The Architecture of
Indian Legal Intelligence
Discover the workflow powering LawAssist.ai. We combine self-hosted open-source LLMs with real-time statutory indexes to retrieve precedents with absolute precision.
Core Intelligence Engine
Case Parameter Extraction
Paste case facts or upload plain text pleadings. Our self-hosted Qwen/Llama models autonomously extract case types, core legal issues, central Acts, and sections to map search parameters instantly.
Kanoon Querying
We translate the extracted legal issues, Acts, and intent parameters into structured queries sent directly to the Indian Kanoon API.
Judgment Re-ranking
Search engines return dozens of judgments. Our LLM reads the retrieved texts and re-ranks them by actual fact similarity to your client case.
AI Judgment Summaries
Save time reading length cases. The LLM extracts facts, legal issues, final decisions, and isolates the court's core ratio decidendi.
- Facts extraction
- Ratio decidendi isolation
- Why it matches explanations
Our path toward a dedicated
Indian Legal LLM
Five phases mapping our evolution from an AI search assistant to a fully fine-tuned legal model.
Phase 1
AI Kanoon Search
Current MVPPhase 2
OCR & PDF Analysis
Phase 3
Proprietary Legal RAG
Phase 4
Indian Legal LLM
Phase 5
Complete Legal AI
The New Standard
Why advocates are looking forward to semantic AI tools.
| Feature | Traditional Search | LawAssist.ai |
|---|---|---|
| Search Paradigm | Rigid keyword matching only | Plain-text case understanding |
| Statutory Mapping | Manual searching of Acts | Instant issue, Act, and section extraction |
| Judgment Analysis | Manual page reading | Facts, decisions, & ratio decidendi isolated |
| Re-ranking Engine | Sorted by date or hits only | Re-ranked by fact similarity to case facts |
| Interface design | Cluttered portals | Modern, focused, clean workspace |
| Data Security | Shared data indexing | Zero-retention data pipeline commitment |
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