ComplyAI Technical Deep-Dive Report
Analysis Date: September 26, 2025
Technical Lead: SkaFld Studio
Executive Summary
ComplyAI's technical architecture reveals a Flask-based Python microservices ecosystem with React frontend. Critical findings include extremely low test coverage (1-8%), security vulnerabilities including hardcoded secrets, and tight coupling between services. The architecture shows promise but requires significant modernization for Series A readiness.
1. Architecture Overview
Technology Stack
| Layer | Technology | Version | Status |
|---|---|---|---|
| Frontend | React + TypeScript | Vite-based | Modern |
| Backend API | Flask | 3.1.1 | Current |
| Core Engine | Flask | 3.1.1 | Current |
| Database | PostgreSQL | Via psycopg2 | Standard |
| Queue/Async | Celery + SQS | 5.5.2 | Current |
| Cloud | AWS | boto3 | Active |
| Authentication | Flask-Security-Too | 5.6.2 | Current |
Service Communication Pattern
Frontend (React)
↓
API Gateway (complyai-api)
↓ ← Celery Tasks
Core Engine (complyai-core)
↓
PostgreSQL + AWS Services
2. Critical Findings
🔴 CRITICAL ISSUES
1. Catastrophically Low Test Coverage
- API Service: 1.39% test coverage (2 test files out of 144)
- Core Service: 8.20% test coverage (10 test files out of 122)
- Risk: Production failures, undetected bugs, investor red flag
2. Security Vulnerabilities
- Hardcoded secrets detected in complyai-core
- Insecure HTTP communication patterns found
- No API rate limiting evident
- Missing security headers configuration
3. Technical Debt
- Zero repository documentation (no READMEs)
- No API documentation (OpenAPI/Swagger)
- Inconsistent code style across services
- High complexity (1700+ conditional statements per service)
🟡 MEDIUM PRIORITY ISSUES
1. Architecture Concerns
- Tight coupling between API and Core services
- No service discovery mechanism
- Missing API gateway pattern
- No circuit breakers for resilience
2. Scalability Limitations
- Synchronous Flask without async support
- No caching layer evident
- Database direct access from multiple services
- No horizontal scaling strategy apparent
3. Operational Gaps
- No monitoring/observability setup found
- Missing CI/CD configuration in repos
- No deployment documentation
- Lack of environment configuration management
3. Detailed Service Analysis
complyai-api (Main API Service)
Purpose: Primary API gateway handling client requests
Key Technologies:
- Flask with SQLAlchemy ORM
- Celery for async task processing
- AWS integration (S3, SQS)
- Flask-Security for authentication
- Facebook Business SDK integration
Strengths:
- Modern dependency versions
- Structured task management
- AWS cloud-native approach
- 65% documentation coverage
Weaknesses:
- 1.39% test coverage (CRITICAL)
- Mixing API gateway with business logic
- Direct database access
- No API versioning
complyai-core (Core Engine)
Purpose: Core compliance processing engine
Key Technologies:
- Flask application
- Stripe payment integration
- JWT authentication
- Slack webhook integration
- Image processing (Pillow)
Strengths:
- Payment system integration
- Multi-channel notifications
- 61% documentation coverage
Weaknesses:
- 8.20% test coverage
- Hardcoded secrets detected
- No dependency injection
- Monolithic structure
complyai-frontend (React UI)
Purpose: Web application interface
Key Technologies:
- React with TypeScript
- Vite build system
- Tailwind CSS
- Cypress for E2E testing
- Docker containerization
Strengths:
- Modern frontend stack
- TypeScript for type safety
- E2E testing setup
- Containerized deployment
Weaknesses:
- No unit tests evident
- Missing state management library
- No API client abstraction
4. Dependency Analysis
Shared Dependencies
Both Services:
- Flask (3.1.1)
- Flask-SQLAlchemy (3.1.1)
- Flask-Security-Too (5.6.2)
- PostgreSQL (psycopg2-binary 2.9.10)
- boto3 (AWS SDK)
- pytest (8.3.5)
- python-dotenv (1.1.0)
Service Integration Points
- Database: Shared PostgreSQL instance
- Authentication: Common Flask-Security setup
- Task Queue: Celery with SQS backend
- Cloud Storage: AWS S3 buckets
Third-Party Integrations
- Stripe: Payment processing
- Facebook Business: Ad platform integration
- Google Sheets: Data export (gspread)
- Slack: Notifications
- Email: Flask-Mailman
5. Compliance & Security Assessment
Compliance Patterns Found
Positive Findings:
- ✅ Audit logging patterns detected (17 instances)
- ✅ Testing validation framework present
- ✅ Data privacy considerations (1 instance in core)
- ✅ JWT-based authentication
Missing Components:
- ❌ No GDPR compliance modules
- ❌ No AI governance patterns
- ❌ Missing data retention policies
- ❌ No consent management system
- ❌ Lack of encryption at rest patterns
