KI_Dokumentenverarbeitung_Stack:
  Document_Capture:
    - Optical Character Recognition (OCR)
    - Handwriting Recognition
    - Form Recognition
    - Image Enhancement

  Document_Classification:
    - Machine Learning Classifiers
    - Deep Learning Models
    - Template Matching
    - Content-based Classification

  Information_Extraction:
    - Named Entity Recognition (NER)
    - Key-Value Pair Extraction
    - Table Extraction
    - Signature Detection

  Document_Understanding:
    - Natural Language Processing (NLP)
    - Sentiment Analysis
    - Intent Recognition
    - Relationship Extraction

  Workflow_Integration:
    - Business Rule Engines
    - API Integrations
    - ERP/CRM Connectors
    - Human-in-the-Loop Validation

# Intelligente Rechnungsverarbeitung
import azure.ai.formrecognizer as fr
from azure.core.credentials import AzureKeyCredential

class IntelligentInvoiceProcessor:
    def __init__(self, azure_config):
        self.form_recognizer_client = fr.DocumentAnalysisClient(
            endpoint=azure_config['endpoint'],
            credential=AzureKeyCredential(azure_config['key'])
        )
        self.erp_connector = ERPConnector()
        self.validation_engine = ValidationEngine()

    def process_invoice(self, invoice_document):
        """
        Vollautomatische Rechnungsverarbeitung
        """
        # 1. Dokumententyp erkennen
        doc_type = self.classify_document(invoice_document)

        if doc_type == 'invoice':
            # 2. Strukturierte Datenextraktion
            invoice_data = self.extract_invoice_data(invoice_document)

            # 3. Datenvalidierung
            validation_result = self.validate_invoice_data(invoice_data)

            # 4. Dublettenprüfung
            duplicate_check = self.check_for_duplicates(invoice_data)

            # 5. ERP-Integration
            if validation_result['is_valid'] and not duplicate_check['is_duplicate']:
                erp_result = self.create_erp_entry(invoice_data)

                return {
                    'status': 'processed',
                    'invoice_data': invoice_data,
                    'erp_reference': erp_result['reference_number'],
                    'processing_time': '< 30 seconds',
                    'confidence': validation_result['confidence']
                }
            else:
                return self.route_for_manual_review(invoice_data, validation_result)

    def extract_invoice_data(self, document):
        """
        KI-basierte Datenextraktion aus Rechnungen
        """
        # Azure Form Recognizer für strukturierte Extraktion
        poller = self.form_recognizer_client.begin_analyze_document(
            "prebuilt-invoice",
            document
        )
        result = poller.result()

        extracted_data = {
            'vendor_name': None,
            'vendor_address': None,
            'invoice_number': None,
            'invoice_date': None,
            'due_date': None,
            'total_amount': None,
            'tax_amount': None,
            'line_items': [],
            'payment_terms': None
        }

        # Strukturierte Feldextraktion
        for document in result.documents:
            for field_name, field in document.fields.items():
                if field_name == "VendorName" and field.value:
                    extracted_data['vendor_name'] = field.value
                elif field_name == "InvoiceId" and field.value:
                    extracted_data['invoice_number'] = field.value
                elif field_name == "InvoiceDate" and field.value:
                    extracted_data['invoice_date'] = field.value
                elif field_name == "InvoiceTotal" and field.value:
                    extracted_data['total_amount'] = field.value
                elif field_name == "Items" and field.value:
                    for item in field.value:
                        line_item = {
                            'description': item.value.get('Description', {}).value if item.value.get('Description') else None,
                            'quantity': item.value.get('Quantity', {}).value if item.value.get('Quantity') else None,
                            'unit_price': item.value.get('UnitPrice', {}).value if item.value.get('UnitPrice') else None,
                            'amount': item.value.get('Amount', {}).value if item.value.get('Amount') else None
                        }
                        extracted_data['line_items'].append(line_item)

        return extracted_data

    def validate_invoice_data(self, invoice_data):
        """
        Intelligente Datenvalidierung
        """
        validation_rules = {
            'vendor_validation': self.validate_vendor(invoice_data['vendor_name']),
            'amount_validation': self.validate_amounts(invoice_data),
            'date_validation': self.validate_dates(invoice_data),
            'format_validation': self.validate_formats(invoice_data)
        }

        overall_confidence = sum(rule['confidence'] for rule in validation_rules.values()) / len(validation_rules)
        is_valid = all(rule['is_valid'] for rule in validation_rules.values())

        return {
            'is_valid': is_valid,
            'confidence': overall_confidence,
            'validation_details': validation_rules,
            'requires_review': overall_confidence < 0.9
        }

