Client Overview

A mid-sized legal consultancy with a database spanning 4TB of contracts, case law, internal memos, and client-specific documentation. Lawyers regularly faced delays while preparing for hearings, drafting arguments, or responding to urgent legal queries.

Challenges

• Information Overload: Lawyers spent 3–5 hours/day manually searching for relevant clauses, rulings, and case data
• Missed Precedents: Lack of a structured system meant attorneys often missed strong legal precedents hidden in past case files
• Slow Client Responses: Providing fast, accurate answers to client questions was difficult without deep doc retrieval
• No Search Intelligence: Their internal drive was just folders and filenames — no semantic search or contextual understanding

Our Solution

We implemented a Retrieval-Augmented Generation (RAG) agent that:

• Ingested 15+ years of documents (PDFs, scanned rulings, client briefs)

• Allowed lawyers to ask natural-language questions like

  “What precedent did we use in the 2019 XYZ Corp arbitration?”

• Returned context-rich answers with cited sources

• Integrated into a sleek internal dashboard for both junior and senior staff

• Built using OpenAI for LLMs, FAISS for vector search, and custom preprocessing for legal metadata

Operational Impact