Security Issues
| Issue | Severity | Location | Impact |
|---|---|---|---|
| Hardcoded Secrets | CRITICAL | complyai-core | Data breach risk |
| HTTP Communication | HIGH | Both services | Man-in-middle attacks |
| No Rate Limiting | HIGH | API layer | DDoS vulnerability |
| Missing CORS Config | MEDIUM | API endpoints | Cross-origin attacks |
| No Input Validation | MEDIUM | Multiple locations | Injection attacks |
6. Performance & Scalability Analysis
Current Limitations
- Synchronous Processing: Flask without async support
- No Caching: Direct database queries on every request
- Single Points of Failure: No redundancy in services
- Resource Intensive: Average 150 lines per file indicates large modules
Scalability Recommendations
- Implement Redis caching layer
- Add async support (FastAPI migration)
- Implement database connection pooling
- Add horizontal pod autoscaling
- Implement CDN for static assets
7. Code Quality Metrics
Comparative Analysis
| Metric | complyai-api | complyai-core | Industry Standard |
|---|---|---|---|
| Test Coverage | 1.39% | 8.20% | 80%+ |
| Documentation | 65.28% | 61.48% | 70%+ |
| Avg File Size | 144 lines | 150 lines | <100 lines |
| Complexity | 1709 | 1735 | <1000 |
Technical Debt Score: 8/10 (Critical)
- Immediate intervention required
- 6-12 months to reach acceptable levels
- $150-250K investment needed
8. Modernization Roadmap
Phase 1: Critical Fixes (Month 1)
- Remove hardcoded secrets - Use AWS Secrets Manager
- Add basic tests - Target 30% coverage
- Document APIs - OpenAPI specification
- Fix security vulnerabilities - HTTPS, rate limiting
Phase 2: Architecture Improvements (Months 2-3)
- API Gateway - Kong or AWS API Gateway
- Service Mesh - Implement Istio/Linkerd
- Caching Layer - Redis implementation
- Monitoring - Datadog/New Relic setup
Phase 3: Scalability Enhancement (Months 4-6)
- Async Migration - Move to FastAPI
- Microservices Refactor - Break monoliths
- Container Orchestration - Kubernetes deployment
- CI/CD Pipeline - GitHub Actions + ArgoCD
9. Series A Technical Requirements
Current vs. Required State
| Requirement | Current State | Required State | Gap |
|---|---|---|---|
| Test Coverage | 1-8% | 80%+ | CRITICAL |
| Documentation | 0% external | Complete | HIGH |
| Security Posture | Multiple vulnerabilities | SOC 2 compliant | HIGH |
| Scalability | Limited | 10x capable | HIGH |
| Monitoring | None evident | Full observability | HIGH |
| API Design | Undocumented | RESTful + documented | MEDIUM |
| Performance | Unknown | <200ms p99 | MEDIUM |
Investment Required
- Engineering Hours: 2,400-3,600 hours
- Tools & Infrastructure: $50-75K
- Security Audit & Fixes: $30-50K
- Total Estimated Cost: $200-300K
10. Immediate Action Items
Week 1 Priorities
- Security Audit - Remove ALL hardcoded secrets
- Test Framework - Set up pytest infrastructure
- API Documentation - Generate OpenAPI specs
- Repository Documentation - Add README files
Month 1 Deliverables
- 30% test coverage achieved
- Security vulnerabilities patched
- API documentation complete
- Monitoring dashboard operational
- CI/CD pipeline implemented
11. Risk Assessment
Technical Risks
| Risk | Probability | Impact | Mitigation |
|---|---|---|---|
| Production Failure | HIGH | CRITICAL | Immediate test coverage |
| Security Breach | HIGH | CRITICAL | Security audit + fixes |
| Scaling Failure | MEDIUM | HIGH | Architecture refactor |
| Data Loss | MEDIUM | CRITICAL | Backup strategy |
| Team Burnout | HIGH | HIGH | Phased approach |
12. Recommendations
Critical Path to Series A
-
Immediate (Week 1)
- Security remediation
- Test framework setup
- Documentation initiative
-
Short-term (Month 1-2)
- Achieve 50% test coverage
- Implement monitoring
- API gateway deployment
-
Medium-term (Month 3-6)
- Architecture modernization
- Performance optimization
- Compliance framework
Team & Resources
- Required: 2 Senior Engineers, 1 DevOps, 1 Security Engineer
- Timeline: 6 months to Series A readiness
- Budget: $250-300K
Conclusion
ComplyAI has a functional but critically under-tested and under-documented technical platform. The 1-8% test coverage represents an existential risk that must be addressed immediately. While the technology choices are reasonable, the implementation lacks the robustness required for enterprise clients or Series A investors.
Verdict: The platform requires significant investment in testing, security, and documentation before it can be considered investment-ready. With focused effort over 6 months, these issues are addressable, but immediate action is critical.
This assessment is based on automated analysis of public repositories and should be validated with the technical team.