Paralegal agentic AI platform
Sensitive entities masked before the model ever sees them, with answers grounded in domain documents.
- LangChain
- Gemini
- Grok
- NER masking
- RAG
- FastAPI
- React
Legal documents are exactly the place an LLM shouldn't guess, but they're also full of names, dates, and case-specific detail that can't be sent to a model carelessly, and multi-step legal reasoning breaks down fast if the system loses track of what it's already established.
Mask sensitive entities before anything reaches the model, so privacy isn't a policy promise, it's structurally true of the pipeline. Ground every claim in retrieved source documents rather than model memory, so a user can trace an answer back to the passage it came from instead of taking it on faith.
LangChain-orchestrated multi-step reasoning over Gemini and Grok, NER-based entity extraction and masking as a pre-processing stage ahead of any model call, and retrieval-augmented generation over a domain document corpus using vector embeddings and semantic search. Python and FastAPI services handle ingestion, chunking, retrieval, and inference; a React and TypeScript interface surfaces the multi-step reasoning and citations rather than hiding them behind a single chat bubble.
A production conversational assistant for legal workflows that reasons across multiple steps while keeping sensitive data out of the model path entirely.