FinanceGuard AI 🔒

Production-ready multi-agent system for autonomous financial risk assessment and compliance auditing, validated through 250+ regression-safe test cases and monitored with real-time observability.

🎯 Project Overview

FinanceGuard AI is a production-hardened, multi-agent financial analysis system that combines:

🤖 Multi-Agent Orchestration: LangGraph-based stateful workflow for dynamic risk assessment
📚 RAG-Powered Intelligence: Vector database with 10K+ financial regulations and market reports
🛡️ Hallucination Guardrails: Self-correction loops and Validator Agent for financial accuracy
🧪 Evaluation-Driven Development: 250+ regression tests integrated into CI/CD pipeline
📊 Full Observability: Prometheus, Grafana, and LangSmith for end-to-end visibility
💰 Cost-Aware Engineering: Optimized to <$0.05 per request

🏗️ Architecture Highlights

4 Specialized Agents: Market Data, Risk Analyst, Compliance, Summarizer
LangGraph Orchestration: Stateful workflow with self-correction loops
RAG Engine: Chroma/Qdrant vector DB for regulatory knowledge retrieval
Tool Integration: Market data APIs, financial calculators, validators
Observability Stack: Metrics, tracing, and logging for production monitoring

📋 Project Documents

This repository contains comprehensive project documentation:

SUPER_PROJECT_PROPOSAL.md - Complete technical project proposal
- Architecture diagrams (ASCII)
- Component breakdown with folder structure
- Evaluation plan (30+ specific test cases)
- 6-week implementation roadmap
- Interview Q&A (12 tough questions + answers)
PORTFOLIO_ASSETS.md - Marketing & portfolio content
- LinkedIn post templates (EN + HE)
- Screenshot guides ("money shots")
- GitHub README template
- Key metrics showcase
- Video demo script

🚀 Current Status

Phase: ✅ Proposal & Planning Complete
Next Steps: Implementation (See SUPER_PROJECT_PROPOSAL.md for detailed roadmap)

Planned Implementation:

Week 1-2: Foundation (Docker, FastAPI, LangGraph setup)
Week 3: Core Agents (Market Data, Risk Analyst, Compliance, Summarizer)
Week 4: Evaluation & Observability (250+ tests, Prometheus, Grafana)
Week 5: UI & Polish (Streamlit, error handling, documentation)
Week 6: Final Testing & Deployment

🛠️ Technology Stack

Orchestration: LangGraph, LangChain
LLMs: OpenAI GPT-4 / GPT-3.5-turbo
RAG: Chroma/Qdrant, OpenAI Embeddings
API: FastAPI, Pydantic
UI: Streamlit, Gradio
Observability: Prometheus, Grafana, LangSmith
Data: PostgreSQL, Redis
Deployment: Docker, Docker Compose
CI/CD: GitHub Actions

📊 Key Metrics (Targets)

Metric	Target	Status
Hard Test Pass Rate	≥90%	📋 Planned
Retrieval Accuracy	≥85%	📋 Planned
P95 Latency	≤5s	📋 Planned
Cost per Request	≤$0.05	📋 Planned
Regression Tests	250+	📋 Planned

📚 Quick Links

Full Proposal: SUPER_PROJECT_PROPOSAL.md
Portfolio Assets: PORTFOLIO_ASSETS.md
GitHub Repository: https://github.com/nitzannossery/financeguard-ai

🤝 Contributing

This is a portfolio project demonstrating production-grade AI engineering. Contributions and feedback are welcome!

📄 License

MIT License - see LICENSE file for details (to be added).

⭐ If you find this project useful, please star it!

Built with: LangChain, LangGraph, FastAPI, and a focus on production-ready AI systems.

nitzannossery/financeguard-ai