Developing an enterprise-grade document chat assistant extracting legal metadata with fallback verification logic.
A legal technology firm needed an AI assistant capable of accurately answering questions about complex contract documents — clauses, obligations, and deadlines — without hallucinating non-existent terms or misattributing provisions between parties.
We built a RAG (Retrieval-Augmented Generation) pipeline using Pinecone for document embedding storage and OpenAI for answer generation. Each document was chunked semantically at clause boundaries to preserve context windows. A confidence-scored citation layer required the model to reference specific document passages before generating answers, with a fallback path that responded with a structured 'not found' signal rather than hallucinating. Helicone provided real-time LLM observability and cost monitoring.
Semantic accuracy — measured as retrieval correctness against a ground-truth QA dataset — reached 99.9% on in-distribution legal document types. Hallucination rate dropped to near-zero after implementing the mandatory citation requirement. Legal teams reported a 60% reduction in manual clause-search time.
ⓘ This is a concept and illustrative case study. It represents Mehrium's engineering design approach and capability depth — not a verified or named client engagement. No client relationship or endorsement is implied.
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