# KI-gestützte Vertragsanalyse
class ContractAnalysisAI:
    def __init__(self):
        self.nlp_model = load_legal_nlp_model()
        self.clause_detector = ClauseDetectionModel()
        self.risk_analyzer = ContractRiskAnalyzer()

    def analyze_contract(self, contract_document):
        """
        Umfassende KI-basierte Vertragsanalyse
        """
        # 1. Text-Extraktion und OCR
        contract_text = self.extract_text_from_document(contract_document)

        # 2. Dokumentenstruktur erkennen
        document_structure = self.analyze_document_structure(contract_text)

        # 3. Vertragsklauseln identifizieren
        clauses = self.identify_contract_clauses(contract_text)

        # 4. Risikobewertung
        risk_assessment = self.assess_contract_risks(clauses)

        # 5. Compliance-Prüfung
        compliance_check = self.check_regulatory_compliance(clauses)

        # 6. Empfehlungen generieren
        recommendations = self.generate_recommendations(risk_assessment, compliance_check)

        return {
            'contract_type': document_structure['contract_type'],
            'parties': document_structure['parties'],
            'key_terms': self.extract_key_terms(clauses),
            'risk_score': risk_assessment['overall_risk'],
            'compliance_status': compliance_check['status'],
            'recommendations': recommendations,
            'requires_legal_review': risk_assessment['overall_risk'] > 0.7
        }

    def identify_contract_clauses(self, contract_text):
        """
        Identifikation und Klassifikation von Vertragsklauseln
        """
        clause_types = [
            'termination_clause',
            'liability_clause',
            'intellectual_property_clause',
            'confidentiality_clause',
            'payment_terms',
            'governing_law',
            'dispute_resolution',
            'force_majeure',
            'warranties_clause'
        ]

        identified_clauses = {}

        for clause_type in clause_types:
            clause_matches = self.clause_detector.find_clauses(contract_text, clause_type)
            if clause_matches:
                identified_clauses[clause_type] = {
                    'text': clause_matches['text'],
                    'confidence': clause_matches['confidence'],
                    'position': clause_matches['position'],
                    'analysis': self.analyze_clause_content(clause_matches['text'], clause_type)
                }

        return identified_clauses

    def assess_contract_risks(self, clauses):
        """
        KI-basierte Risikobewertung von Verträgen
        """
        risk_factors = {
            'liability_risk': self.assess_liability_risk(clauses.get('liability_clause')),
            'termination_risk': self.assess_termination_risk(clauses.get('termination_clause')),
            'ip_risk': self.assess_ip_risk(clauses.get('intellectual_property_clause')),
            'compliance_risk': self.assess_compliance_risk(clauses),
            'financial_risk': self.assess_financial_risk(clauses.get('payment_terms'))
        }

        # Gewichtete Gesamtrisikobewertung
        weights = {
            'liability_risk': 0.3,
            'termination_risk': 0.2,
            'ip_risk': 0.2,
            'compliance_risk': 0.2,
            'financial_risk': 0.1
        }

        overall_risk = sum(
            risk_factors[factor] * weights[factor]
            for factor in risk_factors
        )

        return {
            'overall_risk': overall_risk,
            'risk_breakdown': risk_factors,
            'risk_level': 'high' if overall_risk > 0.7 else 'medium' if overall_risk > 0.4 else 'low'
        }

# Intelligente Formularverarbeitung
class IntelligentFormProcessor:
    def __init__(self):
        self.form_recognizer = FormRecognitionService()
        self.data_validator = FormDataValidator()
        self.workflow_engine = WorkflowEngine()

    def process_application_form(self, form_document, form_type):
        """
        Automatisierte Bearbeitung von Antragsformularen
        """
        # 1. Formulartyp-spezifische Verarbeitung
        if form_type == 'loan_application':
            return self.process_loan_application(form_document)
        elif form_type == 'insurance_claim':
            return self.process_insurance_claim(form_document)
        elif form_type == 'employment_application':
            return self.process_job_application(form_document)
        else:
            return self.process_generic_form(form_document)

    def process_loan_application(self, form_document):
        """
        KI-gestützte Kreditantragsbearbeitung
        """
        # Strukturierte Datenextraktion
        applicant_data = self.extract_applicant_information(form_document)
        financial_data = self.extract_financial_information(form_document)

