Legal contract analysis with a multi-agent RAG pipeline

Traditional legal analysis of long documents can take hours or days. This platform transforms 500–1000 page contracts — with complex formatting, clauses, tables, and cross-references — into a searchable, intelligent knowledge system that legal teams can query in plain language.

Rather than relying on a model's memory, the system retrieves the most relevant document sections from a vector database before generating each answer, so responses stay grounded in — and traceable to — the original contract text.

The problem

Reviewing long legal contracts takes hours or days, and generic LLMs hallucinate on legal text. The system needed accurate answers grounded in — and traceable to — the actual contract language, across documents of 500–1000 pages.

Our approach

• Built an ingestion pipeline that repairs and parses complex document structures — clauses, tables, and references — so models interpret the content correctly.
• Designed a RAG architecture over a Qdrant vector database with query expansion and multi-step retrieval to surface the right clauses in very long documents.
• Split the workload across a multi-agent workflow — document processing, retrieval, query understanding, and response generation — using OpenAI and Google Gemini.

The solution

A FastAPI platform that lets legal teams upload large contracts and ask complex questions, getting context-aware answers grounded in the source clauses — turning static PDFs into an interactive legal knowledge base.

What we built