 0.25      // 25%
   };
   
   return dimensions.map(dim => {
-    // Promedio ponderado
+    // Weighted average
     const score = 
       dim.predictability_score * w.predictability +
       dim.complexity_inverse_score * w.complexity +
       dim.repetitivity_score * w.repetitivity;
     
-    // Categorizar
+    // Categorize
     let category: 'automate_now' | 'assist_copilot' | 'optimize_first';
     let label: string;
     
@@ -245,29 +245,29 @@ export function calculateAgenticReadinessScore(
 }
 
 /**
- * Pipeline completo: Raw Data → Agentic Readiness Score
+ * Complete pipeline: Raw Data → Agentic Readiness Score
  */
 export function transformRawDataToAgenticReadiness(
   rawInteractions: RawInteraction[],
   costPerHour: number,
   weights?: { predictability: number; complexity: number; repetitivity: number }
 ): SkillAgenticReadiness[] {
-  console.log(`🚀 Iniciando pipeline de transformación con ${rawInteractions.length} interacciones...`);
+  console.log(`🚀 Starting transformation pipeline with ${rawInteractions.length} interactions...`);
   
-  // Paso 1: Limpieza de ruido
+  // Step 1: Noise cleanup
   const cleanedData = cleanNoiseFromData(rawInteractions);
   
-  // Paso 2: Calcular métricas base
+  // Step 2: Calculate base metrics
   const baseMetrics = calculateSkillBaseMetrics(cleanedData, costPerHour);
   
-  // Paso 3: Transformar a dimensiones
+  // Step 3: Transform to dimensions
   const dimensions = transformToDimensions(baseMetrics);
   
-  // Paso 4: Calcular Agentic Readiness Score
+  // Step 4: Calculate Agentic Readiness Score
   const agenticReadiness = calculateAgenticReadinessScore(dimensions, weights);
   
-  console.log(`✅ Pipeline completado: ${agenticReadiness.length} skills procesados`);
-  console.log(`📈 Distribución:`);
+  console.log(`✅ Pipeline completed: ${agenticReadiness.length} skills processed`);
+  console.log(`📈 Distribution:`);
   const automateCount = agenticReadiness.filter(s => s.readiness_category === 'automate_now').length;
   const assistCount = agenticReadiness.filter(s => s.readiness_category === 'assist_copilot').length;
   const optimizeCount = agenticReadiness.filter(s => s.readiness_category === 'optimize_first').length;
@@ -279,7 +279,7 @@ export function transformRawDataToAgenticReadiness(
 }
 
 /**
- * Utilidad: Generar resumen de estadísticas
+ * Utility: Generate statistics summary
  */
 export function generateTransformationSummary(
   originalCount: number,
@@ -294,17 +294,17 @@ export function generateTransformationSummary(
   const assistCount = agenticReadiness.filter(s => s.readiness_category === 'assist_copilot').length;
   const optimizeCount = agenticReadiness.filter(s => s.readiness_category === 'optimize_first').length;
 
-  // Validar que skillsCount no sea 0 para evitar división por cero
+  // Validate that skillsCount is not 0 to avoid division by zero
   const automatePercent = skillsCount > 0 ? ((automateCount/skillsCount)*100).toFixed(0) : '0';
   const assistPercent = skillsCount > 0 ? ((assistCount/skillsCount)*100).toFixed(0) : '0';
   const optimizePercent = skillsCount > 0 ? ((optimizeCount/skillsCount)*100).toFixed(0) : '0';
 
   return `
-📊 Resumen de Transformación:
-   • Interacciones originales: ${originalCount.toLocaleString()}
-   • Ruido eliminado: ${removedCount.toLocaleString()} (${removedPercentage}%)
-   • Interacciones limpias: ${cleanedCount.toLocaleString()}
-   • Skills únicos: ${skillsCount}
+📊 Transformation Summary:
+   • Original interactions: ${originalCount.toLocaleString()}
+   • Noise removed: ${removedCount.toLocaleString()} (${removedPercentage}%)
+   • Clean interactions: ${cleanedCount.toLocaleString()}
+   • Unique skills: ${skillsCount}
 
