KI_Bot_Ecosystem:
  Customer_Facing_Bots:
    - Chatbots (Web, WhatsApp, Teams)
    - Voicebots (Telefon, Smart Speaker)
    - Social Media Bots
    - E-Commerce Bots

  Internal_Process_Bots:
    - HR Bots (Recruiting, Onboarding)
    - Finance Bots (Rechnungsverarbeitung)
    - IT Support Bots
    - Knowledge Management Bots

  Specialized_Bots:
    - Sales Bots (Lead Qualification)
    - Marketing Bots (Campaign Management)
    - Compliance Bots (Regulatory Monitoring)
    - Analytics Bots (Report Generation)

  Integration_Layer:
    - API Connectors
    - Database Integrations
    - ERP/CRM Connections
    - Workflow Orchestration

# Enterprise Bot Strategy Framework
class EnterpriseBotStrategy:
    def __init__(self):
        self.business_analyzer = BusinessProcessAnalyzer()
        self.roi_calculator = BotROICalculator()
        self.implementation_planner = ImplementationPlanner()

    def develop_bot_strategy(self, company_profile):
        """
        Entwicklung einer unternehmensweiten Bot-Strategie
        """
        # 1. Business Process Analysis
        process_analysis = self.business_analyzer.analyze_processes(company_profile)

        # 2. Bot Opportunity Identification
        bot_opportunities = self.identify_bot_opportunities(process_analysis)

        # 3. ROI-basierte Priorisierung
        prioritized_bots = self.prioritize_by_roi(bot_opportunities)

        # 4. Implementation Roadmap
        roadmap = self.implementation_planner.create_roadmap(prioritized_bots)

        return {
            'strategy_overview': self.create_strategy_overview(prioritized_bots),
            'implementation_phases': roadmap['phases'],
            'expected_roi': roadmap['roi_projections'],
            'resource_requirements': roadmap['resources'],
            'timeline': roadmap['timeline']
        }

    def identify_bot_opportunities(self, process_analysis):
        """
        Identifikation von Bot-Einsatzmöglichkeiten
        """
        opportunities = []

        for process in process_analysis['processes']:
            if self.is_suitable_for_bot_automation(process):
                bot_opportunity = {
                    'process_name': process['name'],
                    'process_volume': process['monthly_volume'],
                    'current_cost': process['monthly_cost'],
                    'automation_potential': process['automation_score'],
                    'recommended_bot_type': self.recommend_bot_type(process),
                    'expected_savings': self.calculate_expected_savings(process),
                    'implementation_complexity': self.assess_complexity(process)
                }
                opportunities.append(bot_opportunity)

        return opportunities

    def recommend_bot_type(self, process):
        """
        Empfehlung des optimalen Bot-Typs für einen Prozess
        """
        if process['involves_customer_interaction']:
            if process['communication_channel'] == 'phone':
                return 'voicebot'
            elif process['communication_channel'] == 'text':
                return 'chatbot'
            else:
                return 'omnichannel_bot'
        elif process['involves_data_processing']:
            return 'process_bot'
        elif process['involves_knowledge_work']:
            return 'knowledge_bot'
        else:
            return 'workflow_bot'

# Multi-Bot Orchestration Platform
class BotOrchestrationPlatform:
    def __init__(self):
        self.bot_registry = BotRegistry()
        self.workflow_engine = WorkflowEngine()
        self.conversation_manager = ConversationManager()
        self.analytics_engine = BotAnalyticsEngine()

    def orchestrate_bot_interaction(self, user_request, context):
        """
        Intelligente Orchestrierung mehrerer Bots
        """
        # 1. Intent Classification
        intent_analysis = self.classify_user_intent(user_request)

        # 2. Bot Selection
        optimal_bot = self.select_optimal_bot(intent_analysis, context)

        # 3. Context Transfer
        if context.get('previous_bot'):
            self.transfer_context(context['previous_bot'], optimal_bot, context)

        # 4. Bot Execution
        bot_response = optimal_bot.process_request(user_request, context)

        # 5. Response Optimization
        optimized_response = self.optimize_response(bot_response, context)

        # 6. Analytics Tracking
        self.analytics_engine.track_bot_interaction({
            'user_id': context['user_id'],
            'bot_used': optimal_bot.id,
            'intent': intent_analysis['intent'],
            'satisfaction': optimized_response.get('satisfaction_score'),
            'resolution_time': optimized_response['processing_time']
        })

        return optimized_response

    def select_optimal_bot(self, intent_analysis, context):
        """
        Auswahl des optimalen Bots basierend auf Intent und Kontext
        """
        available_bots = self.bot_registry.get_bots_for_intent(intent_analysis['intent'])

