Translate Phase 1 high-priority frontend utils (backendMapper, analysisGenerator, realDataAnalysis)

Phase 1 of Spanish-to-English translation for critical path files:
- backendMapper.ts: Translated ~50 occurrences (comments, labels, dimension titles)
- analysisGenerator.ts: Translated ~49 occurrences (findings, recommendations, dimension content)
- realDataAnalysis.ts: Translated ~92 occurrences (clasificarTier functions, inline comments)

All function names and API variable names preserved for compatibility.
Frontend compilation tested and verified successful.

Related to TRANSLATION_STATUS.md Phase 1 objectives.

https://claude.ai/code/session_01GNbnkFoESkRcnPr3bLCYDg

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
@@ -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"
+  // Si no hay CSAT, mostrar dimensión con "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'
@@ -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
@@ -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
@@ -954,7 +954,7 @@ 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)
@@ -975,9 +975,9 @@ export function mapBackendResultsToAnalysisData(
       : '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),
     });
   }
@@ -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
   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()} interacciones/período`,
+      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: [],
@@ -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)
       });
     }
   }
@@ -1314,7 +1314,7 @@ export function buildHeatmapFromBackend(
     // Dimensiones agentic similares a las que tenías en 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(
@@ -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,