# System Architecture Document ## Virtual Board Member AI System **Document Version**: 1.0 **Date**: August 2025 **Classification**: Confidential --- ## 1. Executive Summary This document defines the complete system architecture for the Virtual Board Member AI system, incorporating microservices architecture, event-driven design patterns, and enterprise-grade security controls. The architecture supports both local development and cloud-scale production deployment. ## 2. High-Level Architecture ### 2.1 System Overview ``` ┌─────────────────────────────────────────────────────────────────┐ │ CLIENT LAYER │ ├─────────────────┬───────────────────┬──────────────────────────┤ │ Web Portal │ Mobile Apps │ API Clients │ └────────┬────────┴────────┬──────────┴────────┬─────────────────┘ │ │ │ ▼ ▼ ▼ ┌─────────────────────────────────────────────────────────────────┐ │ API GATEWAY (Kong/AWS API GW) │ │ • Rate Limiting • Authentication • Request Routing │ └────────┬─────────────────────────────────────┬──────────────────┘ │ │ ▼ ▼ ┌──────────────────────────────┬─────────────────────────────────┐ │ SECURITY LAYER │ ORCHESTRATION LAYER │ ├──────────────────────────────┼─────────────────────────────────┤ │ • OAuth 2.0/OIDC │ • LangChain Controller │ │ • JWT Validation │ • Workflow Engine (Airflow) │ │ • RBAC │ • Model Router │ └──────────────┬───────────────┴───────────┬─────────────────────┘ │ │ ▼ ▼ ┌──────────────────────────────────────────────────────────────┐ │ MICROSERVICES LAYER │ ├────────────────┬────────────────┬───────────────┬─────────────┤ │ LLM Service │ RAG Service │ Doc Processor │ Analytics │ │ • OpenRouter │ • Qdrant │ • PDF/XLSX │ • Metrics │ │ • Fallback │ • Embedding │ • OCR │ • Insights │ └────────┬───────┴────────┬───────┴───────┬──────┴──────┬──────┘ │ │ │ │ ▼ ▼ ▼ ▼ ┌──────────────────────────────────────────────────────────────┐ │ DATA LAYER │ ├─────────────┬──────────────┬──────────────┬─────────────────┤ │ Vector DB │ Document │ Cache │ Message Queue │ │ (Qdrant) │ Store (S3) │ (Redis) │ (Kafka/SQS) │ └─────────────┴──────────────┴──────────────┴─────────────────┘ ``` ### 2.2 Component Responsibilities | Component | Primary Responsibility | Technology Stack | |-----------|----------------------|------------------| | API Gateway | Request routing, rate limiting, authentication | Kong, AWS API Gateway | | LLM Service | Model orchestration, prompt management | LangChain, OpenRouter | | RAG Service | Document retrieval, context management | Qdrant, LangChain | | Document Processor | File parsing, OCR, extraction | Python libs, Tesseract | | Analytics Service | Usage tracking, insights generation | PostgreSQL, Grafana | | Vector Database | Semantic search, document storage | Qdrant | | Cache Layer | Response caching, session management | Redis | | Message Queue | Async processing, event streaming | Kafka/AWS SQS | ## 3. Detailed Component Architecture ### 3.1 LLM Orchestration Service ```python class LLMOrchestrationArchitecture: """ Core orchestration service managing multi-model routing and execution """ components = { "model_router": { "responsibility": "Route requests to optimal models", "implementation": "Strategy pattern with cost/quality optimization", "models": { "extraction": "gpt-4o-mini", "analysis": "claude-3.5-sonnet", "synthesis": "gpt-4-turbo", "vision": "gpt-4-vision" } }, "prompt_manager": { "responsibility": "Manage and version prompt templates", "storage": "PostgreSQL with version control", "caching": "Redis with 1-hour TTL" }, "chain_executor": { "responsibility": "Execute multi-step reasoning chains", "framework": "LangChain with custom extensions", "patterns": ["MapReduce", "Sequential", "Parallel"] }, "memory_manager": { "responsibility": "Maintain conversation context", "types": { "short_term": "Redis (24-hour TTL)", "long_term": "PostgreSQL", "semantic": "Qdrant vectors" } } } ``` ### 3.2 Document Processing Pipeline ```yaml pipeline: stages: - ingestion: supported_formats: [pdf, xlsx, csv, pptx, txt] max_file_size: 100MB concurrent_processing: 10 - extraction: pdf: primary: pdfplumber fallback: PyPDF2 ocr: tesseract-ocr excel: library: openpyxl preserve: [formulas, formatting, charts] powerpoint: library: python-pptx image_extraction: gpt-4-vision - transformation: chunking: strategy: semantic size: 1000-1500 tokens overlap: 200 tokens metadata: extraction: automatic enrichment: business_context - indexing: embedding_model: