Project Overview
YuktiAI (LawBOT) is a multi-agent workspace explicitly designed for Indian litigation practice. The platform uses specialized agents—Research, Drafting, and Case Management—operating within isolated, skill-based execution environments heavily reinforced against hallucinating legal citations and facts.
The Problem
Generative AI is notoriously unreliable in legal applications due to hallucinations involving fake case laws and incorrect facts. In Indian litigation practice, where precedent and exact citations are paramount, this poses severe professional risks and renders standard LLMs virtually unusable for serious legal work.
The Solution
YuktiAI utilizes a deterministic tool layer and strict citation validation integrating with the Indian Kanoon API. It enforces a strict 'no fabrication' rule—if data cannot be computationally verified, it returns empty results or explicit errors, guaranteeing a remarkably low hallucination risk score of 1/10.
Architecture & Infrastructure
- Multi-agent workspaces using OpenClaw-style architecture
- FastAPI backend exposing endpoints for summarization and citation validation
- Static front-end interface interacting asynchronously with the AI core
- API integrations for deterministic legal search via Indian Kanoon
Key Features
- Legal Search via Indian Kanoon Integration
- Structured Document Summarization
- Citation Formatting and Validation
- Isolated attorney workspaces with local memory and skill profiles
Tech Deep Dives
Hallucination Prevention
YuktiAI strictly enforces rules against fabricating case citations, legal facts, court status, and transcriptions. Each claim must be computationally tracked back to verified API responses. By decoupling the generative layer from the fact-retrieval layer, the system maintains strict fidelity to real-world Indian law.
Multi-Agent Architecture
Divided into Research, Drafting, and Case Management agents, allowing discrete tasks to be separated and handled by targeted prompts rather than a massive single context architecture. This separation of concerns significantly increases output reliability and logical consistency across complex legal tasks.
Challenges & Outcome
Challenge: Architecting AI agents that adhere strictly to deterministic outputs for legal research while retaining the generative flexibility required for conversational case management and document drafting.
Outcome: A robust platform featuring a functional litigation intelligence core capable of legal search, document template filling, and highly reliable citation checking.
Future Roadmap
- Adversarial Strategy Simulation
- SCC Online / Manupatra licensed integrations
- Full orchestration gateway for multi-attorney collaboration