Enterprise Legal Tech: RAG Semantic Search Across 500,000+ Case Filings

A premier corporate law firm was losing thousands of associate hours to manual document discovery across decades of archived briefs, trial transcripts, and contracts.

Hybrid Retrieval-Augmented Generation (RAG)

We designed a private on-premises AI retrieval engine combining dense vector embeddings with sparse keyword BM25 indexing in PostgreSQL with the pgvector extension.

System Achievements

• Indexed over 500,000 legal PDFs with automated OCR, chunking, and metadata tagging
• Average brief precedent discovery query time reduced from 4.2 hours to 12 seconds
• Exact citation pinpointing highlighting the specific paragraph and page number in original PDFs
• Strict zero-retention data residency ensuring total attorney-client confidentiality

Managing Partner Review

“Our litigators can now prepare stronger briefs in a fraction of the time. This technology gave our firm an undeniable competitive advantage in court.”