AI analyst agent for financial services that reads, analyzes, and extracts insights from complex documents like SEC filings and contracts.
An AI analyst that reads and understands complex documents — processes contracts, filings, and reports that would take humans hours.
Hebbia is an Enterprise Agents AI analyst platform that reads, analyzes, and extracts insights from complex financial documents at scale, with enterprise pricing available on request. It is built for hedge funds, private equity firms, investment banks, and corporate finance teams handling SEC filings, credit agreements, and M&A documents.
Founded in 2020 by Stanford and Harvard alumnus George Sivulka, Hebbia has raised over $161 million in total funding, including a $130 million Series B led by Andreessen Horowitz in 2024 at a reported $700 million valuation. The platform's flagship Matrix interface allows analysts to ask structured questions across thousands of documents simultaneously, returning answers in spreadsheet-like comparison tables. Each cell links back to the exact page and paragraph in the source document, providing the auditability that regulated financial institutions require. Hebbia processes documents in formats ranging from 10-Ks and credit agreements to earnings transcripts, indentures, and proprietary investment memos, and can ingest entire deal rooms containing hundreds of contracts in a single workflow.
Hebbia is used by major financial institutions including Centerview Partners, the U.S. Air Force, and reportedly nearly a third of the largest 50 U.S. asset managers, with customers including hedge funds and Tier 1 investment banks. Based on our analysis of 870+ AI tools, Hebbia is the most specialized financial document analysis agent in the Enterprise Agents category, distinguished by its Matrix multi-document workflow and citation-first architecture. Compared to Harvey AI (legal-focused) and Elicit (academic research), Hebbia targets investment professionals who need to synthesize quantitative and qualitative information from large document sets under deal-cycle deadlines. The platform integrates with internal data rooms and supports SOC 2 Type II compliance, making it suitable for firms with strict data governance requirements. While the enterprise-only pricing puts it out of reach for individual analysts and small firms, customers report order-of-magnitude productivity gains on due diligence and research workflows that would otherwise require teams of junior analysts working over multiple days.
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AI analyst agent for financial services that reads, analyzes, and extracts insights from complex documents like SEC filings and contracts.
Analyze hundreds of documents in parallel with structured query rows and document columns, producing spreadsheet-like comparison tables. Each Matrix cell is independently sourced and citable, allowing analysts to drill from a comparison view directly into the underlying document evidence.
Use Case:
Extract specific covenant terms from 50 credit agreements and receive a structured comparison table in minutes rather than hours of manual review
Native parsing of finance-specific document types including SEC filings, credit agreements, indentures, M&A purchase agreements, and earnings transcripts. The model understands financial structure conventions like footnote references, schedule attachments, and defined-term cross-references that trip up general LLMs.
Use Case:
Parse 10-K filings to identify risk factors, revenue trends, and regulatory concerns across multiple companies for investment research
Every answer includes citations linking to the exact page and paragraph in the original document, with one-click navigation to the cited passage. This citation-first architecture is a core compliance feature that enables deployment in regulated financial institutions.
Use Case:
Present investment committee recommendations with precise source references that stakeholders can independently verify
Process thousands of documents simultaneously through enterprise-grade infrastructure that scales horizontally with document volume. The platform handles full deal rooms and multi-year filing histories in a single workflow rather than requiring document-by-document interaction.
Use Case:
Analyze entire deal room of documents for due diligence, extracting key terms and risks from hundreds of contracts and agreements
Build firm-specific knowledge bases from proprietary investment memos, deal history, and internal research that remain isolated from other tenants. Collections support fine-grained access controls so different teams can maintain segregated document spaces within the same Hebbia deployment.
Use Case:
Create a searchable database of past investment memos and due diligence reports to inform future deal analysis
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Hebbia continues to expand its Matrix workflow capabilities and has broadened from pure financial services into adjacent enterprise verticals including government (notably a U.S. Air Force engagement) and large-cap asset management. Following its 2024 Series B led by Andreessen Horowitz at a reported $700M valuation, the company has been investing in larger-scale agentic workflows that chain together multi-step analytical tasks across document sets.
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