Document Processing · Enterprise Knowledge Search
Hebbia needed a way to help finance and legal professionals search, interpret, and synthesize huge volumes of private offline documents where standard RAG systems miss implicit answers and manual review takes days or weeks.
Reported impact
Hebbia raised benchmark accuracy from 68% to 92%, automated large portions of finance and legal analysis, and saved users tens of hours per deal while enabling entirely new live-deal and historical-analysis workflows on private data.
Hebbia built Matrix as a multi-agent AI platform for finance and legal work, using OpenAI models as specialized reasoning engines rather than relying on a single chatbot. Matrix runs a distributed orchestration engine that breaks complex work into structured steps, routes tasks to the best model, processes entire documents rather than small excerpts, and returns answers with citations. OpenAI o1 handles advanced reasoning, GPT-4o supports broader processing, and smaller models take on narrow tasks, while the orchestration layer gives the system an effectively infinite context window over secure offline data. This lets private-equity, banking, and legal teams run due diligence, covenant extraction, memo drafting, and live-deal analysis on data rooms, contracts, and filings that are too large or implicit for ordinary search. The platform also builds self-improving indexes and feedback loops so AI adoption expands with real workflow value instead of isolated pilots.
Private documents from data rooms, contracts, filings, and research sources are indexed by Matrix, then routed through the distributed orchestration engine. The engine assigns reasoning and extraction tasks to OpenAI o1, GPT-4o, and smaller models, synthesizes cited answers, and returns memos, clause interpretations, and due-diligence outputs to end users.
"Hebbia with o1 achieves 92% accuracy-up from 68% with out-of-the-box RAG"
"Investment bankers save 30-40 hours per deal"
"Matrix, a multi-agent AI platform designed to handle the most complex financial and legal workflows end-to-end."
"The engine overcomes the limitations of RAG and effectively gives OpenAI's models an "infinite" context window"
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Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
The embedded Organization metadata lists streetAddress '233 Spring Street', addressLocality 'New York', addressCountry 'US', and numberOfEmployees value 120.
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