        # Bot-Performance-basierte Auswahl
        bot_scores = {}
        for bot in available_bots:
            performance_metrics = self.analytics_engine.get_bot_performance(bot.id)

            score = (
                performance_metrics['success_rate'] * 0.4 +
                performance_metrics['user_satisfaction'] * 0.3 +
                performance_metrics['response_time_score'] * 0.2 +
                performance_metrics['escalation_rate'] * 0.1
            )

            bot_scores[bot.id] = score

        # Besten Bot auswählen
        optimal_bot_id = max(bot_scores, key=bot_scores.get)
        return self.bot_registry.get_bot(optimal_bot_id)

# E-Commerce Bot Ecosystem
class ECommerceBotEcosystem:
    def __init__(self):
        self.product_bot = ProductRecommendationBot()
        self.support_bot = CustomerSupportBot()
        self.sales_bot = SalesAssistantBot()
        self.order_bot = OrderManagementBot()

    def deploy_ecommerce_bots(self, shop_config):
        """
        Deployment eines kompletten E-Commerce Bot-Ecosystems
        """
        # Product Discovery Bot
        product_bot_config = {
            'product_catalog': shop_config['product_catalog'],
            'recommendation_engine': 'collaborative_filtering',
            'personalization': True,
            'integration_points': ['website', 'mobile_app', 'whatsapp']
        }

        # Customer Support Bot
        support_bot_config = {
            'knowledge_base': shop_config['faq_database'],
            'escalation_rules': shop_config['support_escalation'],
            'integration_points': ['website_chat', 'email', 'phone'],
            'multilingual': True
        }

        # Sales Assistant Bot
        sales_bot_config = {
            'lead_qualification_rules': shop_config['sales_criteria'],
            'product_expertise': shop_config['product_knowledge'],
            'upselling_strategies': shop_config['sales_strategies'],
            'crm_integration': shop_config['crm_system']
        }

        return {
            'product_bot': self.product_bot.deploy(product_bot_config),
            'support_bot': self.support_bot.deploy(support_bot_config),
            'sales_bot': self.sales_bot.deploy(sales_bot_config),
            'order_bot': self.order_bot.deploy(shop_config['order_system'])
        }

# Healthcare Bot Ecosystem (DSGVO + Medizinproduktegesetz-konform)
class HealthcareBotEcosystem:
    def __init__(self):
        self.appointment_bot = AppointmentBot()
        self.symptom_bot = SymptomCheckerBot()
        self.medication_bot = MedicationReminderBot()
        self.admin_bot = HealthcareAdminBot()

    def deploy_healthcare_bots(self, healthcare_facility_config):
        """
        DSGVO-konforme Healthcare Bot Implementation
        """
        # Terminbuchungs-Bot
        appointment_config = {
            'calendar_integration': healthcare_facility_config['calendar_system'],
            'doctor_availability': healthcare_facility_config['schedules'],
            'patient_verification': 'insurance_card_ocr',
            'privacy_compliance': 'gdpr_strict',
            'data_retention': '30_days'
        }

        # Symptom-Checker Bot (Keine medizinische Diagnose!)
        symptom_config = {
            'symptom_database': 'certified_medical_knowledge',
            'disclaimer_required': True,
            'escalation_threshold': 'immediate_for_serious_symptoms',
            'medical_professional_oversight': True,
            'liability_protection': 'information_only'
        }

        # Medikations-Erinnerungs-Bot
        medication_config = {
            'prescription_integration': healthcare_facility_config['pharmacy_system'],
            'reminder_schedule': 'personalized',
            'adherence_tracking': True,
            'physician_reporting': True,
            'privacy_encryption': 'end_to_end'
        }

        return {
            'appointment_bot': self.appointment_bot.deploy(appointment_config),
            'symptom_bot': self.symptom_bot.deploy(symptom_config),
            'medication_bot': self.medication_bot.deploy(medication_config),
            'admin_bot': self.admin_bot.deploy(healthcare_facility_config['admin_workflows'])
        }

