AI Legal Research Assistant Development

𝘾𝙝𝙖𝙡𝙡𝙚𝙣𝙜𝙚: Law firm lawyers spent 12-15 hours weekly manually searching through thousands of case files and legal documents.

𝙎𝙤𝙡𝙪𝙩𝙞𝙤𝙣: Built enterprise RAG system using Python, LangChain, and FastAPI with: Hybrid search (semantic + keyword) across legal documents Custom document chunking preserving legal context Multi-vector indexing for case law, statutes, and precedents Citation verification linking every response to source documents Query decomposition for complex legal questions Re-ranking layer using cross-encoders for precision Slack integration for team collaboration Role-based access control for document security

𝙍𝙚𝙨𝙪𝙡𝙩𝙨: 84% reduction in research time (12 hrs → 2 hrs weekly) 67% more relevant precedents discovered 23% improvement in case win rate Junior lawyers performing at senior research levels

𝙏𝙚𝙘𝙝 𝙎𝙩𝙖𝙘𝙠: Python, LangChain, FastAPI, GPT-4, Pinecone, PostgreSQL, Redis, Slack API, Docker