        # Automatische Bonitätsprüfung
        credit_check = self.perform_credit_assessment(applicant_data, financial_data)

        # Risikobewertung
        risk_score = self.calculate_risk_score(applicant_data, financial_data, credit_check)

        # Entscheidungslogik
        if risk_score < 0.3:
            decision = 'approved'
            next_steps = ['generate_approval_letter', 'schedule_signing']
        elif risk_score < 0.7:
            decision = 'review_required'
            next_steps = ['route_to_underwriter', 'request_additional_documents']
        else:
            decision = 'declined'
            next_steps = ['generate_decline_letter', 'offer_alternatives']

        return {
            'application_id': self.generate_application_id(),
            'applicant_data': applicant_data,
            'financial_summary': financial_data,
            'credit_assessment': credit_check,
            'risk_score': risk_score,
            'decision': decision,
            'next_steps': next_steps,
            'processing_time': '< 5 minutes',
            'human_review_required': decision == 'review_required'
        }

# Custom Document AI Model Training
from azure.ai.formrecognizer import DocumentModelAdministrationClient
from azure.core.credentials import AzureKeyCredential

class CustomDocumentAI:
    def __init__(self, azure_config):
        self.admin_client = DocumentModelAdministrationClient(
            endpoint=azure_config['endpoint'],
            credential=AzureKeyCredential(azure_config['key'])
        )

    def train_custom_model(self, training_data_url, model_name):
        """
        Training eines Custom Document AI Models
        """
        # Model Training starten
        poller = self.admin_client.begin_build_document_model(
            build_mode="template",  # oder "neural" für komplexere Dokumente
            blob_container_url=training_data_url,
            model_id=model_name,
            description=f"Custom model for {model_name} document processing"
        )

        # Training überwachen
        model = poller.result()

        # Model-Performance evaluieren
        performance_metrics = self.evaluate_model_performance(model)

        return {
            'model_id': model.model_id,
            'training_status': 'completed',
            'accuracy': performance_metrics['accuracy'],
            'confidence_threshold': performance_metrics['recommended_threshold'],
            'supported_fields': list(model.doc_types.keys())
        }

    def create_composite_model(self, component_models):
        """
        Erstellung eines Composite Models für verschiedene Dokumenttypen
        """
        # Mehrere spezialisierte Modelle zu einem kombinieren
        poller = self.admin_client.begin_compose_document_model(
            component_models,
            model_id="composite-document-model",
            description="Composite model for multiple document types"
        )

        composite_model = poller.result()

        return {
            'composite_model_id': composite_model.model_id,
            'component_models': component_models,
            'capabilities': 'Multi-document-type processing'
        }

# Enterprise Document Processing Pipeline
class EnterpriseDocumentPipeline:
    def __init__(self):
        self.document_classifier = DocumentClassifier()
        self.quality_checker = DocumentQualityChecker()
        self.processors = {
            'invoice': InvoiceProcessor(),
            'contract': ContractProcessor(),
            'form': FormProcessor(),
            'email': EmailProcessor(),
            'report': ReportProcessor()
        }
        self.validation_engine = ValidationEngine()
        self.audit_logger = AuditLogger()

    def process_document_batch(self, document_batch):
        """
        Batch-Verarbeitung von Dokumenten
        """
        processing_results = []

        for document in document_batch:
            try:
                # 1. Dokument-Qualitätsprüfung
                quality_check = self.quality_checker.assess_quality(document)

                if quality_check['quality_score'] < 0.7:
                    result = self.handle_low_quality_document(document, quality_check)
                else:
                    # 2. Dokumentenklassifikation
                    doc_type = self.document_classifier.classify(document)

                    # 3. Typ-spezifische Verarbeitung
                    processor = self.processors.get(doc_type['type'])
                    if processor:
                        result = processor.process(document)
                        result['document_type'] = doc_type['type']
                        result['confidence'] = doc_type['confidence']
                    else:
                        result = self.handle_unknown_document_type(document, doc_type)

                # 4. Ergebnisvalidierung
                validation_result = self.validation_engine.validate(result)
                result['validation'] = validation_result