# Financial Services Bot Implementation
class FinancialServicesBots:
    def __init__(self):
        self.advisory_bot = FinancialAdvisoryBot()
        self.transaction_bot = TransactionBot()
        self.compliance_bot = ComplianceBot()
        self.fraud_bot = FraudDetectionBot()

    def deploy_financial_bots(self, financial_institution_config):
        """
        Finanzdienstleistungs-Bot Implementation
        """
        # Beratungs-Bot
        advisory_config = {
            'investment_knowledge': 'certified_financial_advisor_level',
            'risk_assessment': 'automated_questionnaire',
            'product_recommendations': financial_institution_config['products'],
            'regulatory_compliance': 'mifid_ii_compliant',
            'recording_required': True
        }

        # Transaktions-Bot
        transaction_config = {
            'authentication': 'multi_factor',
            'transaction_limits': financial_institution_config['limits'],
            'fraud_monitoring': 'real_time',
            'regulatory_reporting': 'automatic',
            'audit_trail': 'complete'
        }

        # Compliance-Bot
        compliance_config = {
            'regulatory_frameworks': ['mifid_ii', 'gdpr', 'pci_dss'],
            'monitoring_scope': 'all_customer_interactions',
            'reporting_frequency': 'real_time',
            'violation_detection': 'ml_powered',
            'escalation_protocols': financial_institution_config['compliance_team']
        }

        return {
            'advisory_bot': self.advisory_bot.deploy(advisory_config),
            'transaction_bot': self.transaction_bot.deploy(transaction_config),
            'compliance_bot': self.compliance_bot.deploy(compliance_config),
            'fraud_bot': self.fraud_bot.deploy(financial_institution_config['fraud_rules'])
        }

# Comprehensive Bot Analytics Platform
class BotAnalyticsPlatform:
    def __init__(self):
        self.performance_tracker = BotPerformanceTracker()
        self.conversation_analyzer = ConversationAnalyzer()
        self.roi_calculator = BotROICalculator()

    def generate_bot_analytics_dashboard(self, time_period='last_30_days'):
        """
        Umfassendes Bot-Analytics Dashboard
        """
        analytics_data = {
            'performance_metrics': self.get_performance_metrics(time_period),
            'conversation_insights': self.get_conversation_insights(time_period),
            'user_satisfaction': self.get_satisfaction_metrics(time_period),
            'business_impact': self.get_business_impact(time_period),
            'optimization_opportunities': self.identify_optimization_opportunities()
        }

        return analytics_data

    def get_performance_metrics(self, time_period):
        """
        Bot-Performance Metriken
        """
        return {
            'total_conversations': self.performance_tracker.count_conversations(time_period),
            'resolution_rate': self.performance_tracker.calculate_resolution_rate(time_period),
            'average_response_time': self.performance_tracker.calculate_avg_response_time(time_period),
            'escalation_rate': self.performance_tracker.calculate_escalation_rate(time_period),
            'user_retention': self.performance_tracker.calculate_user_retention(time_period),
            'intent_accuracy': self.performance_tracker.calculate_intent_accuracy(time_period)
        }

    def get_conversation_insights(self, time_period):
        """
        Conversation-Analyse für Bot-Optimierung
        """
        conversations = self.conversation_analyzer.get_conversations(time_period)

        insights = {
            'most_common_intents': self.conversation_analyzer.analyze_intent_frequency(conversations),
            'conversation_flow_patterns': self.conversation_analyzer.analyze_flow_patterns(conversations),
            'drop_off_points': self.conversation_analyzer.identify_drop_off_points(conversations),
            'successful_conversation_patterns': self.conversation_analyzer.identify_success_patterns(conversations),
            'language_preferences': self.conversation_analyzer.analyze_language_usage(conversations),
            'peak_usage_times': self.conversation_analyzer.analyze_usage_patterns(conversations)
        }

        return insights

    def identify_optimization_opportunities(self):
        """
        Automatische Identifikation von Optimierungsmöglichkeiten
        """
        opportunities = []

        # Intent-Modell Verbesserungen
        low_confidence_intents = self.find_low_confidence_intents()
        if low_confidence_intents:
            opportunities.append({
                'type': 'intent_training',
                'priority': 'high',
                'description': f'Retraining needed for intents: {", ".join(low_confidence_intents)}',
                'expected_improvement': '15-25% accuracy increase'
            })

        # Conversation Flow Optimierung
        high_drop_off_points = self.find_high_drop_off_points()
        if high_drop_off_points:
            opportunities.append({
                'type': 'flow_optimization',
                'priority': 'medium',
                'description': f'Optimize conversation flows at: {", ".join(high_drop_off_points)}',
                'expected_improvement': '10-20% completion rate increase'
            })

