Aula 3: Análise de Performance

class AdvancedAnalyticsEngine {
  constructor() {
    this.dataCollector = new RealTimeDataCollector();
    this.dataProcessor = new IntelligentDataProcessor();
    this.insightGenerator = new AIInsightGenerator();
    this.actionOrchestrator = new ActionOrchestrator();
  }

  // Sistema abrangente de coleta de dados
  async setupComprehensiveDataCollection() {
    const dataSources = {
      // Dados de conversação WhatsApp
      whatsappData: {
        conversations: await this.setupConversationTracking(),
        interactions: await this.setupInteractionTracking(),
        responses: await this.setupResponseTracking(),
        engagementMetrics: await this.setupEngagementTracking()
      },

      // Dados de funil e conversão
      funnelData: {
        stageConversions: await this.setupStageTracking(),
        dropoffPoints: await this.setupDropoffTracking(),
        leadQuality: await this.setupQualityTracking(),
        timeToConvert: await this.setupTimingTracking()
      },

      // Dados de negócio
      businessData: {
        revenue: await this.setupRevenueTracking(),
        customerLifetime: await this.setupLTVTracking(),
        acquisition: await this.setupAcquisitionTracking(),
        retention: await this.setupRetentionTracking()
      },

      // Dados contextuais
      contextualData: {
        market: await this.setupMarketDataTracking(),
        competitive: await this.setupCompetitiveTracking(),
        seasonal: await this.setupSeasonalTracking(),
        external: await this.setupExternalFactorsTracking()
      }
    };

    return this.unifyDataSources(dataSources);
  }

  // Processamento inteligente de dados
  async processDataIntelligently(rawData) {
    const processedData = {
      // Limpeza e validação
      cleanedData: await this.dataProcessor.cleanAndValidate(rawData),
      
      // Enriquecimento com contexto
      enrichedData: await this.dataProcessor.enrichWithContext(rawData),
      
      // Agregações e cálculos
      aggregatedData: await this.dataProcessor.calculateMetrics(rawData),
      
      // Segmentação automática
      segmentedData: await this.dataProcessor.segmentAutomatically(rawData)
    };

    return processedData;
  }

  // Geração de insights acionáveis
  async generateActionableInsights(processedData) {
    const insights = {
      // Insights de performance
      performanceInsights: await this.insightGenerator.analyzePerformance(processedData),
      
      // Insights de oportunidade
      opportunityInsights: await this.insightGenerator.identifyOpportunities(processedData),
      
      // Insights de risco
      riskInsights: await this.insightGenerator.assessRisks(processedData),
      
      // Insights preditivos
      predictiveInsights: await this.insightGenerator.generatePredictions(processedData)
    };

    // Prioriza insights por impacto
    return this.prioritizeInsightsByImpact(insights);
  }

  // Orquestração de ações baseadas em insights
  async orchestrateDataDrivenActions(insights) {
    const actionPlan = await this.actionOrchestrator.createActionPlan(insights);
    
    for (const action of actionPlan) {
      if (action.automatable && action.riskLevel < 0.3) {
        // Executa ações automáticas de baixo risco
        await this.executeAutomaticAction(action);
      } else {
        // Escalona ações que requerem aprovação humana
        await this.escalateForHumanReview(action);
      }
      
      // Monitora resultado da ação
      await this.monitorActionResults(action);
    }

    return actionPlan;
  }

  // Dashboard executivo em tempo real
  async generateExecutiveDashboard() {
    const dashboardData = {
      // KPIs principais
      primaryKPIs: await this.calculatePrimaryKPIs(),
      
      // Trends e padrões
      trends: await this.identifyKeyTrends(),
      
      // Alertas críticos
      alerts: await this.generateCriticalAlerts(),
      
      // Recomendações estratégicas
      recommendations: await this.generateStrategicRecommendations(),
      
      // Previsões
      forecasts: await this.generateBusinessForecasts()
    };

    return this.formatForExecutiveConsumption(dashboardData);
  }
}

class KPIManagementSystem {
  constructor() {
    this.kpiCalculator = new RealTimeKPICalculator();
    this.thresholdManager = new KPIThresholdManager();
    this.alertSystem = new KPIAlertSystem();
    this.forecastEngine = new KPIForecastEngine();
  }

  // Sistema de cálculo de KPIs em tempo real
  async calculateCriticalKPIs(timeframe = '30_days') {
    const kpis = {
      // KPIs de Receita
      revenueKPIs: await this.calculateRevenueKPIs(timeframe),
      
      // KPIs de Conversão
      conversionKPIs: await this.calculateConversionKPIs(timeframe),
      
      // KPIs de Eficiência
      efficiencyKPIs: await this.calculateEfficiencyKPIs(timeframe),
      
      // KPIs de Qualidade
      qualityKPIs: await this.calculateQualityKPIs(timeframe)
    };

    return this.enrichKPIsWithContext(kpis, timeframe);
  }

  // Cálculo de KPIs de receita
  async calculateRevenueKPIs(timeframe) {
    const data = await this.getRevenueData(timeframe);
    
    return {
      // Receita total
      totalRevenue: {
        value: data.totalRevenue,
        trend: await this.calculateTrend(data.totalRevenue, timeframe),
        target: await this.getRevenueTarget(timeframe),
        performance: this.calculatePerformanceVsTarget(data.totalRevenue, await this.getRevenueTarget(timeframe))
      },

      // Receita recorrente mensal (MRR)
      monthlyRecurringRevenue: {
        value: data.mrr,
        growth: await this.calculateMRRGrowth(timeframe),
        churnImpact: await this.calculateChurnImpact(timeframe),
        expansionRevenue: await this.calculateExpansionRevenue(timeframe)
      },

      // Valor de vida do cliente (LTV)
      customerLifetimeValue: {
        value: await this.calculateLTV(),
        bySegment: await this.calculateLTVBySegment(),
        trend: await this.analyzeLTVTrend(timeframe),
        ltvcacRatio: await this.calculateLTVCACRatio()
      },

      // Receita por cliente
      revenuePerCustomer: {
        value: data.totalRevenue / data.customerCount,
        bySegment: await this.calculateRevenuePerCustomerBySegment(),
        trend: await this.analyzeRevenuePerCustomerTrend(timeframe)
      }
    };
  }

  // Cálculo de KPIs de conversão
  async calculateConversionKPIs(timeframe) {
    const funnelData = await this.getFunnelData(timeframe);
    
    return {
      // Taxa de conversão geral
      overallConversionRate: {
        value: funnelData.conversions / funnelData.totalLeads,
        byStage: await this.calculateStageConversions(funnelData),
        bySource: await this.calculateConversionBySource(funnelData),
        trend: await this.analyzeConversionTrend(timeframe)
      },

      // Velocidade do pipeline
      pipelineVelocity: {
        value: await this.calculatePipelineVelocity(funnelData),
        byStage: await this.calculateStageVelocity(funnelData),
        bottlenecks: await this.identifyVelocityBottlenecks(funnelData)
      },

      // Taxa de fechamento
      winRate: {
        value: funnelData.closedWon / funnelData.totalOpportunities,
        byRep: await this.calculateWinRateByRep(funnelData),
        byProduct: await this.calculateWinRateByProduct(funnelData),
        trend: await this.analyzeWinRateTrend(timeframe)
      },

      // Qualidade dos leads
      leadQuality: {
        qualificationRate: funnelData.qualifiedLeads / funnelData.totalLeads,
        sqlConversionRate: funnelData.sqlLeads / funnelData.qualifiedLeads,
        avgLeadScore: await this.calculateAvgLeadScore(funnelData)
      }
    };
  }

  // Sistema de alertas baseado em KPIs
  async setupKPIAlerting() {
    const alertRules = {
      // Alertas de performance crítica
      criticalPerformance: {
        conversionDropAlert: {
          condition: 'conversion_rate_drops_20_percent_week_over_week',
          severity: 'CRITICAL',
          action: 'immediate_investigation_required',
          recipients: ['sales_director', 'marketing_head']
        },
        
        revenueShortfallAlert: {
          condition: 'monthly_revenue_below_80_percent_target',
          severity: 'HIGH',
          action: 'revenue_recovery_plan_activation',
          recipients: ['ceo', 'sales_director', 'finance_head']
        }
      },

      // Alertas de oportunidade
      opportunityAlerts: {
        highPerformanceDetected: {
          condition: 'conversion_rate_above_150_percent_baseline',
          severity: 'OPPORTUNITY',
          action: 'scale_winning_strategy',
          recipients: ['marketing_team', 'sales_team']
        },
        
        segmentBreakoutDetected: {
          condition: 'new_segment_performance_above_average',
          severity: 'OPPORTUNITY',
          action: 'investigate_and_scale',
          recipients: ['growth_team']
        }
      },

      // Alertas preditivos
      predictiveAlerts: {
        forecastMissRisk: {
          condition: 'pipeline_trajectory_suggests_forecast_miss',
          severity: 'WARNING',
          action: 'pipeline_acceleration_needed',
          recipients: ['sales_director', 'sales_ops']
        }
      }
    };

    return this.activateAlertSystem(alertRules);
  }

  // Dashboard de KPIs em tempo real
  async generateKPIDashboard() {
    const kpis = await this.calculateCriticalKPIs();
    
    return {
      // Visão executiva
      executiveView: {
        primaryKPIs: this.selectPrimaryKPIs(kpis),
        performanceIndicators: this.calculatePerformanceIndicators(kpis),
        trendAnalysis: await this.generateTrendAnalysis(kpis),
        criticalAlerts: await this.getCriticalAlerts()
      },

      // Visão operacional
      operationalView: {
        dailyMetrics: await this.getDailyMetrics(),
        teamPerformance: await this.getTeamPerformance(),
        systemHealth: await this.getSystemHealth(),
        actionableInsights: await this.getActionableInsights()
      },

