Enterprise RAG System for a Legal Tech Startup

A legal tech startup wanted lawyers to query case law across 50,000+ legal documents in natural language, with cited references.

We built a retrieval-augmented generation (RAG) pipeline that indexes 50,000+ legal documents. Lawyers query case law in natural language and get answers with cited references.

Outcome: research time reduced by 70%.

How we would build it in 2026

If we built this today, we would use hybrid search with reranking over the documents, Claude Opus 5.5 to answer with cited references, and RAGAS to evaluate answer quality before each release. This is our current stack, not the one this project used. Typical delivery: a 2-6 week pilot, then 6-12 weeks to production.

See how we deliver generative AI and RAG systems.