Legal Tech

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.

Problem Statement

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

OpenAI APILangGraphpgvectorPostgreSQLNext.js

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