      // Visão estratégica
      strategicView: {
        businessGrowth: await this.getBusinessGrowthMetrics(),
        marketPosition: await this.getMarketPositionMetrics(),
        competitiveAnalysis: await this.getCompetitiveMetrics(),
        forecastAccuracy: await this.getForecastAccuracy()
      }
    };
  }
}

class IntelligentAlertSystem {
  constructor() {
    this.thresholdEngine = new DynamicThresholdEngine();
    this.anomalyDetector = new AnomalyDetector();
    this.alertRouter = new AlertRouter();
    this.escalationManager = new EscalationManager();
  }

  // Sistema de alertas multi-nível
  async setupIntelligentAlerting() {
    const alertLevels = {
      // Nível 1: Alertas Informativos
      informational: {
        triggers: [
          'daily_summary_report',
          'weekly_trend_update',
          'monthly_benchmark_comparison'
        ],
        recipients: ['team_leads', 'analysts'],
        delivery: 'email_digest'
      },

      // Nível 2: Alertas de Atenção
      attention: {
        triggers: [
          'kpi_threshold_approached',
          'unusual_pattern_detected',
          'competitor_activity_spotted'
        ],
        recipients: ['managers', 'directors'],
        delivery: 'real_time_notification'
      },

      // Nível 3: Alertas Críticos
      critical: {
        triggers: [
          'revenue_target_at_risk',
          'system_downtime_detected',
          'major_client_churn_risk'
        ],
        recipients: ['executives', 'on_call_team'],
        delivery: 'immediate_multi_channel'
      },

      // Nível 4: Alertas de Emergência
      emergency: {
        triggers: [
          'security_breach_detected',
          'data_loss_event',
          'regulatory_compliance_violation'
        ],
        recipients: ['ceo', 'legal', 'security_team'],
        delivery: 'emergency_protocol'
      }
    };

    return this.activateAlertLevels(alertLevels);
  }

  // Detecção de anomalias em KPIs
  async detectKPIAnomalies() {
    const currentKPIs = await this.getCurrentKPIs();
    const historicalBaseline = await this.getHistoricalBaseline();
    
    const anomalies = [];
    
    for (const [kpiName, currentValue] of Object.entries(currentKPIs)) {
      const anomaly = await this.anomalyDetector.detect({
        metric: kpiName,
        currentValue: currentValue,
        baseline: historicalBaseline[kpiName],
        seasonality: await this.getSeasonalityPattern(kpiName),
        context: await this.getBusinessContext()
      });

      if (anomaly.isSignificant) {
        anomalies.push({
          kpi: kpiName,
          anomaly: anomaly,
          impact: await this.assessAnomalyImpact(anomaly),
          recommendations: await this.generateAnomalyRecommendations(anomaly)
        });
      }
    }

    return this.prioritizeAnomalies(anomalies);
  }

  // Escalação automática de alertas
  async manageAlertEscalation(alert) {
    const escalationRules = {
      // Escalação por tempo
      timeBasedEscalation: {
        level1: { timeout: '15_minutes', action: 'notify_senior_manager' },
        level2: { timeout: '1_hour', action: 'notify_director' },
        level3: { timeout: '4_hours', action: 'notify_executive_team' }
      },

      // Escalação por impacto
      impactBasedEscalation: {
        low: 'standard_notification',
        medium: 'priority_notification',
        high: 'urgent_notification',
        critical: 'emergency_notification'
      },

      // Escalação por contexto
      contextBasedEscalation: {
        businessHours: 'standard_escalation',
        afterHours: 'on_call_escalation',
        weekend: 'executive_escalation',
        holiday: 'emergency_only_escalation'
      }
    };

    return this.executeEscalation(alert, escalationRules);
  }

  // Relatório de performance de alertas
  async generateAlertPerformanceReport() {
    return {
      alertEffectiveness: {
        truePositiveRate: await this.calculateTruePositiveRate(),
        falsePositiveRate: await this.calculateFalsePositiveRate(),
        responseTime: await this.calculateAverageResponseTime(),
        resolutionTime: await this.calculateAverageResolutionTime()
      },

      alertVolume: {
        totalAlerts: await this.countTotalAlerts(),
        byCategory: await this.categorizeAlerts(),
        bySeverity: await this.groupAlertsBySeverity(),
        trends: await this.analyzeAlertTrends()
      },

      businessImpact: {
        issuesPrevented: await this.countPreventedIssues(),
        revenueProtected: await this.calculateRevenueProtected(),
        costOfMissedAlerts: await this.calculateMissedAlertCost(),
        roiOfAlertSystem: await this.calculateAlertSystemROI()
      }
    };
  }
}

// Sistema de Dashboard Executivo Inteligente
class ExecutiveDashboard {
  constructor() {
    this.dataAggregator = new DataAggregator();
    this.visualizationEngine = new VisualizationEngine();
    this.insightGenerator = new InsightGenerator();
    this.alertSystem = new AlertSystem();
    this.accessControl = new AccessControl();
  }

  // Configuração principal do dashboard
  async setupDashboard(executiveConfig) {
    return {
      layout: await this.generateLayout(executiveConfig.role),
      widgets: await this.selectWidgets(executiveConfig.priorities),
      permissions: await this.configurePermissions(executiveConfig.level),
      refreshInterval: this.calculateRefreshRate(executiveConfig.urgency)
    };
  }

  // Geração automática de layout baseada no papel
  async generateLayout(executiveRole) {
    const layouts = {
      ceo: {
        priority: ['revenue_overview', 'growth_metrics', 'market_position'],
        secondary: ['team_performance', 'customer_satisfaction', 'operational_efficiency']
      },
      sales_director: {
        priority: ['pipeline_health', 'conversion_rates', 'team_quota_progress'],
        secondary: ['lead_quality', 'deal_velocity', 'forecast_accuracy']
      },
      marketing_director: {
        priority: ['campaign_performance', 'lead_generation', 'attribution_analysis'],
        secondary: ['content_engagement', 'channel_effectiveness', 'brand_metrics']
      },
      operations_manager: {
        priority: ['system_performance', 'automation_efficiency', 'cost_optimization'],
        secondary: ['quality_metrics', 'process_improvements', 'resource_utilization']
      }
    };

    return layouts[executiveRole] || layouts.ceo;
  }
}

// Engine de Visualização Inteligente
class VisualizationEngine {
  constructor() {
    this.chartTypes = new Map();
    this.colorSchemes = new ColorSchemeManager();
    this.interactionPatterns = new InteractionManager();
  }

  // Seleção automática do tipo de visualização
  selectOptimalVisualization(dataType, context) {
    const rules = {
      trend_analysis: () => this.createTimeSeriesChart(),
      comparison: (data) => data.length > 5 ? this.createHeatmap() : this.createBarChart(),
      distribution: () => this.createHistogram(),
      correlation: () => this.createScatterPlot(),
      geographic: () => this.createMapVisualization(),
      hierarchical: () => this.createTreemap()
    };

    return rules[dataType]?.() || this.createDefaultChart();
  }

  // Geração de insights visuais automáticos
  async generateVisualInsights(chartData, businessContext) {
    const insights = {
      trends: await this.identifyTrends(chartData),
      anomalies: await this.detectAnomalies(chartData),
      patterns: await this.findPatterns(chartData),
      predictions: await this.generatePredictions(chartData)
    };

    return this.contextualizeInsights(insights, businessContext);
  }
}

// Sistema de Agregação de Dados Multi-fonte
class DataAggregator {
  constructor() {
    this.sources = new Map();
    this.processors = new Map();
    this.cache = new CacheManager();
    this.validator = new DataValidator();
  }

  // Conexão com múltiplas fontes de dados
  async connectDataSources() {
    const sources = {
      whatsapp_api: {
        endpoint: process.env.WHATSAPP_WEBHOOK_URL,
        auth: process.env.WHATSAPP_ACCESS_TOKEN,
        refreshRate: 'real-time'
      },
      crm_system: {
        endpoint: process.env.CRM_API_ENDPOINT,
        auth: process.env.CRM_API_KEY,
        refreshRate: '5min'
      },
      analytics_platform: {
        endpoint: process.env.ANALYTICS_API_ENDPOINT,
        auth: process.env.ANALYTICS_TOKEN,
        refreshRate: '15min'
      },
      payment_gateway: {
        endpoint: process.env.PAYMENT_API_ENDPOINT,
        auth: process.env.PAYMENT_API_KEY,
        refreshRate: '1hour'
      }
    };

    for (const [name, config] of Object.entries(sources)) {
      await this.registerSource(name, config);
    }
  }

  // Processamento em tempo real de dados executivos
  async processExecutiveMetrics() {
    const metrics = await Promise.all([
      this.calculateRevenueMetrics(),
      this.calculateGrowthMetrics(),
      this.calculateEfficiencyMetrics(),
      this.calculateCustomerMetrics(),
      this.calculateTeamMetrics()
    ]);

    return this.consolidateMetrics(metrics);
  }

  // Cálculo de métricas de receita em tempo real
  async calculateRevenueMetrics() {
    const data = await this.aggregateFromSources(['crm_system', 'payment_gateway']);
    
    return {
      total_revenue: this.sumRevenue(data),
      revenue_growth: this.calculateGrowth(data, 'revenue'),
      recurring_revenue: this.calculateARR(data),
      revenue_per_customer: this.calculateAverageRevenue(data),
      forecast_accuracy: this.calculateForecastAccuracy(data)
    };
  }
}

// Sistema de Alertas Inteligentes para Executivos
class ExecutiveAlertSystem {
  constructor() {
    this.ruleEngine = new RuleEngine();
    this.notificationManager = new NotificationManager();
    this.priorityCalculator = new PriorityCalculator();
  }

