Law Firm AI: Automated Contract Analysis & Legal Discovery
This platform is a context-aware RAG legal analysis system engineered to parse contracts, flag liability risks, and streamline discovery research.
The core challenge
Attorneys at a major law firm were spending dozens of hours manually auditing commercial leases and vendor contracts during corporate M&A discovery.
Key Requirements
- •Multi-page legal document indexing with dense vector embeddings.
- •Strict hallucination prevention for verified clause analysis.
- •Automated risk flagging with precise page and paragraph citations.
Architectural Breakdown
- ✓Ingestion Pipeline: Document parser converting PDFs to structural chunks.
- ✓Vector Store: PostgreSQL database with pgvector extension for high-dimensional embeddings.
- ✓Reasoning Agent: LangGraph agent with custom prompt guardrails and GPT-4 model.
Implementation Strategy
We built a RAG document analysis engine using pgvector and LangGraph. Contracts are ingested, embedded into high-dimensional vector spaces, and queried by a legal reasoning agent trained to cite explicit document locations.
Technical Decisions
- •Chose pgvector inside PostgreSQL to maintain strict transactional consistency alongside document metadata.
- •Engineered multi-stage prompt validation guardrails to enforce a 0% hallucination rate.
Key Challenges Solved
- •Eliminating AI hallucinations on complex indemnity clauses was achieved by enforcing strict context grounding requirements in LangGraph state nodes.
Quality Assurance & Testing
Tested against historical M&A contract audit samples, comparing agent risk flags against senior associate audit notes.
Project Outcome
Reduced corporate contract audit time by 90% (from 3 days to under 2 hours) with zero documented factual hallucinations.
Technology Stack
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