voyage-3-large batch_size: 100 parallel_workers: 4 ``` ### 3.3 Vector Database Architecture ```python class VectorDatabaseSchema: """ Qdrant collection schema for board documents """ collection_config = { "name": "board_documents", "vector_size": 1024, "distance": "Cosine", "optimizers_config": { "indexing_threshold": 20000, "memmap_threshold": 50000, "default_segment_number": 4 }, "payload_schema": { "document_id": "keyword", "document_type": "keyword", # report|presentation|minutes "department": "keyword", # finance|hr|legal|operations "date_created": "datetime", "reporting_period": "keyword", "confidentiality": "keyword", # public|internal|confidential "stakeholders": "keyword[]", "key_topics": "text[]", "content": "text", "chunk_index": "integer", "total_chunks": "integer" } } ``` ## 4. Data Flow Architecture ### 4.1 Document Ingestion Flow ``` User Upload → API Gateway → Document Processor ↓ Validation & Security Scan ↓ Format-Specific Parser ↓ Content Extraction ↓ ┌──────────┴──────────┐ ↓ ↓ Raw Storage (S3) Text Processing ↓ Chunking Strategy ↓ Embedding Generation ↓ Vector Database ↓ Indexing Complete ``` ### 4.2 Query Processing Flow ``` User Query → API Gateway → Authentication ↓ Query Processor ↓ Intent Classification ↓ ┌─────────────┼─────────────┐ ↓ ↓ ↓ RAG Pipeline Direct LLM Analytics ↓ ↓ ↓ Vector Search Model Router SQL Query ↓ ↓ ↓ Context Build Prompt Build Data Fetch ↓ ↓ ↓ └─────────────┼─────────────┘ ↓ Response Synthesis ↓ Output Validation ↓ Client Response ``` ## 5. Security Architecture ### 5.1 Security Layers ```yaml security_architecture: perimeter_security: - waf: AWS WAF / Cloudflare - ddos_protection: Cloudflare / AWS Shield - api_gateway: Rate limiting, API key validation authentication: - protocol: OAuth 2.0 / OIDC - provider: Auth0 / AWS Cognito - mfa: Required for admin access authorization: - model: RBAC with attribute-based extensions - roles: - board_member: Full access to all features - executive: Department-specific access - analyst: Read-only access - admin: System configuration data_protection: encryption_at_rest: - algorithm: AES-256-GCM - key_management: AWS KMS / HashiCorp Vault encryption_in_transit: - protocol: TLS 1.3 - certificate: EV SSL llm_security: - prompt_injection_prevention: Input validation - output_filtering: PII detection and masking - audit_logging: All queries and responses - rate_limiting: Per-user and per-endpoint ``` ### 5.2 Zero-Trust Architecture ```python class ZeroTrustImplementation: """ Zero-trust security model implementation """ principles = { "never_trust": "All requests validated regardless of source", "always_verify": "Continuous authentication and authorization", "least_privilege": "Minimal access rights by default", "assume_breach": "Design assumes compromise has occurred" } implementation = { "micro_segmentation": { "network": "Service mesh with Istio", "services": "Individual service authentication", "data": "Field-level encryption where needed" }, "continuous_validation": { "token_refresh": "15-minute intervals", "behavior_analysis": "Anomaly detection on usage patterns", "device_trust": "Device fingerprinting and validation" } } ``` ## 6. Scalability Architecture ### 6.1 Horizontal Scaling Strategy ```yaml scaling_configuration: kubernetes: autoscaling: - type: HorizontalPodAutoscaler metrics: - cpu: 70% - memory: 80% - custom: requests_per_second > 100 services: llm_service: min_replicas: 2 max_replicas: 20 target_cpu: 70% rag_service: min_replicas: 3 max_replicas: 15 target_cpu: 60% document_processor: min_replicas: 2 max_replicas: 10 scaling_policy: job_queue_length database: qdrant: sharding: 4 shards replication: 3 replicas per shard distribution: Consistent hashing redis: clustering: Redis Cluster mode nodes: 6 (3 masters, 3 replicas) ``` ### 6.2 Performance Optimization ```python class PerformanceOptimization: """ System-wide performance optimization strategies """ caching_strategy = { "l1_cache": { "type": "Application memory", "ttl": "5 minutes", "size": "1GB per instance" }, "l2_cache": { "type": "Redis", "ttl": "1 hour", "size": "10GB cluster" }, "l3_cache": { "type": "CDN (CloudFront)", "ttl": "24 hours", "content": "Static assets, common reports" } } database_optimization = { "connection_pooling": { "min_connections": 10, "max_connections": 100, "timeout": 30 }, "query_optimization": { "indexes": "Automated index recommendation", "partitioning": "Time-based