  // Configuração de alertas baseada no perfil executivo
  configureExecutiveAlerts(executiveProfile) {
    const alertConfigs = {
      critical: {
        revenue_drop: { threshold: -10, timeframe: '24h' },
        system_outage: { threshold: 99, metric: 'uptime' },
        security_breach: { severity: 'high', immediate: true }
      },
      important: {
        conversion_decline: { threshold: -15, timeframe: '48h' },
        cost_increase: { threshold: 20, timeframe: '72h' },
        customer_churn: { threshold: 5, timeframe: '24h' }
      },
      informational: {
        goal_achievement: { threshold: 95, metric: 'target_completion' },
        new_opportunities: { growth: 25, timeframe: '7d' },
        efficiency_gains: { improvement: 10, timeframe: '7d' }
      }
    };

    return this.customizeForRole(alertConfigs, executiveProfile.role);
  }

  // Processamento de alertas com contexto executivo
  async processAlert(alertData) {
    const context = await this.gatherBusinessContext(alertData);
    const priority = await this.calculatePriority(alertData, context);
    const recommendations = await this.generateRecommendations(alertData, context);

    return {
      alert: alertData,
      priority,
      context,
      recommendations,
      actionable_steps: await this.generateActionSteps(alertData, context)
    };
  }
}

// Exportação da configuração completa
export const executiveDashboardConfig = {
  autoRefresh: true,
  refreshInterval: 30000, // 30 segundos
  highPriorityAlerts: true,
  mobileOptimized: true,
  exportCapabilities: ['pdf', 'excel', 'powerpoint'],
  collaborationFeatures: true
};

// Widgets Executivos Especializados
class ExecutiveWidgets {
  constructor() {
    this.widgetFactory = new WidgetFactory();
    this.dataFormatter = new DataFormatter();
    this.interactionHandler = new InteractionHandler();
  }

  // Widget de Visão Geral de Receita
  createRevenueOverviewWidget() {
    return {
      type: 'revenue_overview',
      layout: {
        size: 'large',
        position: 'top-left',
        responsive: true
      },
      data: {
        primary: ['total_revenue', 'monthly_growth', 'forecast_vs_actual'],
        secondary: ['revenue_by_channel', 'customer_segments', 'geographic_distribution']
      },
      visualization: {
        main: 'line_chart_with_goals',
        secondary: 'donut_charts',
        trending: 'sparklines'
      },
      interactions: {
        drill_down: ['by_product', 'by_region', 'by_time_period'],
        export: ['monthly_report', 'quarterly_analysis'],
        alerts: ['goal_achievement', 'significant_changes']
      }
    };
  }

  // Widget de Saúde do Pipeline
  createPipelineHealthWidget() {
    return {
      type: 'pipeline_health',
      metrics: {
        conversion_rates: 'by_stage',
        velocity: 'average_days_per_stage',
        quality: 'lead_score_distribution',
        forecast: 'weighted_pipeline_value'
      },
      visualizations: {
        funnel: 'interactive_funnel_chart',
        velocity: 'velocity_trends',
        quality: 'score_distribution_histogram',
        forecast: 'probability_weighted_bars'
      },
      actionable_insights: {
        bottlenecks: 'identify_slow_stages',
        opportunities: 'high_value_deals_at_risk',
        improvements: 'conversion_optimization_suggestions'
      }
    };
  }

  // Widget de Performance da Equipe
  createTeamPerformanceWidget() {
    return {
      type: 'team_performance',
      dimensions: {
        individual: ['quota_attainment', 'activity_levels', 'conversion_rates'],
        team: ['collective_goals', 'collaboration_metrics', 'efficiency_trends'],
        comparative: ['peer_benchmarking', 'historical_comparison', 'industry_benchmarks']
      },
      insights: {
        top_performers: 'identification_and_analysis',
        improvement_areas: 'skill_gap_analysis',
        coaching_opportunities: 'personalized_recommendations'
      }
    };
  }

  // Widget de Inteligência de Mercado
  createMarketIntelligenceWidget() {
    return {
      type: 'market_intelligence',
      data_sources: {
        competitive: 'competitor_analysis',
        industry: 'market_trends',
        customer: 'sentiment_analysis',
        economic: 'market_indicators'
      },
      analysis: {
        positioning: 'competitive_positioning_matrix',
        opportunities: 'market_gap_identification',
        threats: 'risk_assessment_matrix',
        trends: 'predictive_trend_analysis'
      }
    };
  }
}

// Sistema de Personalização de Dashboard
class DashboardPersonalization {
  constructor() {
    this.userBehaviorTracker = new UserBehaviorTracker();
    this.preferenceEngine = new PreferenceEngine();
    this.adaptiveLayoutEngine = new AdaptiveLayoutEngine();
  }

  // Aprendizado de preferências do executivo
  async learnExecutivePreferences(userId, interactions) {
    const patterns = {
      time_preferences: this.analyzeTimePatterns(interactions),
      content_preferences: this.analyzeContentPreferences(interactions),
      detail_level: this.analyzeDetailPreferences(interactions),
      visualization_preferences: this.analyzeVisualizationPreferences(interactions)
    };

    return this.updatePersonalizationModel(userId, patterns);
  }

  // Adaptação automática do layout
  async adaptLayout(userId, currentContext) {
    const preferences = await this.getPreferences(userId);
    const contextualFactors = await this.analyzeContext(currentContext);
    
    return {
      widget_priorities: this.reorderWidgetsByImportance(preferences, contextualFactors),
      detail_levels: this.adjustDetailLevels(preferences, contextualFactors),
      refresh_frequencies: this.optimizeRefreshRates(preferences, contextualFactors),
      notification_settings: this.customizeNotifications(preferences, contextualFactors)
    };
  }
}

// Integração com Sistemas Executivos
class ExecutiveSystemIntegration {
  constructor() {
    this.apiManager = new APIManager();
    this.dataTransformer = new DataTransformer();
    this.securityManager = new SecurityManager();
  }

  // Integração com WhatsApp Business API
  async integrateWhatsAppData() {
    const whatsappMetrics = {
      conversation_analytics: {
        total_conversations: await this.getConversationCount(),
        active_conversations: await this.getActiveConversations(),
        response_times: await this.getResponseTimeMetrics(),
        resolution_rates: await this.getResolutionRates()
      },
      business_impact: {
        sales_attribution: await this.getSalesAttribution(),
        lead_quality: await this.getLeadQualityMetrics(),
        customer_satisfaction: await this.getCustomerSatisfaction(),
        automation_efficiency: await this.getAutomationMetrics()
      }
    };

    return this.transformForExecutiveView(whatsappMetrics);
  }

  // Integração com CRM Executivo
  async integrateCRMExecutiveView() {
    return {
      sales_pipeline: {
        total_value: await this.calculateTotalPipelineValue(),
        weighted_forecast: await this.calculateWeightedForecast(),
        stage_distribution: await this.getPipelineDistribution(),
        velocity_trends: await this.getVelocityTrends()
      },
      customer_lifecycle: {
        acquisition_cost: await this.calculateCAC(),
        lifetime_value: await this.calculateCLV(),
        churn_prediction: await this.predictChurn(),
        expansion_opportunities: await this.identifyExpansionOpportunities()
      },
      team_effectiveness: {
        productivity_metrics: await this.getProductivityMetrics(),
        goal_achievement: await this.getGoalAchievement(),
        coaching_needs: await this.identifyCoachingNeeds(),
        resource_optimization: await this.analyzeResourceUtilization()
      }
    };
  }

  // Processamento de dados para vista executiva
  transformForExecutiveView(rawData) {
    return {
      summary: this.generateExecutiveSummary(rawData),
      key_insights: this.extractKeyInsights(rawData),
      action_items: this.generateActionItems(rawData),
      risk_indicators: this.identifyRisks(rawData),
      opportunity_indicators: this.identifyOpportunities(rawData)
    };
  }

  // Geração de relatórios executivos automatizados
  async generateExecutiveReport(timeframe, focus_areas) {
    const reportData = await this.aggregateReportData(timeframe, focus_areas);
    
    return {
      executive_summary: this.createExecutiveSummary(reportData),
      performance_highlights: this.extractHighlights(reportData),
      key_challenges: this.identifyChallenges(reportData),
      strategic_recommendations: this.generateRecommendations(reportData),
      action_plan: this.createActionPlan(reportData),
      appendix: this.createDetailedAppendix(reportData)
    };
  }
}

// Sistema de Exportação e Compartilhamento
class ExecutiveReporting {
  constructor() {
    this.reportGenerator = new ReportGenerator();
    this.templateManager = new TemplateManager();
    this.distributionManager = new DistributionManager();
  }

  // Geração de apresentações executivas
  async generateExecutivePresentation(data, audience) {
    const templates = {
      board_meeting: 'high_level_strategic_overview',
      leadership_team: 'detailed_operational_review',
      stakeholder_update: 'progress_and_results_focused',
      investor_presentation: 'growth_and_financial_metrics'
    };

    const template = templates[audience] || templates.leadership_team;
    
    return {
      slides: await this.generateSlides(data, template),
      executive_summary: await this.generateSummary(data),
      appendix: await this.generateDetailedData(data),
      talking_points: await this.generateTalkingPoints(data, audience)
    };
  }

  // Distribuição automática de relatórios
  async scheduleAutomaticReporting(recipients, frequency, format) {
    const schedule = {
      daily: this.setupDailyReports(recipients, format),
      weekly: this.setupWeeklyReports(recipients, format),
      monthly: this.setupMonthlyReports(recipients, format),
      quarterly: this.setupQuarterlyReports(recipients, format)
    };

    return schedule[frequency] || schedule.weekly;
  }
}

// Sistema de Predições de Vendas com IA
class SalesPredictionEngine {
  constructor() {
    this.mlModels = new MLModelManager();
    this.dataProcessor = new DataProcessor();
    this.featureEngine = new FeatureEngine();
    this.validationEngine = new ValidationEngine();
    this.realTimePredictor = new RealTimePredictor();
  }

