AI / LegalTech / Multi-Agent Systems
Samvidhaan
A multi-agent legal research system designed to produce balanced, citation-backed answers about Indian law. Instead of relying on a single LLM response, Samvidhaan uses an AI Council architecture where specialized agents analyze a query before a final reasoning agent synthesizes the response.
AI-powered legal reasoning system for Indian law
Visit live site- AI
- LegalTech
- Multi-Agent
- RAG
Project
- Type
- Client project
- Year
- 2026
Client project
The problem
What was actually broken.
Legal questions rarely have one correct answer, and a single language model asked for one will produce a confident summary that hides the disagreement. For research on Indian law, that is the wrong shape of answer — what is needed is the competing readings, and the sources each rests on.
The approach
- 01Model the question the way a chamber would: several specialised agents examine it independently before anything is written.
- 02Give each agent its own retrieval pass over the legal corpus, so positions are built from sources rather than from recall.
- 03Have a final reasoning agent synthesise the deliberation into one balanced answer that preserves the disagreement.
- 04Attach citations to the response so every claim can be traced back to the passage it came from.
What we built
The capabilities in this build.
- 01Multi-agent architecture
- 02Legal research pipeline
- 03Citation-backed answers
- 04Agent deliberation
- 05Retrieval / knowledge processing
- 06Final answer synthesis
How it is built.
- Architecture
- AI Council, multi-agent
- Reasoning
- Deliberation then synthesis
- Grounding
- Citations on every answer
- Retrieval
- Legal corpus processing
Stack.
- Next.js
- TypeScript
- Python
- Multi-agent orchestration
- Vector search
- PostgreSQL
Outcomes.
- Answers preserve competing legal readings
- Every claim traceable to a source passage
- Deliberation visible rather than hidden
More work.
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