Many legal teams today face an overwhelming volume of documents — from court transcripts and contracts to internal memos and regulatory correspondence. While AI tools promise to streamline this work, most fall short when it comes to precision. Popular language models tend to “hallucinate,” generating plausible-sounding but unverified content — a critical flaw in legal practice, where every statement must be backed by a source.
The real issue isn’t lack of data — it’s lack of reliable, transparent, and precise access to that data. That’s the problem we solved: making critical legal information truly accessible, verifiable, and safe to use in decision-making.
Client Request
The client needed a tool to efficiently process large volumes of legal documents. The goal was to enable precise search through court records, legal memos, correspondence with government agencies, and other legal materials. A key requirement was the ability to ask questions in natural language and receive trustworthy answers — grounded in actual documents, not generated by a language model.
The Problem
Law firms and legal departments deal with hundreds or thousands of documents. Searching through them manually is time-consuming and error-prone. While many LLM-based AI tools offer quick answers, they often struggle with reliability — hallucinating details or inventing information. In legal practice, where accuracy and source validation are critical, this is simply not acceptable.
In legal practice, there’s no room for AI hallucinations. A single incorrect citation or fabricated precedent can undermine an entire case. That’s why we needed a fundamentally different approach to AI search — one that prioritizes verification over generation.
Evgeny Smirnov, Co-founder
What We Did
We built an intelligent search system designed for complex, natural-language queries. Users can ask questions like “Show me all hearings where topic X was discussed” or “Who was listed as a witness in case Y?” The system finds highly relevant document fragments with precision — not by generating responses, but by searching through verified content.
Our architecture was deliberately designed to minimize hallucinations: the language model is as a tool used purely for search assistance, never for generating final answers. We implemented algorithms to rank, rerank, and retrieve documents by multiple criteria and return either a precise result or an honest “no answer” when the data is insufficient.
Project Highlights
What sets our system apart from traditional LLM-based tools is its focus on verifiability over generation. Instead of generating polished but unreliable text, our system delivers factual results based on real legal content. If a reliable answer doesn’t exist, the system tells you so — a level of transparency that’s essential in legal environments.
What we delivered:
- A system that provides direct source links, citations, and document previews for every answer
- LLMs used only for search parsing — not for text generation — eliminating hallucinations
- A built-in fallback mechanism that returns “no answer” when appropriate
- Automated metadata extraction to better structure documents and improve search precision
- A fast and flexible deployment, enabled by deep domain knowledge and task-specific design
The Result
The client received a dependable tool for analyzing legal documents. Not just another chatbot, but a robust system built for trust. It either returns a verified answer — or transparently acknowledges the lack of data. As a result, the time needed to find relevant information dropped significantly, error risks decreased, and legal teams were able to make decisions faster and with greater confidence.