  // Configuração dos modelos preditivos
  async initializePredictionModels() {
    const models = {
      revenue_forecasting: {
        type: 'time_series',
        algorithm: 'LSTM',
        features: ['historical_revenue', 'seasonality', 'market_trends', 'campaign_data'],
        horizon: '90_days',
        confidence_interval: 0.95
      },
      deal_probability: {
        type: 'classification',
        algorithm: 'gradient_boosting',
        features: ['lead_score', 'interaction_history', 'company_size', 'budget_confirmed'],
        output: 'win_probability',
        threshold: 0.7
      },
      customer_lifetime_value: {
        type: 'regression',
        algorithm: 'random_forest',
        features: ['purchase_history', 'engagement_metrics', 'support_interactions'],
        prediction_window: '24_months',
        update_frequency: 'daily'
      },
      churn_prediction: {
        type: 'binary_classification',
        algorithm: 'xgboost',
        features: ['usage_decline', 'support_tickets', 'payment_delays', 'engagement_drop'],
        early_warning: '30_days',
        action_threshold: 0.6
      }
    };

    for (const [modelName, config] of Object.entries(models)) {
      await this.trainModel(modelName, config);
    }

    return this.validateAllModels();
  }

  // Predição de receita com múltiplos cenários
  async predictRevenue(timeframe, scenario = 'realistic') {
    const scenarios = {
      pessimistic: { growth_factor: 0.85, confidence: 0.8 },
      realistic: { growth_factor: 1.0, confidence: 0.9 },
      optimistic: { growth_factor: 1.15, confidence: 0.75 }
    };

    const baseData = await this.prepareRevenueData();
    const features = await this.extractRevenueFeatures(baseData);
    
    const prediction = await this.mlModels.predict('revenue_forecasting', features);
    const adjustedPrediction = this.applyScenario(prediction, scenarios[scenario]);

    return {
      predicted_revenue: adjustedPrediction.value,
      confidence_interval: adjustedPrediction.confidence_range,
      contributing_factors: await this.identifyRevenueDrivers(features),
      risk_factors: await this.identifyRisks(features),
      scenario_analysis: await this.generateScenarioComparison(),
      recommendations: await this.generateRevenueRecommendations(adjustedPrediction)
    };
  }

  // Análise preditiva de pipeline
  async analyzePipelineQuality() {
    const pipelineData = await this.getPipelineData();
    const predictions = await Promise.all(
      pipelineData.deals.map(deal => this.predictDealOutcome(deal))
    );

    return {
      pipeline_health_score: this.calculateHealthScore(predictions),
      high_probability_deals: predictions.filter(p => p.win_probability > 0.8),
      at_risk_deals: predictions.filter(p => p.win_probability < 0.3),
      expected_close_dates: this.predictCloseDates(predictions),
      revenue_forecast: this.calculateWeightedRevenue(predictions),
      bottleneck_analysis: await this.identifyPipelineBottlenecks(pipelineData)
    };
  }
}

// Engine de Features Avançadas
class AdvancedFeatureEngine {
  constructor() {
    this.timeSeriesProcessor = new TimeSeriesProcessor();
    this.behavioralAnalyzer = new BehavioralAnalyzer();
    this.externalDataIntegrator = new ExternalDataIntegrator();
  }

  // Extração de features temporais
  extractTimeSeriesFeatures(data, windowSize = 30) {
    return {
      trend_features: {
        linear_trend: this.calculateLinearTrend(data, windowSize),
        exponential_trend: this.calculateExponentialTrend(data, windowSize),
        seasonal_patterns: this.identifySeasonality(data),
        cyclical_patterns: this.identifyCycles(data)
      },
      statistical_features: {
        moving_averages: this.calculateMovingAverages(data, [7, 14, 30]),
        volatility: this.calculateVolatility(data, windowSize),
        momentum_indicators: this.calculateMomentum(data),
        support_resistance: this.identifySupportResistance(data)
      },
      lag_features: {
        autoregressive: this.createLagFeatures(data, 5),
        seasonal_lags: this.createSeasonalLags(data),
        interaction_lags: this.createInteractionLags(data)
      }
    };
  }

  // Features comportamentais avançadas
  extractBehavioralFeatures(customerData) {
    return {
      engagement_patterns: {
        session_frequency: this.analyzeSessionFrequency(customerData),
        feature_usage: this.analyzeFeatureUsage(customerData),
        support_interaction_patterns: this.analyzeSupportPatterns(customerData),
        content_consumption: this.analyzeContentConsumption(customerData)
      },
      lifecycle_stage: {
        customer_maturity: this.assessCustomerMaturity(customerData),
        adoption_velocity: this.calculateAdoptionVelocity(customerData),
        expansion_readiness: this.assessExpansionReadiness(customerData),
        churn_indicators: this.identifyChurnSignals(customerData)
      },
      interaction_quality: {
        response_latency: this.analyzeResponseLatency(customerData),
        conversation_depth: this.analyzeConversationDepth(customerData),
        satisfaction_signals: this.extractSatisfactionSignals(customerData),
        escalation_patterns: this.analyzeEscalationPatterns(customerData)
      }
    };
  }

  // Integração de dados externos
  async integrateExternalSignals() {
    return {
      market_indicators: {
        industry_growth: await this.getIndustryGrowthData(),
        competitor_activity: await this.getCompetitorIntelligence(),
        economic_indicators: await this.getEconomicData(),
        technology_trends: await this.getTechnologyTrends()
      },
      social_signals: {
        brand_sentiment: await this.getBrandSentiment(),
        social_media_buzz: await this.getSocialMediaMetrics(),
        review_sentiment: await this.getReviewSentiment(),
        influencer_mentions: await this.getInfluencerMentions()
      },
      temporal_signals: {
        seasonal_factors: this.getSeasonalFactors(),
        holiday_impact: this.getHolidayImpact(),
        business_cycle: this.getBusinessCyclePhase(),
        market_volatility: this.getMarketVolatility()
      }
    };
  }
}

// Sistema de Predições em Tempo Real
class RealTimePredictionSystem {
  constructor() {
    this.streamProcessor = new StreamProcessor();
    this.modelCache = new ModelCache();
    this.alertManager = new AlertManager();
    this.decisionEngine = new DecisionEngine();
  }

  // Processamento de eventos em tempo real
  async processRealTimeEvent(event) {
    const enrichedEvent = await this.enrichEvent(event);
    const predictions = await this.generateRealTimePredictions(enrichedEvent);
    const actions = await this.determineActions(predictions);

    return {
      event: enrichedEvent,
      predictions,
      confidence_scores: this.calculateConfidenceScores(predictions),
      recommended_actions: actions,
      urgency_level: this.assessUrgency(predictions),
      automated_actions: await this.executeAutomatedActions(actions)
    };
  }

  // Predições contextuais de oportunidades
  async predictOpportunityScore(customerInteraction) {
    const context = await this.buildInteractionContext(customerInteraction);
    const features = await this.extractRealTimeFeatures(context);
    
    const predictions = {
      purchase_intent: await this.predictPurchaseIntent(features),
      optimal_timing: await this.predictOptimalTiming(features),
      price_sensitivity: await this.predictPriceSensitivity(features),
      product_affinity: await this.predictProductAffinity(features),
      channel_preference: await this.predictChannelPreference(features)
    };

    return {
      opportunity_score: this.calculateCompositeScore(predictions),
      next_best_action: await this.determineNextBestAction(predictions),
      personalization_strategy: await this.generatePersonalizationStrategy(predictions),
      timing_recommendations: this.generateTimingRecommendations(predictions),
      conversion_probability: this.calculateConversionProbability(predictions)
    };
  }

  // Sistema de alertas preditivos
  configureProactiveAlerts() {
    return {
      revenue_at_risk: {
        threshold: 0.15, // 15% probability drop
        monitoring: 'continuous',
        escalation: ['sales_manager', 'revenue_ops'],
        actions: ['review_pipeline', 'intensify_outreach', 'discount_authorization']
      },
      churn_early_warning: {
        threshold: 0.4, // 40% churn probability
        lookAhead: '30_days',
        escalation: ['customer_success', 'account_manager'],
        actions: ['health_check_call', 'value_reinforcement', 'retention_offer']
      },
      expansion_opportunity: {
        threshold: 0.7, // 70% expansion probability
        conditions: ['usage_growth', 'engagement_increase', 'positive_sentiment'],
        escalation: ['account_manager', 'sales_rep'],
        actions: ['expansion_conversation', 'demo_additional_features', 'custom_proposal']
      },
      competitive_threat: {
        indicators: ['decreased_engagement', 'pricing_inquiries', 'feature_comparisons'],
        urgency: 'high',
        escalation: ['sales_director', 'product_team'],
        actions: ['competitive_analysis', 'value_demonstration', 'retention_strategy']
      }
    };
  }
}

// Engine de Validação e Calibração
class ModelValidationEngine {
  constructor() {
    this.backtester = new Backtester();
    this.crossValidator = new CrossValidator();
    this.driftDetector = new DriftDetector();
    this.performanceTracker = new PerformanceTracker();
  }

  // Validação contínua de modelos
  async validateModelPerformance(modelName, timeframe = '30d') {
    const validationResults = {
      accuracy_metrics: await this.calculateAccuracyMetrics(modelName, timeframe),
      prediction_drift: await this.detectPredictionDrift(modelName, timeframe),
      feature_importance: await this.analyzeFeatureImportance(modelName),
      calibration_analysis: await this.analyzeCalibration(modelName),
      business_impact: await this.measureBusinessImpact(modelName, timeframe)
    };

    const overallScore = this.calculateOverallScore(validationResults);
    
    if (overallScore < 0.75) {
      await this.triggerModelRetraining(modelName);
    }

    return {
      validation_score: overallScore,
      detailed_results: validationResults,
      recommendations: await this.generateImprovementRecommendations(validationResults),
      next_validation: this.scheduleNextValidation(overallScore)
    };
  }

  // Detecção de deriva de dados
  async detectDataDrift(modelName) {
    const currentData = await this.getCurrentData(modelName);
    const trainingData = await this.getTrainingData(modelName);
    
    const driftAnalysis = {
      statistical_drift: this.detectStatisticalDrift(currentData, trainingData),
      distribution_shift: this.detectDistributionShift(currentData, trainingData),
      concept_drift: this.detectConceptDrift(currentData, trainingData),
      covariate_shift: this.detectCovariateShift(currentData, trainingData)
    };

    const driftScore = this.calculateDriftScore(driftAnalysis);
    
    return {
      drift_detected: driftScore > 0.3,
      drift_score: driftScore,
      affected_features: this.identifyAffectedFeatures(driftAnalysis),
      severity: this.assessDriftSeverity(driftScore),
      recommended_actions: this.recommendDriftActions(driftAnalysis)
    };
  }
}