 🎯 Agentic Readiness:
    • 🟢 Automate Now: ${automateCount} skills (${automatePercent}%)

frontend/utils/segmentClassifier.ts

@@ -1,5 +1,5 @@
 // utils/segmentClassifier.ts
-// Utilidad para clasificar colas/skills en segmentos de cliente
+// Utility to classify queues/skills into customer segments
 
 import type { CustomerSegment, RawInteraction, StaticConfig } from '../types';
 
@@ -10,8 +10,8 @@ export interface SegmentMapping {
 }
 
 /**
- * Parsea string de colas separadas por comas
- * Ejemplo: "VIP, Premium, Enterprise" → ["VIP", "Premium", "Enterprise"]
+ * Parses queue string separated by commas
+ * Example: "VIP, Premium, Enterprise" → ["VIP", "Premium", "Enterprise"]
  */
 export function parseQueueList(input: string): string[] {
   if (!input || input.trim().length === 0) {
@@ -25,13 +25,13 @@ export function parseQueueList(input: string): string[] {
 }
 
 /**
- * Clasifica una cola según el mapeo proporcionado
- * Usa matching parcial y case-insensitive
+ * Classifies a queue according to the provided mapping
+ * Uses partial and case-insensitive matching
  * 
- * Ejemplo:
+ * Example:
  * - queue: "VIP_Support" + mapping.high: ["VIP"] → "high"
- * - queue: "Soporte_General_N1" + mapping.medium: ["Soporte_General"] → "medium"
- * - queue: "Retencion" (no match) → "medium" (default)
+ * - queue: "General_Support_L1" + mapping.medium: ["General_Support"] → "medium"
+ * - queue: "Retention" (no match) → "medium" (default)
  */
 export function classifyQueue(
   queue: string, 
@@ -39,7 +39,7 @@ export function classifyQueue(
 ): CustomerSegment {
   const normalizedQueue = queue.toLowerCase().trim();
   
-  // Buscar en high value
+  // Search in high value
   for (const highQueue of mapping.high_value_queues) {
     const normalizedHigh = highQueue.toLowerCase().trim();
     if (normalizedQueue.includes(normalizedHigh) || normalizedHigh.includes(normalizedQueue)) {
@@ -47,7 +47,7 @@ export function classifyQueue(
     }
   }
   
-  // Buscar en low value
+  // Search in low value
   for (const lowQueue of mapping.low_value_queues) {
     const normalizedLow = lowQueue.toLowerCase().trim();
     if (normalizedQueue.includes(normalizedLow) || normalizedLow.includes(normalizedQueue)) {
@@ -55,7 +55,7 @@ export function classifyQueue(
     }
   }
   
-  // Buscar en medium value (explícito)
+  // Search in medium value (explicit)
   for (const mediumQueue of mapping.medium_value_queues) {
     const normalizedMedium = mediumQueue.toLowerCase().trim();
     if (normalizedQueue.includes(normalizedMedium) || normalizedMedium.includes(normalizedQueue)) {
@@ -63,13 +63,13 @@ export function classifyQueue(
     }
   }
   
-  // Default: medium (para colas no mapeadas)
+  // Default: medium (for unmapped queues)
   return 'medium';
 }
 
 /**
- * Clasifica todas las colas únicas de un conjunto de interacciones
- * Retorna un mapa de cola → segmento
+ * Classifies all unique queues from a set of interactions
+ * Returns a map of queue → segment
  */
 export function classifyAllQueues(
   interactions: RawInteraction[],
@@ -77,10 +77,10 @@ export function classifyAllQueues(
 ): Map<string, CustomerSegment> {
   const queueSegments = new Map<string, CustomerSegment>();
   
