LawyerAI

Law firms operate in a high-stakes environment where factual accuracy is non-negotiable. A hallucinated court ruling or a misquoted statute isn't just embarrassing — it can destroy a case and expose the firm to liability. Yet the available AI tools treated legal work like any other text generation task.

The client came to us with a specific problem: their attorneys were spending 3–4 hours reviewing a single contract. That time was billable, but barely defensible to clients who knew AI could do it faster. The challenge wasn't 'make a chatbot for lawyers' — it was 'build a system that never fabricates a source.'

We designed a RAG (Retrieval-Augmented Generation) pipeline where every AI-generated answer is grounded in retrieved source chunks. Before any response reaches the user, a citation verification layer cross-checks the references — if a source can't be confirmed, the response is blocked. No hallucinations reach production.

The product ships as 7 specialized tools, each doing one job well: contract analysis with clause-by-clause risk scoring, semantic case law search across Czech and Slovak court databases, document drafting with jurisdiction-specific templates, legal Q&A with statute citations, clause comparison between document versions, executive summaries, and a risk matrix output.

We chose Next.js for the frontend for fast SSR, pgvector in PostgreSQL for embeddings storage, and Cohere re-ranking to improve retrieval precision. The OpenAI GPT-4o generation layer uses strict citation grounding prompts that force the model to only reference retrieved chunks.