// Sistema de Insights Acionáveis
class ActionableInsightsEngine {
  constructor() {
    this.insightGenerator = new InsightGenerator();
    this.actionRecommender = new ActionRecommender();
    this.impactCalculator = new ImpactCalculator();
    this.prioritizer = new Prioritizer();
  }

  // Geração de insights acionáveis
  async generateActionableInsights(predictions, businessContext) {
    const insights = {
      opportunity_insights: await this.identifyOpportunities(predictions),
      risk_insights: await this.identifyRisks(predictions),
      efficiency_insights: await this.identifyEfficiencyGains(predictions),
      strategic_insights: await this.identifyStrategicShifts(predictions)
    };

    const actionableInsights = await Promise.all(
      Object.entries(insights).map(([category, categoryInsights]) =>
        this.enrichWithActions(categoryInsights, businessContext)
      )
    );

    return {
      insights: actionableInsights,
      priority_ranking: await this.prioritizeInsights(actionableInsights),
      impact_estimation: await this.estimateImpact(actionableInsights),
      implementation_roadmap: await this.createImplementationRoadmap(actionableInsights)
    };
  }

  // Recomendações específicas por cenário
  async generateScenarioRecommendations(scenario, predictions) {
    const recommendations = {
      revenue_acceleration: {
        condition: predictions.revenue_growth < 0.1,
        actions: [
          'intensify_high_probability_deals',
          'accelerate_pipeline_velocity',
          'optimize_pricing_strategy',
          'expand_successful_channels'
        ],
        expected_impact: '15-25% revenue increase',
        timeframe: '30-60 days'
      },
      churn_prevention: {
        condition: predictions.churn_rate > 0.05,
        actions: [
          'proactive_customer_outreach',
          'value_realization_sessions',
          'product_adoption_boost',
          'competitive_differentiation'
        ],
        expected_impact: '30-50% churn reduction',
        timeframe: '15-30 days'
      },
      expansion_maximization: {
        condition: predictions.expansion_opportunities > 10,
        actions: [
          'systematic_expansion_campaigns',
          'usage_based_recommendations',
          'executive_relationship_building',
          'roi_demonstration_sessions'
        ],
        expected_impact: '200-400% account expansion',
        timeframe: '60-90 days'
      },
      efficiency_optimization: {
        condition: predictions.conversion_efficiency < 0.2,
        actions: [
          'process_automation_enhancement',
          'lead_qualification_refinement',
          'sales_enablement_improvement',
          'technology_stack_optimization'
        ],
        expected_impact: '25-40% efficiency gain',
        timeframe: '45-75 days'
      }
    };

    const applicableRecommendations = Object.entries(recommendations)
      .filter(([_, rec]) => rec.condition)
      .map(([scenario, rec]) => ({
        scenario,
        ...rec,
        priority: this.calculateRecommendationPriority(rec, predictions)
      }));

    return applicableRecommendations.sort((a, b) => b.priority - a.priority);
  }

  // Automação de ações baseadas em predições
  async automatePreventiveActions(predictions, automationLevel = 'moderate') {
    const automationRules = {
      conservative: {
        churn_prevention: { threshold: 0.8, actions: ['alert_team'] },
        opportunity_capture: { threshold: 0.9, actions: ['notify_sales'] },
        risk_mitigation: { threshold: 0.7, actions: ['flag_review'] }
      },
      moderate: {
        churn_prevention: { threshold: 0.6, actions: ['alert_team', 'schedule_call'] },
        opportunity_capture: { threshold: 0.7, actions: ['notify_sales', 'warm_intro'] },
        risk_mitigation: { threshold: 0.5, actions: ['flag_review', 'escalate_manager'] }
      },
      aggressive: {
        churn_prevention: { threshold: 0.4, actions: ['alert_team', 'schedule_call', 'retention_offer'] },
        opportunity_capture: { threshold: 0.5, actions: ['notify_sales', 'warm_intro', 'demo_booking'] },
        risk_mitigation: { threshold: 0.3, actions: ['flag_review', 'escalate_manager', 'immediate_action'] }
      }
    };

    const rules = automationRules[automationLevel];
    const automatedActions = [];

    for (const [prediction, value] of Object.entries(predictions)) {
      const rule = rules[prediction];
      if (rule && value >= rule.threshold) {
        const executedActions = await this.executeAutomatedActions(rule.actions, value);
        automatedActions.push({
          prediction,
          value,
          actions: executedActions,
          timestamp: new Date().toISOString()
        });
      }
    }

    return {
      actions_executed: automatedActions,
      next_review: this.calculateNextReview(automatedActions),
      performance_tracking: await this.setupPerformanceTracking(automatedActions)
    };
  }
}

// Sistema de Otimização com IA Avançada
class AIOptimizationEngine {
  constructor() {
    this.geneticAlgorithm = new GeneticAlgorithm();
    this.bayesianOptimizer = new BayesianOptimizer();
    this.reinforcementLearner = new ReinforcementLearner();
    this.multiObjectiveOptimizer = new MultiObjectiveOptimizer();
    this.realTimeAdjuster = new RealTimeAdjuster();
  }

  // Configuração do sistema de otimização multi-objetivo
  async initializeOptimization() {
    const objectives = {
      primary: {
        revenue_maximization: { weight: 0.4, target: 'maximize' },
        conversion_rate: { weight: 0.3, target: 'maximize' },
        customer_satisfaction: { weight: 0.2, target: 'maximize' },
        operational_efficiency: { weight: 0.1, target: 'maximize' }
      },
      constraints: {
        budget_limit: { type: 'hard', value: 'monthly_budget' },
        resource_capacity: { type: 'soft', value: 'team_capacity' },
        compliance_requirements: { type: 'hard', value: 'regulatory_constraints' },
        brand_consistency: { type: 'soft', value: 'brand_guidelines' }
      },
      optimization_parameters: {
        exploration_rate: 0.2, // 20% exploration, 80% exploitation
        learning_rate: 0.001,
        convergence_threshold: 0.95,
        max_iterations: 1000,
        population_size: 50
      }
    };

    return this.setupOptimizationFramework(objectives);
  }

  // Otimização de campanhas WhatsApp em tempo real
  async optimizeCampaignPerformance(campaignData) {
    const optimizationSpace = {
      message_timing: {
        type: 'categorical',
        options: ['morning', 'afternoon', 'evening', 'night'],
        current: campaignData.current_timing
      },
      message_frequency: {
        type: 'continuous',
        range: [1, 10], // messages per week
        current: campaignData.current_frequency
      },
      personalization_level: {
        type: 'ordinal',
        levels: ['basic', 'intermediate', 'advanced', 'hyper_personalized'],
        current: campaignData.current_personalization
      },
      content_type: {
        type: 'categorical',
        options: ['text', 'image', 'video', 'audio', 'document'],
        current: campaignData.current_content_type
      },
      call_to_action: {
        type: 'categorical',
        options: ['soft_touch', 'direct_ask', 'time_limited', 'social_proof'],
        current: campaignData.current_cta
      }
    };

    const optimization = await this.bayesianOptimizer.optimize({
      space: optimizationSpace,
      objective: this.calculateCampaignObjective,
      n_iterations: 100,
      acquisition_function: 'expected_improvement'
    });

    return {
      optimal_configuration: optimization.best_params,
      expected_improvement: optimization.expected_gain,
      confidence_interval: optimization.confidence,
      a_b_test_plan: await this.generateABTestPlan(optimization),
      implementation_roadmap: await this.createImplementationPlan(optimization)
    };
  }

  // Otimização de sequências de nurturing
  async optimizeNurturingSequences(customerSegment) {
    const sequenceSpace = {
      sequence_length: { type: 'discrete', range: [3, 15] },
      message_intervals: { type: 'continuous', range: [1, 7] }, // days
      content_progression: { 
        type: 'sequence', 
        options: ['educational', 'social_proof', 'urgency', 'value_demo'] 
      },
      personalization_triggers: {
        type: 'multi_select',
        options: ['behavior', 'demographics', 'engagement', 'lifecycle_stage']
      },
      escalation_rules: {
        type: 'conditional',
        conditions: ['no_response', 'low_engagement', 'high_intent', 'objection']
      }
    };

    const segmentData = await this.getSegmentData(customerSegment);
    const currentPerformance = await this.getCurrentSequencePerformance(customerSegment);

    const optimization = await this.geneticAlgorithm.evolve({
      population_size: 50,
      generations: 200,
      mutation_rate: 0.1,
      crossover_rate: 0.8,
      fitness_function: this.calculateSequenceFitness,
      elite_size: 5
    });

    return {
      optimized_sequence: optimization.best_individual,
      performance_improvement: this.calculateImprovement(currentPerformance, optimization),
      rollout_strategy: await this.planRollout(optimization, customerSegment),
      monitoring_framework: await this.setupMonitoring(optimization)
    };
  }
}

// Engine de Aprendizado por Reforço
class ReinforcementOptimizer {
  constructor() {
    this.qLearningAgent = new QLearningAgent();
    this.policyGradientAgent = new PolicyGradientAgent();
    this.actorCriticAgent = new ActorCriticAgent();
    this.environmentSimulator = new EnvironmentSimulator();
  }