-  // Obtener colas únicas
+  // Get unique queues
   const uniqueQueues = [...new Set(interactions.map(i => i.queue_skill))];
   
-  // Clasificar cada cola
+  // Classify each queue
   uniqueQueues.forEach(queue => {
     queueSegments.set(queue, classifyQueue(queue, mapping));
   });
@@ -89,8 +89,8 @@ export function classifyAllQueues(
 }
 
 /**
- * Genera estadísticas de segmentación
- * Retorna conteo, porcentaje y lista de colas por segmento
+ * Generates segmentation statistics
+ * Returns count, percentage and list of queues by segment
  */
 export function getSegmentationStats(
   interactions: RawInteraction[],
@@ -108,13 +108,13 @@ export function getSegmentationStats(
     total: interactions.length
   };
   
-  // Contar interacciones por segmento
+  // Count interactions by segment
   interactions.forEach(interaction => {
     const segment = queueSegments.get(interaction.queue_skill) || 'medium';
     stats[segment].count++;
   });
   
-  // Calcular porcentajes
+  // Calculate percentages
   const total = interactions.length;
   if (total > 0) {
     stats.high.percentage = Math.round((stats.high.count / total) * 100);
@@ -122,7 +122,7 @@ export function getSegmentationStats(
     stats.low.percentage = Math.round((stats.low.count / total) * 100);
   }
   
-  // Obtener colas por segmento (únicas)
+  // Get queues by segment (unique)
   queueSegments.forEach((segment, queue) => {
     if (!stats[segment].queues.includes(queue)) {
       stats[segment].queues.push(queue);
@@ -133,7 +133,7 @@ export function getSegmentationStats(
 }
 
 /**
- * Valida que el mapeo tenga al menos una cola en algún segmento
+ * Validates that the mapping has at least one queue in some segment
  */
 export function isValidMapping(mapping: SegmentMapping): boolean {
   return (
@@ -144,8 +144,8 @@ export function isValidMapping(mapping: SegmentMapping): boolean {
 }
 
 /**
- * Crea un mapeo desde StaticConfig
- * Si no hay segment_mapping, retorna mapeo vacío
+ * Creates a mapping from StaticConfig
+ * If there is no segment_mapping, returns empty mapping
  */
 export function getMappingFromConfig(config: StaticConfig): SegmentMapping | null {
   if (!config.segment_mapping) {
@@ -160,8 +160,8 @@ export function getMappingFromConfig(config: StaticConfig): SegmentMapping | nul
 }
 
 /**
- * Obtiene el segmento para una cola específica desde el config
- * Si no hay mapeo, retorna 'medium' por defecto
+ * Gets the segment for a specific queue from the config
+ * If there is no mapping, returns 'medium' by default
  */
 export function getSegmentForQueue(
   queue: string,
@@ -177,7 +177,7 @@ export function getSegmentForQueue(
 }
 
 /**
- * Formatea estadísticas para mostrar en UI
+ * Formats statistics for display in UI
  */
 export function formatSegmentationSummary(
   stats: ReturnType<typeof getSegmentationStats>
@@ -185,15 +185,15 @@ export function formatSegmentationSummary(
   const parts: string[] = [];
   
   if (stats.high.count > 0) {
-    parts.push(`${stats.high.percentage}% High Value (${stats.high.count} interacciones)`);
+    parts.push(`${stats.high.percentage}% High Value (${stats.high.count} interactions)`);
   }
   
   if (stats.medium.count > 0) {
-    parts.push(`${stats.medium.percentage}% Medium Value (${stats.medium.count} interacciones)`);
+    parts.push(`${stats.medium.percentage}% Medium Value (${stats.medium.count} interactions)`);
   }
   
   if (stats.low.count > 0) {
-    parts.push(`${stats.low.percentage}% Low Value (${stats.low.count} interacciones)`);
+    parts.push(`${stats.low.percentage}% Low Value (${stats.low.count} interactions)`);
   }
   
   return parts.join(' | ');