Prepare the archive
We built an ingestion pipeline that extracts text from the firm’s documents, splits it into meaningful passages and tags each with its matter and permissions.
A retrieval-augmented generation (RAG) system that lets a top-50 law firm search its precedents and contracts in seconds, with every answer linked to its source.
Chen & Associates holds a vast archive of case files, contracts and internal memos. Finding the right precedent meant keyword searches across separate systems and hours of reading, much of it carried out by senior lawyers whose time is the firm’s most valuable resource.
Off-the-shelf AI chat tools were not an option. The firm needed answers grounded in its own documents, a citation for every claim, and strict controls over who could see which material.
We built an ingestion pipeline that extracts text from the firm’s documents, splits it into meaningful passages and tags each with its matter and permissions.
Passages are embedded and stored in a vector database, so searches match legal concepts rather than exact keywords.
The LLM answers only from retrieved passages and cites each source document, so lawyers can verify any statement in one click.
A secure Next.js application gives the legal team a familiar search experience, with access controls that mirror the firm’s existing permissions.
“The RAG pipeline they deployed for our legal team is flawless. It’s like having a senior partner who has memorized every contract we’ve ever signed.”
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