for logs", "materialized_views": "Common aggregations" } } llm_optimization = { "batching": "Group similar requests", "caching": "Semantic similarity matching", "model_routing": "Cost-optimized selection", "token_optimization": "Prompt compression" } ``` ## 7. Deployment Architecture ### 7.1 Environment Strategy ```yaml environments: development: infrastructure: Docker Compose database: Chroma (local) llm: OpenRouter sandbox data: Synthetic test data staging: infrastructure: Kubernetes (single node) database: Qdrant Cloud (dev tier) llm: OpenRouter with rate limits data: Anonymized production sample production: infrastructure: EKS/GKE/AKS database: Qdrant Cloud (production) llm: OpenRouter production data: Full production access backup: Real-time replication ``` ### 7.2 CI/CD Pipeline ```yaml pipeline: source_control: platform: GitHub/GitLab branching: GitFlow protection: Main branch protected continuous_integration: - trigger: Pull request - steps: - lint: Black, isort, mypy - test: pytest with 80% coverage - security: Bandit, safety - build: Docker multi-stage continuous_deployment: - staging: trigger: Merge to develop approval: Automatic rollback: Automatic on failure - production: trigger: Merge to main approval: Manual (2 approvers) strategy: Blue-green deployment rollback: One-click rollback ``` ## 8. Monitoring & Observability ### 8.1 Monitoring Stack ```yaml monitoring: metrics: collection: Prometheus storage: VictoriaMetrics visualization: Grafana logging: aggregation: Fluentd storage: Elasticsearch analysis: Kibana tracing: instrumentation: OpenTelemetry backend: Jaeger sampling: 1% in production alerting: manager: AlertManager channels: [email, slack, pagerduty] escalation: 3-tier support model ``` ### 8.2 Key Performance Indicators ```python class SystemKPIs: """ Critical metrics for system health monitoring """ availability = { "uptime_target": "99.9%", "measurement": "Synthetic monitoring", "alert_threshold": "99.5%" } performance = { "response_time_p50": "< 2 seconds", "response_time_p95": "< 5 seconds", "response_time_p99": "< 10 seconds", "throughput": "> 100 requests/second" } business_metrics = { "daily_active_users": "Track unique users", "query_success_rate": "> 95%", "document_processing_rate": "> 500/hour", "cost_per_query": "< $0.10" } ai_metrics = { "model_accuracy": "> 90%", "hallucination_rate": "< 2%", "context_relevance": "> 85%", "user_satisfaction": "> 4.5/5" } ``` ## 9. Disaster Recovery ### 9.1 Backup Strategy ```yaml backup_strategy: data_classification: critical: - vector_database - document_store - configuration important: - logs - metrics - cache backup_schedule: critical: frequency: Real-time replication retention: 90 days location: Multi-region important: frequency: Daily retention: 30 days location: Single region recovery_objectives: rto: 4 hours # Recovery Time Objective rpo: 1 hour # Recovery Point Objective ``` ### 9.2 Failure Scenarios ```python class FailureScenarios: """ Documented failure scenarios and recovery procedures """ scenarios = { "llm_service_failure": { "detection": "Health check failure", "immediate_action": "Fallback to secondary model", "recovery": "Auto-restart with exponential backoff", "escalation": "Page on-call after 3 failures" }, "database_failure": { "detection": "Connection timeout", "immediate_action": "Serve from cache", "recovery": "Automatic failover to replica", "escalation": "Immediate page to DBA" }, "data_corruption": { "detection": "Checksum validation", "immediate_action": "Isolate affected data", "recovery": "Restore from last known good backup", "escalation": "Executive notification" } } ``` ## 10. Integration Architecture ### 10.1 External System Integrations ```yaml integrations: document_sources: sharepoint: protocol: REST API auth: OAuth 2.0 sync: Incremental every 15 minutes google_drive: protocol: REST API auth: OAuth 2.0 sync: Real-time via webhooks email: protocol: IMAP/Exchange auth: OAuth 2.0 sync: Every 5 minutes identity_providers: primary: Active Directory protocol: SAML 2.0 attributes: [email, department, role] notification_systems: email: SMTP with TLS slack: Webhook API teams: Graph API ``` ### 10.2 API Specifications ```python class APISpecification: """ RESTful API design following OpenAPI 3.0 """ endpoints = { "/api/v1/documents": { "POST": "Upload document", "GET": "List documents", "DELETE": "Remove document" }, "/api/v1/query": { "POST": "Submit