Legal document parser
Project context
An internal research initiative to evaluate the viability of localized, air-gapped LLM deployments for processing highly sensitive legal documentation without third-party API exposure.
Business challenge
Traditional legal document parsing relies heavily on manual auditing or cloud-based AI APIs, which pose severe data-privacy risks for confidential agreements.
Research findings
Evaluated several quantized models (Llama 3, Mistral) against state-of-the-art closed models to determine the accuracy of zero-shot entity extraction on legal text.
Solution design
Designed a secure retrieval-augmented generation architecture that runs entirely on local infrastructure, so data never leaves the client environment.
System architecture
Next.js frontend communicating with a containerized Python backend. The backend hosts a quantized local LLM, using ChromaDB for rapid similarity search over vectorized document chunks.
Implementation
A custom chunking algorithm tuned for legal clauses (definitions, terms, signatures) and a React dashboard for attorneys to query documents in plain language.
Outcome
A fully offline document query system that extracts clauses with high accuracy, eliminating third-party data exposure entirely.
Lessons learned
Context-window limits in local models demand extremely precise vector retrieval; tuning chunk overlap was critical to preventing hallucinated clauses.