LawFirmCutsResearchTimewithLangChain.jsDocumentSearch
How a law firm built a RAG pipeline with LangChain.js and Qdrant to make 50,000+ case files and precedent documents searchable in natural language — reducing the time associates spend on document research per matter.
Key Benefits
How a law firm built a RAG pipeline with LangChain.js and Qdrant to make 50,000+ case files and precedent documents searchable in natural language — reducing the time associates spend on document research per matter.
- RAG: Deployed
- 50K+: Docs Indexed
- 6 Wks: Delivery
The Challenge
A law firm with a substantial case history maintained case files, precedents, and legal opinions across multiple document repositories. Associates spent significant time searching manually for relevant precedents when preparing for new matters, and senior lawyers were frequently interrupted to answer questions that were already documented in completed case work. The volume of documents made keyword search unreliable — relevant content was missed when query terms did not exactly match document language.
The Solution
Techseria built a RAG pipeline using LangChain.js and Qdrant vector database, ingesting over 50,000 case documents, precedents, and legal opinions from the firm's existing repositories. Associates can now query the knowledge base in plain English — 'find precedents where a force majeure clause was successfully invoked in a commercial lease dispute' — and receive accurate, sourced answers with citations back to the original case files. The system runs on Node.js, deployed on Azure with role-based access control ensuring associates only retrieve documents from cases they are authorized to access. New case documents are ingested automatically as they are added to the document management system. The entire build was scoped and priced from a 2-week Discovery Workshop and delivered in 6 weeks.
Impact by the numbers
- 50K+
- Documents IndexedOver 50,000 case files, precedents, and legal opinions indexed into the Qdrant vector database and queryable from day one.
- 6Weeks
- Full DeliveryFrom Discovery Workshop to production deployment in 6 weeks, including document ingestion, access control, and user testing.
Results
How a law firm built a RAG pipeline with LangChain.js and Qdrant to make 50,000+ case files and precedent documents searchable in natural language — reducing the time associates spend on document research per matter.
- Research Time Per Matter: Faster - Associates locate relevant precedents in minutes rather than hours of manual document searching across repositories.
- Answers with Citations: Sourced - Every response references the originating document with a direct link — associates can verify the source immediately.
- Role-Based Access Control: ✓ - Document-level access control ensures associates only retrieve content from matters they are authorised to work on.
- Continuous Ingestion: Auto - New case documents added to the DMS are automatically re-ingested into the knowledge base without manual intervention.
- LangChain.js
- Qdrant Vector Database
- Azure OpenAI Service
- Node.js / TypeScript
- Azure Blob Storage
Technologies Used
Client Voice
"We have years of case work sitting in document repositories that associates could never search effectively. Techseria built a system that makes all of it accessible in natural language — associates now find relevant precedents in minutes rather than half a morning. The sourced citations were important to us: the system points back to the original document so our lawyers can read the full context themselves."