MatchBot AI is an MVP financial reconciliation platform designed to automate the matching and reconciliation of financial transactions across multiple data sources. Built as a modular monolith with clear service boundaries, the platform provides intelligent document processing, fuzzy matching algorithms, and AI-powered anomaly detection to streamline financial operations.
This is NOT a microservices architecture. We've deliberately chosen a modular monolith pattern for this MVP, which provides:
- Single deployment unit with logical service boundaries
- Shared database with schema-level separation
- In-process communication reducing network overhead
- Simplified operations while maintaining clear module boundaries for future extraction
- Premature Optimization: Microservices add complexity that isn't justified at MVP scale
- Development Velocity: Monolithic architecture accelerates feature delivery
- Operational Overhead: Single deployment reduces DevOps complexity
- Data Consistency: Shared database simplifies transaction management
- Future-Ready: Module boundaries allow extraction to services when needed
Role: Central request orchestration and business logic coordination
Key Responsibilities:
- JWT validation via Supabase Auth (planned migration from Clerk)
- Request routing and validation
- Billing integration with Stripe API (planned)
- Real-time dashboard aggregation
Design Rationale: FastAPI's async capabilities handle concurrent reconciliation jobs efficiently while maintaining type safety through Pydantic models.
Role: Asynchronous job processing for compute-intensive reconciliation tasks
Processing Pipeline:
- File ingestion and parsing (CSV, Excel, PDF)
- Data normalization and standardization
- Fuzzy matching execution
- Result validation and conflict resolution
Technical Choice: Celery provides reliable task distribution with Redis as both message broker and result backend, crucial for processing large financial datasets.
- Transaction Records: Immutable ledger of all financial transactions
- Reconciliation Rules: Configurable matching criteria
- Audit Trail: Complete history of all reconciliation activities
- User Management: RBAC with Clerk/Supabase integration
- Session Management: User session and authentication tokens
- Result Caching: Frequently accessed reconciliation summaries
- Task Queue: Celery job distribution
- Real-time Updates: WebSocket message broker (planned)
- Local Storage (MVP): Uploaded financial documents
- S3 Migration Path: Ready for cloud storage when scaling
OpenAI API + RapidFuzz Hybrid Approach:
- Fuzzy Matching: RapidFuzz for deterministic string matching
- Semantic Understanding: LLM for complex pattern recognition
- Anomaly Detection: AI-powered fraud and error detection
- Smart Categorization: Automatic transaction classification
Current: Clerk integration (implemented) Migration Path: Supabase Auth (planned)
Security Features:
- Row-level security (RLS) policies
- JWT token validation
- Role-based access control (RBAC)
- Audit logging for compliance
- User authentication and authorization
- File upload infrastructure
- Background job processing
- Basic transaction matching
- Manual reconciliation workflow
- Exception handling
- Fuzzy matching algorithms
- Rule-based reconciliation engine
- Bulk reconciliation processing
- Reconciliation reporting
- AI-powered matching suggestions
- Anomaly detection
- Predictive reconciliation
- Natural language rule creation
- Decision: Modular monolith with service extraction points
- Rationale: Faster iteration, simpler debugging, easier data consistency
- Future Path: Extract heavy processors (matching engine, AI services) as needed
- Decision: Hybrid - sync for UI operations, async for reconciliation
- Rationale: Responsive UI while handling large dataset processing
- Implementation: Celery for background jobs, FastAPI for real-time responses
- Decision: PostgreSQL ACID with strategic caching
- Rationale: Financial data requires consistency; cache derived data only
- Strategy: Write-through cache for summaries, invalidation on updates
backend/
├── app/
│ ├── api/ # HTTP endpoints
│ ├── core/ # Configuration, security
│ ├── models/ # SQLModel entities
│ ├── services/ # Business logic
│ ├── tasks/ # Celery tasks
│ └── webhooks/ # External integrations
frontend/
├── src/
│ ├── routes/ # Page components
│ ├── components/ # Reusable UI
│ ├── features/ # Feature modules
│ └── services/ # API clients
- Matching Engine: Core reconciliation algorithms
- File Processor: Document parsing and normalization
- Rule Engine: Reconciliation rule evaluation
- Reporting Service: Analytics and report generation
- Notification Service: Alerts and communications
- Concurrent Users: ~100 (current infrastructure)
- File Processing: 10MB files in <5 seconds
- API Response: P95 < 200ms
- Database Queries: Optimized with proper indexing
- Large File Processing: Stream processing implementation pending
- Complex Matching: Algorithm optimization needed
- Report Generation: Background generation with caching
- Real-time Updates: WebSocket implementation planned
# Start all services
docker-compose up -d
# Access points
# Frontend: http://localhost:5173
# Backend API: http://localhost:8000
# API Documentation: http://localhost:8000/docs
# Database Admin: http://adminer.localhost- Single Docker Compose: Simplified orchestration
- Health Checks: Automatic recovery
- Volume Persistence: Data survives container restarts
- Network Isolation: Internal service communication
- Encryption: TLS in transit, AES-256 at rest (planned)
- Access Control: RBAC with audit trails
- Data Retention: Configurable retention policies
- PII Handling: Tokenization for sensitive data (planned)
- Audit Logging: All reconciliation actions logged
- Data Lineage: Track data transformations
- Immutable Records: Transaction history preservation
- Export Controls: Regulated data export features
- Scale Limits: Single-server deployment
- Real-time Updates: Polling-based, not WebSocket
- Matching Algorithms: Basic fuzzy matching only
- Report Formats: Limited export options
- Multi-tenancy: Single tenant currently
- Backend: Unit tests for services
- Frontend: Component testing with Playwright
- Integration: API endpoint testing
- E2E: Critical user journeys
- Reconciliation accuracy
- Data integrity
- Security boundaries
- Performance regression
- Vertical scaling on single server
- Database query optimization
- Redis caching layer
- Read replicas for reporting
- CDN for static assets
- Background job prioritization
- Service extraction (matching engine first)
- Horizontal scaling with load balancer
- Multi-region deployment
- Python: Black, Ruff, type hints
- TypeScript: Strict mode, no
any - Testing: Minimum 70% coverage
- Documentation: API docs via OpenAPI
- Modular Boundaries: Maintain service interfaces
- Database Migrations: Alembic for schema changes
- Feature Flags: Gradual rollout capability
- Backward Compatibility: API versioning strategy
This MVP establishes the foundation for a comprehensive financial reconciliation platform. The modular monolith architecture provides the simplicity needed for rapid development while maintaining clear boundaries for future service extraction as the platform scales.