                # 5. Audit-Logging
                self.audit_logger.log_processing(document, result)

                processing_results.append(result)

            except Exception as e:
                error_result = self.handle_processing_error(document, e)
                processing_results.append(error_result)

        return {
            'batch_id': self.generate_batch_id(),
            'total_documents': len(document_batch),
            'successful_processing': len([r for r in processing_results if r['status'] == 'success']),
            'failed_processing': len([r for r in processing_results if r['status'] == 'error']),
            'requires_review': len([r for r in processing_results if r.get('requires_review', False)]),
            'results': processing_results
        }

    def handle_low_quality_document(self, document, quality_check):
        """
        Behandlung von Dokumenten mit niedriger Qualität
        """
        # Bildverbesserung versuchen
        enhanced_document = self.enhance_document_quality(document)

        if enhanced_document['quality_improvement'] > 0.2:
            # Erneute Verarbeitung mit verbessertem Dokument
            return self.process_single_document(enhanced_document['document'])
        else:
            return {
                'status': 'quality_issue',
                'message': 'Document quality too low for reliable processing',
                'quality_score': quality_check['quality_score'],
                'recommended_action': 'manual_review_or_rescan',
                'quality_issues': quality_check['issues']
            }

KI_Dokumentenverarbeitung_ROI:
  Manuelle_Verarbeitung_Kosten:
    Rechnungsverarbeitung: '€8-15 pro Rechnung'
    Vertragsanalyse: '€200-500 pro Vertrag'
    Formularverarbeitung: '€5-12 pro Formular'
    E-Mail_Klassifikation: '€2-5 pro E-Mail'

  KI_Automatisierung_Kosten:
    Rechnungsverarbeitung: '€0.50-1.50 pro Rechnung'
    Vertragsanalyse: '€20-50 pro Vertrag'
    Formularverarbeitung: '€0.30-1.00 pro Formular'
    E-Mail_Klassifikation: '€0.10-0.30 pro E-Mail'

  Zeitersparnis:
    Rechnungsverarbeitung: '90% (15min → 1.5min)'
    Vertragsanalyse: '85% (4h → 36min)'
    Formularverarbeitung: '95% (20min → 1min)'
    E-Mail_Klassifikation: '98% (5min → 6sek)'

  Qualitätsverbesserung:
    Fehlerreduzierung: '80-95%'
    Konsistenz: '99%+'
    Compliance: 'Automatische Prüfung'
    Audit_Trail: 'Vollständig dokumentiert'

  Implementierungskosten:
    Setup_und_Training: '€25.000-60.000'
    Custom_Model_Development: '€15.000-40.000'
    Integration_Kosten: '€20.000-50.000'
    Laufende_Kosten_monatlich: '€2.000-8.000'

  Erwarteter_ROI: '200-400% nach 12 Monaten'

# DSGVO-konforme Dokumentenverarbeitung
class PrivacyCompliantDocumentAI:
    def __init__(self):
        self.pii_detector = PIIDetectionService()
        self.encryption_service = EncryptionService()
        self.consent_manager = ConsentManager()
        self.audit_logger = ComplianceAuditLogger()

    def process_document_with_privacy(self, document, processing_consent):
        """
        DSGVO-konforme Dokumentenverarbeitung
        """
        # 1. Einverständniserklärung prüfen
        if not self.consent_manager.has_valid_consent(processing_consent):
            return {'error': 'Keine gültige Einverständniserklärung'}

        # 2. PII-Erkennung vor Verarbeitung
        pii_analysis = self.pii_detector.scan_document(document)

        # 3. Automatische Pseudonymisierung
        if pii_analysis['contains_pii']:
            pseudonymized_doc = self.pseudonymize_document(document, pii_analysis)
            processing_document = pseudonymized_doc
        else:
            processing_document = document

        # 4. Verschlüsselte Verarbeitung
        encrypted_document = self.encryption_service.encrypt(processing_document)
        processing_result = self.process_encrypted_document(encrypted_document)

        # 5. Audit-Logging
        self.audit_logger.log_privacy_compliant_processing({
            'document_id': document.id,
            'pii_detected': pii_analysis['contains_pii'],
            'pseudonymization_applied': pii_analysis['contains_pii'],
            'processing_time': processing_result['processing_time'],
            'consent_reference': processing_consent['consent_id']
        })