  // Otimização de políticas de engajamento
  async optimizeEngagementPolicy(customerProfiles) {
    const state_space = {
      customer_lifecycle_stage: ['prospect', 'trial', 'customer', 'expansion'],
      engagement_level: ['low', 'medium', 'high'],
      last_interaction: ['< 1d', '1-3d', '3-7d', '> 7d'],
      conversion_probability: ['< 0.2', '0.2-0.5', '0.5-0.8', '> 0.8'],
      channel_preference: ['whatsapp', 'email', 'phone', 'in_app']
    };

    const action_space = {
      message_type: ['educational', 'promotional', 'support', 'feedback'],
      timing: ['immediate', 'scheduled', 'optimal_window'],
      intensity: ['light_touch', 'moderate', 'intensive'],
      channel: ['whatsapp_only', 'multi_channel', 'preferred_channel'],
      personalization: ['none', 'basic', 'advanced', 'hyper_personal']
    };

    const reward_function = (state, action, next_state) => {
      const engagement_reward = this.calculateEngagementReward(state, action, next_state);
      const conversion_reward = this.calculateConversionReward(state, action, next_state);
      const efficiency_reward = this.calculateEfficiencyReward(state, action, next_state);
      
      return 0.5 * engagement_reward + 0.3 * conversion_reward + 0.2 * efficiency_reward;
    };

    const policy = await this.actorCriticAgent.train({
      state_space,
      action_space,
      reward_function,
      episodes: 10000,
      learning_rate: 0.001,
      discount_factor: 0.95
    });

    return {
      optimal_policy: policy,
      policy_performance: await this.evaluatePolicy(policy),
      deployment_plan: await this.createDeploymentPlan(policy),
      continuous_learning: await this.setupContinuousLearning(policy)
    };
  }

  // Otimização dinâmica de pricing
  async optimizeDynamicPricing(marketConditions) {
    const pricing_features = {
      customer_segment: await this.extractCustomerFeatures(),
      market_conditions: await this.extractMarketFeatures(),
      competitive_landscape: await this.extractCompetitiveFeatures(),
      demand_patterns: await this.extractDemandFeatures(),
      inventory_status: await this.extractInventoryFeatures()
    };

    const pricing_actions = {
      base_price_adjustment: { range: [-0.3, 0.3] }, // -30% to +30%
      discount_strategy: ['none', 'early_bird', 'volume', 'loyalty', 'competitive'],
      bundling_options: ['single', 'basic_bundle', 'premium_bundle', 'custom'],
      payment_terms: ['monthly', 'quarterly', 'annual', 'custom'],
      trial_extensions: ['none', '7_days', '14_days', '30_days']
    };

    const pricing_policy = await this.policyGradientAgent.optimize({
      features: pricing_features,
      actions: pricing_actions,
      objective: this.maximizeRevenueObjective,
      constraints: [
        this.maintainMarginConstraint,
        this.competitivePositionConstraint,
        this.customerValueConstraint
      ],
      episodes: 5000
    });

    return {
      optimal_pricing_policy: pricing_policy,
      revenue_impact: await this.calculateRevenueImpact(pricing_policy),
      market_response: await this.simulateMarketResponse(pricing_policy),
      implementation_guide: await this.createPricingImplementationGuide(pricing_policy)
    };
  }
}

// Sistema AutoML para Otimização Contínua
class AutoMLOptimizer {
  constructor() {
    this.modelSelector = new ModelSelector();
    this.hyperparameterTuner = new HyperparameterTuner();
    this.featureSelector = new FeatureSelector();
    this.pipelineOptimizer = new PipelineOptimizer();
    this.performanceTracker = new PerformanceTracker();
  }

  // AutoML para otimização de modelos de conversão
  async autoOptimizeConversionModels() {
    const optimization_pipeline = {
      data_preprocessing: {
        missing_value_strategies: ['mean', 'median', 'mode', 'knn', 'iterative'],
        scaling_methods: ['standard', 'minmax', 'robust', 'quantile'],
        feature_encoding: ['onehot', 'label', 'target', 'binary', 'hash'],
        outlier_handling: ['iqr', 'isolation_forest', 'local_outlier', 'zscore']
      },
      feature_engineering: {
        polynomial_features: { degree_range: [2, 4] },
        interaction_features: { max_combinations: 3 },
        temporal_features: ['lag', 'rolling_stats', 'seasonal_decompose'],
        text_features: ['tfidf', 'word2vec', 'bert_embeddings'],
        behavioral_features: ['sequence_patterns', 'frequency_analysis', 'recency_features']
      },
      model_selection: {
        algorithms: [
          'gradient_boosting', 'random_forest', 'neural_networks',
          'svm', 'logistic_regression', 'naive_bayes', 'ensemble_methods'
        ],
        hyperparameter_spaces: this.defineHyperparameterSpaces(),
        cross_validation: { folds: 5, strategy: 'time_series_split' },
        optimization_metric: 'f1_weighted'
      },
      ensemble_methods: {
        stacking: { meta_learner: 'xgboost', base_models: 3 },
        blending: { weights_optimization: 'bayesian' },
        voting: { strategy: 'soft', models: 5 }
      }
    };

    const optimization_results = await this.runAutoMLPipeline(optimization_pipeline);

    return {
      best_model: optimization_results.champion_model,
      performance_metrics: optimization_results.validation_scores,
      feature_importance: optimization_results.feature_rankings,
      model_explanation: await this.generateModelExplanation(optimization_results),
      deployment_package: await this.createDeploymentPackage(optimization_results)
    };
  }

  // Otimização automática de hiperparâmetros
  async autoTuneHyperparameters(model_type, objective_function) {
    const tuning_strategies = {
      bayesian_optimization: {
        acquisition_function: 'expected_improvement',
        n_iterations: 100,
        exploration_weight: 0.01
      },
      genetic_algorithm: {
        population_size: 50,
        generations: 100,
        mutation_rate: 0.1,
        crossover_rate: 0.8
      },
      random_search: {
        n_iterations: 200,
        random_state: 42
      },
      grid_search: {
        exhaustive: false,
        intelligent_sampling: true
      }
    };

    const hyperparameter_spaces = this.getHyperparameterSpace(model_type);
    
    const tuning_results = await Promise.all([
      this.bayesianOptimizer.optimize(hyperparameter_spaces, objective_function),
      this.geneticAlgorithm.optimize(hyperparameter_spaces, objective_function),
      this.randomSearch.optimize(hyperparameter_spaces, objective_function)
    ]);

    const best_configuration = this.selectBestConfiguration(tuning_results);

    return {
      optimal_hyperparameters: best_configuration.params,
      performance_improvement: best_configuration.improvement,
      tuning_history: tuning_results,
      sensitivity_analysis: await this.analyzeSensitivity(best_configuration),
      production_config: await this.generateProductionConfig(best_configuration)
    };
  }

  // Sistema de otimização contínua
  async setupContinuousOptimization() {
    const monitoring_framework = {
      performance_drift_detection: {
        metrics: ['accuracy', 'precision', 'recall', 'f1_score'],
        thresholds: { warning: 0.05, critical: 0.1 },
        monitoring_window: '7d',
        statistical_tests: ['ks_test', 'mann_whitney', 'chi_square']
      },
      data_drift_detection: {
        features: 'all_model_features',
        drift_detection_methods: ['psi', 'js_divergence', 'wasserstein_distance'],
        alert_thresholds: { moderate: 0.1, severe: 0.25 },
        comparison_window: '30d'
      },
      concept_drift_detection: {
        target_variable: 'conversion_rate',
        detection_methods: ['adwin', 'page_hinkley', 'ddm'],
        adaptation_strategies: ['retrain', 'incremental_update', 'ensemble_update']
      },
      automated_retraining: {
        triggers: ['performance_drop', 'data_drift', 'concept_drift', 'scheduled'],
        retraining_data_window: '90d',
        validation_strategy: 'temporal_holdout',
        deployment_criteria: { min_improvement: 0.02, confidence_level: 0.95 }
      }
    };

    return {
      monitoring_setup: monitoring_framework,
      automated_workflows: await this.createAutomatedWorkflows(monitoring_framework),
      alert_configuration: await this.setupAlertSystem(monitoring_framework),
      governance_framework: await this.establishGovernance(monitoring_framework)
    };
  }
}

// Otimização em Tempo Real
class RealTimeOptimizer {
  constructor() {
    this.streamProcessor = new StreamProcessor();
    this.onlineOptimizer = new OnlineOptimizer();
    this.adaptiveController = new AdaptiveController();
    this.performanceMonitor = new PerformanceMonitor();
  }

  // Otimização adaptativa de experiências
  async optimizeUserExperienceRealTime(userSession) {
    const context = await this.extractSessionContext(userSession);
    const currentState = await this.getCurrentOptimizationState();
    
    const optimization_decisions = {
      message_timing: await this.optimizeMessageTiming(context),
      content_personalization: await this.optimizeContentPersonalization(context),
      interaction_flow: await this.optimizeInteractionFlow(context),
      escalation_strategy: await this.optimizeEscalationStrategy(context),
      next_best_action: await this.determineNextBestAction(context)
    };

    const real_time_adjustments = await this.makeRealTimeAdjustments(
      optimization_decisions,
      currentState,
      context
    );

    return {
      optimized_experience: real_time_adjustments,
      performance_prediction: await this.predictPerformanceImpact(real_time_adjustments),
      feedback_collection: await this.setupFeedbackCollection(real_time_adjustments),
      learning_update: await this.updateLearningModels(real_time_adjustments, context)
    };
  }

  // Otimização de alocação de recursos
  async optimizeResourceAllocation(currentDemand, availableResources) {
    const allocation_problem = {
      resources: {
        sales_agents: { capacity: availableResources.agents, cost_per_hour: 50 },
        automated_responses: { capacity: 'unlimited', cost_per_interaction: 0.1 },
        escalation_specialists: { capacity: availableResources.specialists, cost_per_hour: 80 },
        ai_processing: { capacity: availableResources.ai_credits, cost_per_request: 0.01 }
      },
      demand_patterns: {
        high_value_leads: currentDemand.high_value,
        standard_inquiries: currentDemand.standard,
        support_requests: currentDemand.support,
        escalated_issues: currentDemand.escalated
      },
      optimization_objectives: {
        maximize_revenue: { weight: 0.4 },
        minimize_cost: { weight: 0.3 },
        maximize_satisfaction: { weight: 0.2 },
        minimize_response_time: { weight: 0.1 }
      }
    };

    const optimal_allocation = await this.solveAllocationProblem(allocation_problem);

    return {
      resource_assignments: optimal_allocation.assignments,
      expected_performance: optimal_allocation.performance_metrics,
      cost_efficiency: optimal_allocation.cost_analysis,
      dynamic_adjustments: await this.setupDynamicAdjustments(optimal_allocation)
    };
  }

