RAG Knowledge Base
Built a vector-powered knowledge base over 50,000 legal documents. Lawyers query natural language and get cited answers in seconds.
The Challenge
A legal firm needed associates to research case law and regulatory documents before drafting opinions. Research took 2-4 hours per matter, with junior associates often missing relevant precedents buried in the firm's 50,000-document archive.
The Solution
Ingested the entire document archive into UniCore's RAG pipeline backed by Qdrant vector database. The Research Agent processes natural language queries, retrieves relevant document passages with citations, and summarizes findings. Results include source references with page numbers for verification.
Key Features Used
- ✓Qdrant vector database with 50,000+ document embeddings
- ✓Research Agent with natural language query interface
- ✓Citation-aware responses with document source references
- ✓Multi-model embedding pipeline (OpenAI ada-002)
- ✓Confidentiality-scoped retrieval (per-matter access control)
- ✓Chat history for iterative research sessions
Results
“Our associates now find precedents they would have missed entirely. The ROI paid for itself in the first month.”
Tech Stack
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