query", "GET": "Retrieve query history" }, "/api/v1/analysis": { "POST": "Generate analysis", "GET": "Retrieve past analyses" }, "/api/v1/commitments": { "GET": "List commitments", "PUT": "Update commitment status", "POST": "Create manual commitment" } } authentication = { "type": "Bearer token (JWT)", "header": "Authorization: Bearer ", "expiry": "1 hour", "refresh": "Available via /api/v1/auth/refresh" } rate_limiting = { "default": "100 requests per minute", "burst": "200 requests allowed", "headers": { "X-RateLimit-Limit": "Current limit", "X-RateLimit-Remaining": "Requests remaining", "X-RateLimit-Reset": "Reset timestamp" } } ``` ## 11. Development Architecture ### 11.1 Local Development Setup ```yaml local_development: prerequisites: - Docker Desktop 4.0+ - Python 3.11+ - Node.js 18+ (for frontend) - 16GB RAM minimum - 50GB free disk space setup_script: | # Clone repository git clone https://github.com/company/vbm-ai cd vbm-ai # Environment setup cp .env.example .env.local # Start services docker-compose -f docker-compose.dev.yml up -d # Install dependencies poetry install # Run migrations poetry run alembic upgrade head # Seed test data poetry run python scripts/seed_data.py # Start development server poetry run uvicorn app.main:app --reload ``` ### 11.2 Testing Architecture ```python class TestingStrategy: """ Comprehensive testing approach for AI systems """ test_levels = { "unit_tests": { "coverage_target": "80%", "framework": "pytest", "mocking": "unittest.mock for LLM calls", "execution": "On every commit" }, "integration_tests": { "scope": "Service boundaries", "framework": "pytest + testcontainers", "data": "Synthetic test fixtures", "execution": "On pull requests" }, "e2e_tests": { "scope": "Full user workflows", "framework": "Playwright", "environment": "Staging", "execution": "Before production deploy" }, "llm_tests": { "framework": "DeepEval", "metrics": ["correctness", "relevance", "hallucination"], "dataset": "Golden test set of 100 queries", "threshold": "90% pass rate" } } test_data_strategy = { "synthetic_generation": "Faker + custom generators", "anonymization": "Production data scrubbing", "volume": "1000 documents minimum", "diversity": "All document types represented" } ``` ## 12. Migration Strategy ### 12.1 Local to Cloud Migration Path ```yaml migration_phases: phase_1_local: duration: Weeks 1-4 environment: Docker Compose components: - vector_db: Chroma (local) - llm: OpenRouter dev keys - storage: Local filesystem goals: - Validate core functionality - Establish development workflow - Create initial test suite phase_2_hybrid: duration: Weeks 5-8 environment: Local + Cloud services components: - vector_db: Qdrant Cloud - llm: OpenRouter production - storage: AWS S3 goals: - Test cloud service integration - Validate performance at scale - Implement security controls phase_3_cloud: duration: Weeks 9-12 environment: Full cloud deployment infrastructure: Kubernetes (EKS/GKE) components: - All services containerized - Multi-region deployment - Full monitoring stack goals: - Production readiness - High availability setup - Disaster recovery validation ``` ### 12.2 Data Migration Strategy ```python class DataMigrationPlan: """ Zero-downtime data migration strategy """ migration_steps = [ { "step": 1, "action": "Setup parallel environments", "duration": "2 days", "rollback": "No impact - parallel setup" }, { "step": 2, "action": "Initial data sync", "duration": "1-3 days depending on volume", "rollback": "Delete cloud copies" }, { "step": 3, "action": "Enable dual writes", "duration": "1 day", "rollback": "Disable dual writes" }, { "step": 4, "action": "Validation and reconciliation", "duration": "2 days", "rollback": "Fix discrepancies and retry" }, { "step": 5, "action": "Traffic cutover", "duration": "1 hour", "rollback": "DNS switch back" } ] validation_criteria = { "document_count": "100% match", "vector_similarity": "> 99% cosine similarity", "metadata_integrity": "100% match", "query_results": "95% similarity in top-10 results" } ``` ## 13. Performance Requirements ### 13.1 Service Level Objectives (SLOs) ```yaml slos: availability: target: 99.9% measurement_window: 30 days exclusions: Planned maintenance windows latency: p50: < 2 seconds p95: < 5 seconds p99: < 10 seconds measurement: End-to-end including LLM calls error_rate: target: < 1% exclusions: Client errors (4xx) measurement_window: 1 hour rolling throughput: sustained: 100 requests/second burst: 500 requests/second for 60 seconds concurrent_users: 100 ``` ### 13.2 Capacity Planning ```python class CapacityPlanning: """ Resource requirements for different scales """ sizing_tiers = { "small": { "users": "< 50", "documents": "< 10,000", "queries_per_day": "< 1,000", "infrastructure": { "compute": "8 vCPUs, 32GB RAM", "storage": "500GB SSD", "database": "Qdrant 2-node cluster" }, "monthly_cost": "$2,000 - $3,000" }, "medium": { "users": "50-500", "documents": "10,000-100,000", "queries_per_day": "1,000-10,000", "infrastructure": { "compute": "32 vCPUs, 128GB RAM", "storage": "2TB SSD", "database": "Qdrant 4-node cluster" }, "monthly_cost": "$5,000 - $8,000" }, "large": { "users": "> 500", "documents": "> 100,000", "queries_per_day": "> 10,000", "infrastructure": { "compute": "100+ vCPUs, 400GB+ RAM", "storage": "10TB+ SSD", "database": "Qdrant 8+ node cluster" }, "monthly_cost": "$15,000+" } } ``` ## 14. Compliance & Governance ### 14.1 Regulatory Compliance ```yaml compliance_requirements: data_privacy: gdpr: - data_minimization: Collect only necessary data - right_to_erasure: Implement data deletion - data_portability: Export user data on request - consent_management: Track and manage consent ccpa: - disclosure: What data is collected - deletion: Honor deletion requests - opt_out: Allow opt-out of data sale - non_discrimination: No penalty for exercising rights industry_standards: soc2_type2: - security: Encryption and access controls - availability: SLA compliance - processing_integrity: Data accuracy - confidentiality: Data protection - privacy: Personal information handling iso_27001: - risk_assessment: Annual assessment - security_controls: 114 controls implemented - continuous_improvement: Regular audits - documentation: Complete ISMS ``` ### 14.2 Audit Architecture ```python class AuditArchitecture: """ Comprehensive audit logging and compliance tracking """ audit_events = { "authentication": ["login", "logout", "failed_auth", "mfa_challenge"], "authorization": ["permission_grant", "permission_deny", "role_change"], "data_access": ["document_view", "document_download", "query_execution"], "data_modification": ["document_upload", "document_delete", "metadata_update"], "system_changes": ["config_change", "deployment", "user_management"], "ai_operations": ["model_selection", "prompt_execution", "output_filtering"] } audit_log_schema = { "timestamp": "ISO 8601 with timezone", "user_id": "Authenticated user identifier", "session_id": "Unique session identifier", "event_type": "Category and specific event", "resource": "Affected resource identifier", "action": "Specific action performed", "result": "Success/failure", "metadata": "Additional context", "ip_address": "Client IP (hashed)", "user_agent": "Client information" } retention_policy = { "audit_logs": "7 years", "system_logs": "90 days", "performance_metrics": "13 months", "security_events": "7 years" } ``` ## 15. Appendices ### Appendix A: Technology Stack Summary | Layer | Technology | Version | License | |-------|------------|---------|---------| | Language | Python | 3.11+ | PSF | | Framework | FastAPI | 0.100+ | MIT | | LLM Orchestration | LangChain | 0.1+ | MIT | | Vector Database | Qdrant | 1.7+ | Apache 2.0 | | Cache | Redis | 7.0+ | BSD | | Message Queue | Kafka | 3.5+ | Apache 2.0 | | Container | Docker | 24+ | Apache 2.0 | | Orchestration | Kubernetes | 1.28+ | Apache 2.0 | | Monitoring | Prometheus | 2.45+ | Apache 2.0 | ### Appendix B: Network Architecture ```yaml network_topology: dmz: - Load balancer - WAF - CDN endpoints application_tier: - API servers - Web servers - WebSocket servers service_tier: - Microservices - Background workers - Scheduled jobs data_tier: - Databases - Cache layers - File storage management_tier: - Monitoring - Logging - CI/CD ``` ### Appendix C: Security Checklist - [ ] TLS 1.3 for all communications - [ ] Secrets management via Vault/KMS - [ ] Regular dependency updates - [ ] Security scanning in CI/CD - [ ] Penetration testing quarterly - [ ] Security training for developers - [ ] Incident response plan documented - [ ] Data encryption at rest - [ ] Network segmentation implemented - [ ] Zero-trust architecture adopted --- **Document Approval** | Role | Name | Signature | Date | |------|------|-----------|------| | Chief Architect | | | | | Security Architect | | | | | DevOps Lead | | | | | CTO | | | |