        # Response Quality Verbesserung
        low_satisfaction_topics = self.find_low_satisfaction_topics()
        if low_satisfaction_topics:
            opportunities.append({
                'type': 'response_improvement',
                'priority': 'medium',
                'description': f'Improve responses for: {", ".join(low_satisfaction_topics)}',
                'expected_improvement': '20-30% satisfaction increase'
            })

        return opportunities

# DSGVO-konforme Bot-Architektur
class PrivacyCompliantBotPlatform:
    def __init__(self):
        self.consent_manager = ConsentManager()
        self.data_minimizer = DataMinimizer()
        self.encryption_service = EncryptionService()
        self.audit_logger = PrivacyAuditLogger()

    def deploy_privacy_compliant_bot(self, bot_config):
        """
        DSGVO-konforme Bot-Deployment
        """
        # 1. Privacy Impact Assessment
        privacy_assessment = self.conduct_privacy_assessment(bot_config)

        # 2. Consent Management Setup
        consent_framework = self.setup_consent_management(bot_config)

        # 3. Data Minimization Implementation
        data_handling_rules = self.implement_data_minimization(bot_config)

        # 4. Encryption Configuration
        encryption_config = self.configure_encryption(bot_config)

        # 5. Audit Logging Setup
        audit_config = self.setup_audit_logging(bot_config)

        return {
            'privacy_compliance_level': privacy_assessment['compliance_score'],
            'consent_framework': consent_framework,
            'data_handling': data_handling_rules,
            'security_measures': encryption_config,
            'audit_capabilities': audit_config,
            'gdpr_ready': privacy_assessment['compliance_score'] >= 0.95
        }

    def implement_data_minimization(self, bot_config):
        """
        Implementierung von Datenminimierung
        """
        minimization_rules = {
            'collection_limitation': 'Only collect data necessary for bot function',
            'purpose_limitation': 'Use data only for stated purposes',
            'retention_limitation': f"Auto-delete after {bot_config.get('retention_days', 30)} days",
            'processing_limitation': 'Process only when user consent is active',
            'accuracy_maintenance': 'Regular data quality checks',
            'storage_limitation': 'EU-only data storage'
        }

        # Automatische PII-Erkennung und -Schutz
        pii_protection = {
            'pii_detection': 'Real-time scanning for personal data',
            'automatic_pseudonymization': 'Auto-replace PII with tokens',
            'data_masking': 'Mask sensitive data in logs',
            'secure_deletion': 'Cryptographic deletion after retention period'
        }

        return {
            'minimization_rules': minimization_rules,
            'pii_protection': pii_protection
        }

# Enterprise Bot Security
class BotSecurityFramework:
    def __init__(self):
        self.threat_detector = BotThreatDetector()
        self.authentication_service = BotAuthenticationService()
        self.rate_limiter = RateLimiter()
        self.fraud_detector = BotFraudDetector()

    def secure_bot_interaction(self, user_input, session_context):
        """
        Comprehensive Bot Security Processing
        """
        security_checks = {
            'input_validation': self.validate_user_input(user_input),
            'rate_limiting': self.check_rate_limits(session_context),
            'fraud_detection': self.detect_fraudulent_behavior(user_input, session_context),
            'threat_analysis': self.analyze_security_threats(user_input),
            'authentication_status': self.verify_user_authentication(session_context)
        }

        # Security Score berechnen
        security_score = self.calculate_security_score(security_checks)

        if security_score < 0.7:
            return self.handle_security_incident(security_checks, session_context)

        return {
            'security_cleared': True,
            'security_score': security_score,
            'additional_verification_required': security_score < 0.9
        }

    def detect_fraudulent_behavior(self, user_input, session_context):
        """
        KI-basierte Betrugserkennung für Bots
        """
        fraud_indicators = {
            'unusual_request_patterns': self.analyze_request_patterns(session_context),
            'social_engineering_attempts': self.detect_social_engineering(user_input),
            'automated_attack_detection': self.detect_bot_attacks(session_context),
            'data_harvesting_attempts': self.detect_data_harvesting(user_input),
            'account_takeover_indicators': self.detect_account_takeover(session_context)
        }

        fraud_score = sum(indicator['risk_score'] for indicator in fraud_indicators.values()) / len(fraud_indicators)

        return {
            'fraud_risk_score': fraud_score,
            'fraud_indicators': fraud_indicators,
            'requires_human_verification': fraud_score > 0.8
        }