        # 6. Automatische Löschung einplanen
        self.schedule_document_deletion(
            document_id=document.id,
            retention_period=processing_consent['retention_period']
        )

        return {
            'processing_result': processing_result,
            'privacy_compliance': True,
            'pii_handling': 'pseudonymized' if pii_analysis['contains_pii'] else 'none_detected',
            'audit_trail': True
        }

    def pseudonymize_document(self, document, pii_analysis):
        """
        Intelligente Pseudonymisierung von Dokumenten
        """
        pseudonymization_map = {}
        pseudonymized_content = document.content

        for pii_entity in pii_analysis['pii_entities']:
            if pii_entity['type'] == 'person_name':
                pseudonym = f"Person_{len(pseudonymization_map) + 1}"
            elif pii_entity['type'] == 'email':
                pseudonym = f"email_{len(pseudonymization_map) + 1}@example.com"
            elif pii_entity['type'] == 'phone':
                pseudonym = f"+49-XXX-{len(pseudonymization_map) + 1}"
            elif pii_entity['type'] == 'address':
                pseudonym = f"Address_{len(pseudonymization_map) + 1}"
            else:
                pseudonym = f"Data_{len(pseudonymization_map) + 1}"

            pseudonymization_map[pii_entity['text']] = pseudonym
            pseudonymized_content = pseudonymized_content.replace(
                pii_entity['text'],
                pseudonym
            )

        # Pseudonymisierungs-Mapping sicher speichern (verschlüsselt)
        self.store_pseudonymization_mapping(document.id, pseudonymization_map)

        return Document(
            id=document.id,
            content=pseudonymized_content,
            metadata={**document.metadata, 'pseudonymized': True}
        )

# Compliance Monitoring für Document AI
class DocumentAIComplianceMonitor:
    def __init__(self):
        self.compliance_rules = self.load_compliance_rules()
        self.violation_detector = ViolationDetector()

    def monitor_processing_compliance(self, processing_activities):
        """
        Kontinuierliches Compliance-Monitoring
        """
        compliance_report = {
            'total_documents_processed': len(processing_activities),
            'compliance_violations': [],
            'privacy_metrics': {},
            'audit_completeness': {},
            'recommendations': []
        }

        for activity in processing_activities:
            # DSGVO-Compliance prüfen
            gdpr_check = self.check_gdpr_compliance(activity)
            if not gdpr_check['compliant']:
                compliance_report['compliance_violations'].append(gdpr_check)

            # Retention Policy Compliance
            retention_check = self.check_retention_compliance(activity)
            if not retention_check['compliant']:
                compliance_report['compliance_violations'].append(retention_check)

            # Data Minimization Compliance
            minimization_check = self.check_data_minimization(activity)
            if not minimization_check['compliant']:
                compliance_report['compliance_violations'].append(minimization_check)

        # Compliance Score berechnen
        compliance_score = 1.0 - (len(compliance_report['compliance_violations']) / len(processing_activities))
        compliance_report['overall_compliance_score'] = compliance_score

        # Empfehlungen generieren
        if compliance_score < 0.95:
            compliance_report['recommendations'] = self.generate_compliance_recommendations(
                compliance_report['compliance_violations']
            )

        return compliance_report

# Medizinische Dokumentenverarbeitung (DSGVO + Medizinproduktegesetz)
class HealthcareDocumentAI:
    def __init__(self):
        self.medical_nlp = MedicalNLPEngine()
        self.hipaa_compliance = HIPAAComplianceEngine()
        self.icd_classifier = ICDClassifier()

    def process_medical_document(self, document, document_type):
        """
        Verarbeitung medizinischer Dokumente
        """
        if document_type == 'patient_record':
            return self.process_patient_record(document)
        elif document_type == 'lab_report':
            return self.process_lab_report(document)
        elif document_type == 'discharge_summary':
            return self.process_discharge_summary(document)
        elif document_type == 'prescription':
            return self.process_prescription(document)

    def process_patient_record(self, document):
        """
        Intelligente Patientenakten-Verarbeitung
        """
        # Medizinische Entitäten extrahieren
        medical_entities = self.medical_nlp.extract_medical_entities(document)

        # ICD-10 Codes automatisch zuordnen
        icd_codes = self.icd_classifier.classify_conditions(medical_entities['conditions'])

        # Medikationsanalyse
        medication_analysis = self.analyze_medications(medical_entities['medications'])

        # Risikofaktoren identifizieren
        risk_factors = self.identify_risk_factors(medical_entities)

        return {
            'patient_id': self.extract_patient_id(document),
            'medical_conditions': medical_entities['conditions'],
            'icd_codes': icd_codes,
            'medications': medication_analysis,
            'risk_factors': risk_factors,
            'requires_physician_review': self.assess_review_necessity(risk_factors),
            'compliance_status': 'HIPAA_compliant'
        }