  // Sistema de feedback loop para otimização contínua
  async establishFeedbackLoop() {
    const feedback_components = {
      performance_measurement: {
        real_time_metrics: ['response_time', 'conversion_rate', 'satisfaction_score'],
        batch_metrics: ['revenue_impact', 'cost_effectiveness', 'long_term_value'],
        measurement_frequency: { real_time: '1min', batch: '1hour' }
      },
      learning_mechanisms: {
        online_learning: {
          algorithms: ['stochastic_gradient_descent', 'adaptive_learning_rate'],
          update_frequency: 'per_interaction',
          learning_rate_schedule: 'exponential_decay'
        },
        batch_learning: {
          algorithms: ['ensemble_retraining', 'transfer_learning'],
          update_frequency: 'daily',
          data_window: '30d'
        },
        meta_learning: {
          learn_to_optimize: true,
          adaptation_speed: 'fast',
          generalization_capability: 'high'
        }
      },
      adaptation_strategies: {
        immediate_adjustments: {
          triggers: ['performance_drop', 'new_pattern_detected'],
          adjustment_magnitude: 'conservative',
          rollback_capability: true
        },
        gradual_improvements: {
          optimization_schedule: 'continuous',
          improvement_targets: { daily: 0.1, weekly: 0.5, monthly: 2.0 },
          stability_requirements: 'high'
        }
      }
    };

    return {
      feedback_system: feedback_components,
      monitoring_dashboard: await this.createMonitoringDashboard(feedback_components),
      alert_mechanisms: await this.setupAlertMechanisms(feedback_components),
      governance_controls: await this.establishGovernanceControls(feedback_components)
    };
  }
}

// Sistema de Relatórios Automatizados Inteligentes
class AutomatedReportingEngine {
  constructor() {
    this.dataAggregator = new DataAggregator();
    this.narrativeGenerator = new NarrativeGenerator();
    this.visualizationEngine = new VisualizationEngine();
    this.distributionManager = new DistributionManager();
    this.templateManager = new TemplateManager();
    this.scheduleManager = new ScheduleManager();
  }

  // Configuração de relatórios personalizados por perfil
  async setupPersonalizedReports() {
    const reportProfiles = {
      executive_dashboard: {
        frequency: 'daily',
        delivery_time: '08:00',
        format: ['pdf_executive', 'mobile_dashboard'],
        content: {
          summary: 'high_level_kpis',
          deep_dive: 'revenue_performance',
          alerts: 'critical_only',
          recommendations: 'strategic_actions'
        },
        length: 'concise', // 2-3 pages max
        tone: 'executive'
      },
      sales_team_report: {
        frequency: 'weekly',
        delivery_time: 'monday_09:00',
        format: ['interactive_dashboard', 'pdf_detailed'],
        content: {
          summary: 'pipeline_health',
          deep_dive: 'individual_performance',
          alerts: 'opportunity_risks',
          recommendations: 'tactical_actions'
        },
        length: 'detailed', // 8-12 pages
        tone: 'actionable'
      },
      marketing_analytics: {
        frequency: 'bi_weekly',
        delivery_time: 'friday_16:00',
        format: ['html_interactive', 'csv_data_export'],
        content: {
          summary: 'campaign_performance',
          deep_dive: 'attribution_analysis',
          alerts: 'budget_optimization',
          recommendations: 'campaign_adjustments'
        },
        length: 'comprehensive', // 15-20 pages
        tone: 'analytical'
      },
      board_presentation: {
        frequency: 'monthly',
        delivery_time: 'last_friday_eod',
        format: ['powerpoint_presentation', 'pdf_appendix'],
        content: {
          summary: 'business_overview',
          deep_dive: 'strategic_metrics',
          alerts: 'major_risks_opportunities',
          recommendations: 'strategic_initiatives'
        },
        length: 'presentation', // 25-30 slides
        tone: 'strategic'
      }
    };

    return this.initializeReportingFramework(reportProfiles);
  }

  // Geração automática de narrativas contextuais
  async generateContextualNarrative(data, audience, timeframe) {
    const narrative_components = {
      executive_summary: await this.generateExecutiveSummary(data, audience),
      key_insights: await this.extractKeyInsights(data, timeframe),
      performance_analysis: await this.analyzePerformanceTrends(data),
      anomaly_explanation: await this.explainAnomalies(data),
      predictive_insights: await this.generatePredictiveInsights(data),
      action_recommendations: await this.generateActionRecommendations(data, audience)
    };

    const narrative_structure = {
      opening: this.craftOpening(narrative_components, audience),
      body_sections: this.organizeSections(narrative_components, audience),
      conclusion: this.craftConclusion(narrative_components, audience),
      appendix: this.compileAppendix(narrative_components)
    };

    return this.synthesizeNarrative(narrative_structure, audience);
  }

  // Sistema de distribuição inteligente
  async setupIntelligentDistribution() {
    const distribution_logic = {
      timing_optimization: {
        audience_analysis: await this.analyzeAudiencePreferences(),
        engagement_patterns: await this.identifyEngagementPatterns(),
        optimal_delivery_windows: await this.calculateOptimalTiming(),
        timezone_awareness: await this.setupTimezoneHandling()
      },
      format_personalization: {
        device_preferences: await this.analyzeDeviceUsage(),
        consumption_patterns: await this.analyzeConsumptionBehavior(),
        accessibility_requirements: await this.identifyAccessibilityNeeds(),
        interaction_preferences: await this.analyzeInteractionPreferences()
      },
      content_adaptation: {
        attention_span_analysis: await this.analyzeAttentionSpans(),
        information_density_preferences: await this.analyzeInformationPreferences(),
        visual_vs_textual_preferences: await this.analyzeContentPreferences(),
        detail_level_requirements: await this.analyzeDetailRequirements()
      }
    };

    return this.implementDistributionStrategy(distribution_logic);
  }
}

// Engine de Visualização Automática
class AutoVisualizationEngine {
  constructor() {
    this.chartSelector = new ChartSelector();
    this.colorPalette = new ColorPaletteManager();
    this.layoutOptimizer = new LayoutOptimizer();
    this.interactivityEngine = new InteractivityEngine();
  }

  // Seleção automática de visualizações baseada em dados
  async selectOptimalVisualizations(dataSet, audience, context) {
    const visualization_rules = {
      time_series_data: {
        primary: 'line_chart_with_trend',
        secondary: 'sparklines_summary',
        interactive: 'zoomable_time_navigator',
        mobile: 'simplified_trend_cards'
      },
      categorical_comparison: {
        primary: 'horizontal_bar_chart',
        secondary: 'donut_chart_breakdown',
        interactive: 'sortable_data_table',
        mobile: 'compact_metric_cards'
      },
      hierarchical_data: {
        primary: 'treemap_visualization',
        secondary: 'sunburst_chart',
        interactive: 'drill_down_table',
        mobile: 'nested_accordion_view'
      },
      correlation_analysis: {
        primary: 'scatter_plot_matrix',
        secondary: 'correlation_heatmap',
        interactive: 'interactive_scatter_plot',
        mobile: 'correlation_summary_cards'
      },
      geographic_data: {
        primary: 'choropleth_map',
        secondary: 'bubble_map',
        interactive: 'zoomable_map_layers',
        mobile: 'geographic_summary_list'
      },
      funnel_analysis: {
        primary: 'conversion_funnel_chart',
        secondary: 'step_analysis_bars',
        interactive: 'clickable_funnel_segments',
        mobile: 'vertical_funnel_steps'
      }
    };

    const selected_visualizations = await this.analyzeDataAndSelectCharts(
      dataSet, 
      visualization_rules, 
      audience, 
      context
    );

    return {
      recommended_charts: selected_visualizations,
      layout_suggestions: await this.generateLayoutSuggestions(selected_visualizations),
      accessibility_enhancements: await this.addAccessibilityFeatures(selected_visualizations),
      responsive_adaptations: await this.createResponsiveAdaptations(selected_visualizations)
    };
  }

  // Geração automática de dashboards
  async generateAutomatedDashboard(reportData, userProfile) {
    const dashboard_layout = await this.optimizeLayoutForProfile(userProfile);
    const widget_selection = await this.selectOptimalWidgets(reportData, userProfile);
    const interaction_patterns = await this.defineInteractionPatterns(userProfile);

    const dashboard_config = {
      layout: {
        grid_system: dashboard_layout.grid,
        responsive_breakpoints: dashboard_layout.breakpoints,
        widget_priorities: dashboard_layout.priorities,
        white_space_optimization: dashboard_layout.spacing
      },
      widgets: {
        primary_metrics: widget_selection.primary,
        secondary_metrics: widget_selection.secondary,
        trend_indicators: widget_selection.trends,
        alert_components: widget_selection.alerts
      },
      interactivity: {
        drill_down_paths: interaction_patterns.drill_downs,
        filter_controls: interaction_patterns.filters,
        export_options: interaction_patterns.exports,
        sharing_mechanisms: interaction_patterns.sharing
      },
      personalization: {
        customizable_elements: await this.identifyCustomizableElements(),
        user_preference_tracking: await this.setupPreferenceTracking(),
        adaptive_layout: await this.enableAdaptiveLayout(),
        learning_system: await this.implementLearningSystem()
      }
    };

    return this.buildDashboard(dashboard_config);
  }
}

// Sistema de Insights Inteligentes
class IntelligentInsightsEngine {
  constructor() {
    this.patternRecognizer = new PatternRecognizer();
    this.anomalyDetector = new AnomalyDetector();
    this.trendAnalyzer = new TrendAnalyzer();
    this.correlationAnalyzer = new CorrelationAnalyzer();
    this.narrativeGenerator = new NarrativeGenerator();
  }

