A retrieval-augmented GenAI assistant that lets lawyers ask questions of dense legal documents and get instant, source-grounded answers and summaries.
Lawyers at a leading firm spent hours reading, cross-referencing, and summarizing long, dense legal and regulatory documents — high-value expertise spent on low-leverage reading.
The firm wanted to extract, summarize, and reason over large document sets in natural language, without sacrificing accuracy or confidentiality. Generic chatbots were a non-starter: answers had to be grounded in the actual documents and traceable to their source.
We built an extraction pipeline to parse large, complex documents into clean, structured text — the foundation that determines how good every downstream answer can be.
Document content is embedded into a vector store, so the LLM answers from the relevant passages of the actual documents (RAG) rather than from its training data — keeping responses grounded and citable.
A chat interface supports natural-language Q&A and summarization, with security and access controls built in from the start to meet confidentiality requirements.
The single most important architectural decision in this project was how documents get chunked for retrieval. Legal documents have meaningful structure — sections, clauses, defined terms, exceptions that modify a preceding clause — and naive fixed-size chunking regularly splits a clause from the exception that changes its meaning, producing retrieval results that look relevant but are missing the qualifying context. The chunking pipeline parses document structure (sections, sub-clauses, cross-references to defined terms) and chunks along those boundaries, keeping a clause and its directly relevant modifiers together even when that means variable chunk sizes.
Retrieved chunks are embedded into a vector store, and the retrieval step over-fetches slightly and re-ranks based on structural relevance (is this chunk from the same section as the top match, does it reference a defined term used in the query) before passing context to the generation step. Every answer the system produces is grounded with explicit source attribution — which document, which section, which clause — so a lawyer reviewing the answer can verify it against the source rather than trusting the model's summary at face value. This attribution requirement shaped the chunking and retrieval design from the start, not as an afterthought.
The interface lets lawyers ask natural-language questions across a document set and get answers with inline citations to source clauses, as well as request summaries of specific sections or documents. Summaries are generated with the same source-attribution requirement — a summary of an indemnification clause links back to the clause itself. The interface was deliberately kept narrow in scope (Q&A and summarisation over a defined document set) rather than a general-purpose chat assistant, because the verifiability requirements that make this useful for legal work don't transfer to open-ended conversation.
Lawyers could ask a question and get an instant, source-grounded answer or summary from documents that previously took hours to work through — keeping expert time on judgement and advice rather than reading.
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