# Finanzdienstleistungs-Dokumentenverarbeitung
class FinancialDocumentAI:
    def __init__(self):
        self.fraud_detector = FraudDetectionEngine()
        self.compliance_checker = FinancialComplianceChecker()
        self.risk_assessor = FinancialRiskAssessor()

    def process_financial_document(self, document, document_type):
        """
        Verarbeitung von Finanzdienstleistungsdokumenten
        """
        if document_type == 'loan_application':
            return self.process_loan_application(document)
        elif document_type == 'bank_statement':
            return self.process_bank_statement(document)
        elif document_type == 'insurance_claim':
            return self.process_insurance_claim(document)
        elif document_type == 'kyc_document':
            return self.process_kyc_document(document)

    def process_loan_application(self, document):
        """
        KI-gestützte Kreditantragsbearbeitung
        """
        # Antragsdaten extrahieren
        applicant_data = self.extract_applicant_information(document)

        # Betrugserkennungsprüfung
        fraud_analysis = self.fraud_detector.analyze_application(applicant_data)

        # Kreditwürdigkeitsprüfung
        creditworthiness = self.assess_creditworthiness(applicant_data)

        # Compliance-Prüfung (KYC/AML)
        compliance_check = self.compliance_checker.verify_compliance(applicant_data)

        # Automatische Entscheidung
        if fraud_analysis['risk_score'] < 0.3 and creditworthiness['score'] > 0.7:
            decision = 'pre_approved'
        elif fraud_analysis['risk_score'] > 0.7:
            decision = 'declined_fraud_risk'
        else:
            decision = 'manual_review_required'

        return {
            'application_id': self.generate_application_id(),
            'applicant_summary': applicant_data,
            'fraud_risk_score': fraud_analysis['risk_score'],
            'credit_score': creditworthiness['score'],
            'compliance_status': compliance_check['status'],
            'decision': decision,
            'confidence': min(fraud_analysis['confidence'], creditworthiness['confidence'])
        }

# Document AI Performance Monitoring
class DocumentAIMonitoring:
    def __init__(self):
        self.performance_tracker = PerformanceTracker()
        self.model_drift_detector = ModelDriftDetector()

    def monitor_model_performance(self, model_id, recent_predictions):
        """
        Kontinuierliches Model-Performance Monitoring
        """
        performance_metrics = {
            'accuracy': self.calculate_accuracy(recent_predictions),
            'precision': self.calculate_precision(recent_predictions),
            'recall': self.calculate_recall(recent_predictions),
            'f1_score': self.calculate_f1_score(recent_predictions),
            'confidence_distribution': self.analyze_confidence_distribution(recent_predictions),
            'processing_time': self.measure_processing_time(recent_predictions)
        }

        # Model Drift Detection
        drift_analysis = self.model_drift_detector.detect_drift(recent_predictions)

        # Performance Alerts
        alerts = []
        if performance_metrics['accuracy'] < 0.9:
            alerts.append('Accuracy below threshold')
        if drift_analysis['drift_detected']:
            alerts.append('Model drift detected')
        if performance_metrics['processing_time'] > 30:
            alerts.append('Processing time above threshold')

        return {
            'model_id': model_id,
            'performance_metrics': performance_metrics,
            'drift_analysis': drift_analysis,
            'alerts': alerts,
            'recommendations': self.generate_performance_recommendations(performance_metrics, drift_analysis)
        }

    def generate_performance_recommendations(self, metrics, drift_analysis):
        """
        Automatische Performance-Optimierungsempfehlungen
        """
        recommendations = []

        if metrics['accuracy'] < 0.9:
            recommendations.append({
                'type': 'retraining',
                'priority': 'high',
                'description': 'Model accuracy below acceptable threshold. Retraining recommended.'
            })

        if drift_analysis['drift_detected']:
            recommendations.append({
                'type': 'data_refresh',
                'priority': 'medium',
                'description': 'Data drift detected. Consider updating training data.'
            })

        if metrics['processing_time'] > 30:
            recommendations.append({
                'type': 'optimization',
                'priority': 'medium',
                'description': 'Processing time optimization needed. Consider model compression.'
            })

        return recommendations