  // Detecção automática de insights acionáveis
  async detectActionableInsights(reportData, businessContext) {
    const insight_categories = {
      performance_insights: {
        trend_analysis: await this.analyzeTrends(reportData.time_series_data),
        performance_gaps: await this.identifyPerformanceGaps(reportData.kpi_data),
        efficiency_opportunities: await this.findEfficiencyOpportunities(reportData.process_data),
        competitive_advantages: await this.identifyCompetitiveAdvantages(reportData.market_data)
      },
      risk_insights: {
        early_warning_signals: await this.detectEarlyWarnings(reportData.leading_indicators),
        vulnerability_assessment: await this.assessVulnerabilities(reportData.risk_factors),
        scenario_analysis: await this.performScenarioAnalysis(reportData.simulation_data),
        mitigation_strategies: await this.generateMitigationStrategies(reportData.risk_data)
      },
      opportunity_insights: {
        growth_opportunities: await this.identifyGrowthOpportunities(reportData.market_data),
        optimization_potential: await this.calculateOptimizationPotential(reportData.efficiency_data),
        expansion_possibilities: await this.evaluateExpansionPossibilities(reportData.customer_data),
        innovation_areas: await this.suggestInnovationAreas(reportData.technology_data)
      },
      strategic_insights: {
        market_positioning: await this.analyzeMarketPositioning(reportData.competitive_data),
        resource_allocation: await this.optimizeResourceAllocation(reportData.resource_data),
        capability_gaps: await this.identifyCapabilityGaps(reportData.capability_data),
        strategic_initiatives: await this.recommendStrategicInitiatives(reportData.strategic_data)
      }
    };

    const prioritized_insights = await this.prioritizeInsights(insight_categories, businessContext);
    const actionable_recommendations = await this.generateRecommendations(prioritized_insights);

    return {
      insights: prioritized_insights,
      recommendations: actionable_recommendations,
      impact_assessment: await this.assessImpact(actionable_recommendations),
      implementation_roadmap: await this.createImplementationRoadmap(actionable_recommendations)
    };
  }

  // Geração automática de explicações
  async generateAutomaticExplanations(data_points, audience_level) {
    const explanation_framework = {
      statistical_explanations: {
        correlation_explanations: await this.explainCorrelations(data_points.correlations),
        trend_explanations: await this.explainTrends(data_points.trends),
        variance_explanations: await this.explainVariances(data_points.variances),
        outlier_explanations: await this.explainOutliers(data_points.outliers)
      },
      business_explanations: {
        performance_drivers: await this.identifyPerformanceDrivers(data_points.performance_data),
        cause_effect_relationships: await this.mapCauseEffectRelationships(data_points.causal_data),
        market_influences: await this.explainMarketInfluences(data_points.market_data),
        operational_factors: await this.identifyOperationalFactors(data_points.operational_data)
      },
      contextual_explanations: {
        historical_context: await this.provideHistoricalContext(data_points.historical_data),
        industry_benchmarks: await this.compareIndustryBenchmarks(data_points.benchmark_data),
        seasonal_factors: await this.explainSeasonalFactors(data_points.seasonal_data),
        external_influences: await this.identifyExternalInfluences(data_points.external_data)
      }
    };

    const audience_adapted_explanations = await this.adaptToAudience(
      explanation_framework,
      audience_level
    );

    return {
      explanations: audience_adapted_explanations,
      supporting_evidence: await this.gatherSupportingEvidence(audience_adapted_explanations),
      visual_aids: await this.createVisualAids(audience_adapted_explanations),
      further_reading: await this.suggestFurtherReading(audience_adapted_explanations)
    };
  }

  // Sistema de alertas inteligentes em relatórios
  async setupIntelligentAlerting() {
    const alerting_system = {
      threshold_based_alerts: {
        static_thresholds: await this.defineStaticThresholds(),
        dynamic_thresholds: await this.calculateDynamicThresholds(),
        adaptive_thresholds: await this.implementAdaptiveThresholds(),
        contextual_thresholds: await this.setupContextualThresholds()
      },
      pattern_based_alerts: {
        trend_pattern_alerts: await this.setupTrendPatternAlerts(),
        anomaly_pattern_alerts: await this.setupAnomalyPatternAlerts(),
        seasonal_pattern_alerts: await this.setupSeasonalPatternAlerts(),
        correlation_pattern_alerts: await this.setupCorrelationPatternAlerts()
      },
      predictive_alerts: {
        forecast_based_alerts: await this.setupForecastAlerts(),
        risk_prediction_alerts: await this.setupRiskPredictionAlerts(),
        opportunity_prediction_alerts: await this.setupOpportunityPredictionAlerts(),
        performance_prediction_alerts: await this.setupPerformancePredictionAlerts()
      },
      business_rule_alerts: {
        compliance_alerts: await this.setupComplianceAlerts(),
        budget_variance_alerts: await this.setupBudgetVarianceAlerts(),
        performance_target_alerts: await this.setupPerformanceTargetAlerts(),
        strategic_goal_alerts: await this.setupStrategicGoalAlerts()
      }
    };

    return {
      alert_configuration: alerting_system,
      notification_preferences: await this.setupNotificationPreferences(),
      escalation_procedures: await this.defineEscalationProcedures(),
      alert_fatigue_prevention: await this.implementAlertFatiguePrevention()
    };
  }
}

// Sistema de Automação Completa
class ReportAutomationOrchestrator {
  constructor() {
    this.dataCollector = new DataCollector();
    this.reportGenerator = new ReportGenerator();
    this.qualityChecker = new QualityChecker();
    this.distributionEngine = new DistributionEngine();
    this.feedbackCollector = new FeedbackCollector();
  }

  // Orquestração completa do processo automatizado
  async orchestrateAutomatedReporting() {
    const automation_workflow = {
      data_collection: {
        scheduled_extraction: await this.scheduleDataExtraction(),
        real_time_streaming: await this.setupRealTimeStreaming(),
        data_validation: await this.implementDataValidation(),
        quality_assurance: await this.setupQualityAssurance()
      },
      report_generation: {
        template_selection: await this.automateTemplateSelection(),
        content_generation: await this.automateContentGeneration(),
        visualization_creation: await this.automateVisualizationCreation(),
        narrative_generation: await this.automateNarrativeGeneration()
      },
      quality_control: {
        automated_testing: await this.setupAutomatedTesting(),
        content_validation: await this.implementContentValidation(),
        accessibility_checking: await this.setupAccessibilityChecking(),
        performance_validation: await this.implementPerformanceValidation()
      },
      distribution_management: {
        audience_segmentation: await this.automateAudienceSegmentation(),
        delivery_optimization: await this.optimizeDeliveryTiming(),
        format_personalization: await this.personalizeFormats(),
        delivery_confirmation: await this.trackDeliveryConfirmation()
      },
      feedback_integration: {
        usage_analytics: await this.trackUsageAnalytics(),
        engagement_measurement: await this.measureEngagement(),
        feedback_collection: await this.collectFeedback(),
        continuous_improvement: await this.implementContinuousImprovement()
      }
    };

    return this.executeAutomationWorkflow(automation_workflow);
  }

  // Sistema de controle de qualidade automatizado
  async implementAutomaticQualityControl() {
    const quality_checks = {
      data_quality: {
        completeness_check: (data) => this.checkDataCompleteness(data),
        accuracy_validation: (data) => this.validateDataAccuracy(data),
        consistency_verification: (data) => this.verifyDataConsistency(data),
        timeliness_assessment: (data) => this.assessDataTimeliness(data)
      },
      content_quality: {
        narrative_coherence: (content) => this.checkNarrativeCoherence(content),
        insight_relevance: (insights) => this.validateInsightRelevance(insights),
        recommendation_feasibility: (recommendations) => this.assessRecommendationFeasibility(recommendations),
        language_quality: (text) => this.validateLanguageQuality(text)
      },
      visual_quality: {
        chart_appropriateness: (charts) => this.validateChartAppropriatenessfromdata(charts),
        color_accessibility: (visuals) => this.checkColorAccessibility(visuals),
        layout_optimization: (layout) => this.optimizeLayoutQuality(layout),
        mobile_compatibility: (design) => this.ensureMobileCompatibility(design)
      },
      technical_quality: {
        performance_benchmarks: (report) => this.benchmarkPerformance(report),
        cross_platform_compatibility: (report) => this.testCrossPlatformCompatibility(report),
        security_compliance: (report) => this.validateSecurityCompliance(report),
        scalability_assessment: (system) => this.assessScalability(system)
      }
    };

    return {
      quality_framework: quality_checks,
      automated_testing_suite: await this.createAutomatedTestingSuite(quality_checks),
      continuous_monitoring: await this.setupContinuousMonitoring(quality_checks),
      improvement_feedback_loop: await this.establishImprovementFeedbackLoop(quality_checks)
    };
  }

  // Personalização avançada baseada em ML
  async implementMLPersonalization() {
    const personalization_models = {
      content_preference_model: {
        features: ['reading_time', 'interaction_patterns', 'content_types_engaged'],
        algorithm: 'collaborative_filtering',
        update_frequency: 'daily',
        personalization_scope: 'content_selection_and_ordering'
      },
      timing_optimization_model: {
        features: ['open_times', 'engagement_windows', 'timezone_preferences'],
        algorithm: 'time_series_analysis',
        update_frequency: 'weekly',
        personalization_scope: 'delivery_timing'
      },
      format_preference_model: {
        features: ['device_usage', 'format_interactions', 'accessibility_needs'],
        algorithm: 'decision_tree',
        update_frequency: 'monthly',
        personalization_scope: 'format_selection'
      },
      detail_level_model: {
        features: ['role_seniority', 'expertise_level', 'time_constraints'],
        algorithm: 'neural_network',
        update_frequency: 'bi_weekly',
        personalization_scope: 'information_density'
      }
    };

    const personalization_engine = await this.buildPersonalizationEngine(personalization_models);

    return {
      personalization_models: personalization_models,
      recommendation_engine: personalization_engine,
      adaptive_learning: await this.setupAdaptiveLearning(personalization_engine),
      privacy_compliance: await this.ensurePrivacyCompliance(personalization_engine)
    };
  }
}