About
Hebbia builds an AI platform for asset managers, investment banks, law firms, and Fortune 500 companies. Its AI processes full documents and synthesizes answers with citations, differentiating it from tools that analyze only excerpts.
Market
Hebbia competes in enterprise generative-AI and knowledge-work software, with a vertical focus on institutional finance; it sells primarily to asset managers, investment banks, and other financial institutions. It differentiates through Matrix’s ability to process multiple unlimited-length files, produce spreadsheet-like comparison tables, and combine multi-agent orchestration with auditable knowledge charts for complex research, diligence, and compliance workflows.
Hebbia primarily targets enterprise financial institutions—especially asset managers, investment banks, and institutional-finance teams. Its likely users and buyers are investors, bankers, research and diligence teams, and compliance or strategy professionals handling large volumes of private and financial documents.
At a Glance
Problem
Finance and other high-stakes knowledge-work teams must turn huge, fragmented sets of private documents, public filings, and financial data into defensible decisions. The pain is not simply finding a fact: it is repeatedly decomposing complex research, checking every claim, and producing client- or management-ready work under time pressure. That creates expensive analyst labor, slower deal and research cycles, and risk of missed information or unsupported conclusions.
Hebbia’s killer use case is diligence and document-heavy decision work—such as comparing companies or reviewing thousands of pages for an investment or strategy decision—where traceability matters. It targets investors, bankers, advisors, and Fortune 500 teams making high-stakes decisions, and has become a preferred platform for due diligence and document analysis in financial services.
Product / Service
Hebbia is an integrated enterprise AI platform for finance and knowledge work. Users can ask questions across thousands of pages and receive finance-formatted answers with citations; Matrix analyzes documents or companies at scale with traceability to each finding; and Draft turns the analysis into branded spreadsheets, presentations, and reports. The platform also connects private documents, public filings, and financial-data providers through integrations, APIs, and an MCP connector.
Its Skills and Agents encode a firm’s processes so recurring workflows can run continuously, while Projects gives people and agents a shared workspace. The benefit is a repeatable, auditable layer for research and decision-making: Hebbia’s agents show their work and link claims back to original document quotations, reducing manual synthesis and making outputs easier to review and share.
Market
Hebbia competes in enterprise AI, financial research, document intelligence, and workflow automation. Its closest alternatives include AlphaSense and Glean, alongside broader knowledge-management and enterprise-search products such as Guru and Bloomreach; general-purpose LLM stacks with retrieval are another substitute. Hebbia differentiates around finance-specific workflows, large-scale document reasoning, source traceability, and process automation.
The company is not presented as pre-revenue: its materials say it is deployed at scale at leading asset managers, law firms, banks, and Fortune 100 companies, and describe it as trusted by leading investors, bankers, advisors, and Fortune 500 companies. It has also raised a $130 million Series B led by Andreessen Horowitz, with Index Ventures, Google Ventures, and Peter Thiel participating. The available evidence does not disclose revenue, customer counts, or quantified ROI, so traction is best characterized as substantial enterprise adoption rather than a precisely measurable revenue scale.
Founders & Leadership
Funding History
Peter Thiel, Naval Ravikant, Kevin Hartz, Cory Levy, Floodgate
Radical Ventures, Index Ventures, Raquel Urtasun, Jerry Yang
Andreessen Horowitz (a16z), Index Ventures, Google Ventures, Peter Thiel
Recent News
Hebbia announced an integration with Snowflake that brings institutional structured data directly into Hebbia’s AI-native research and analysis environment.
Hebbia’s July product update focused on making the platform sharper across users’ workflows, data, and connected tools.
Hebbia announced new data partnerships, Matrix capabilities, and workflow improvements across Chat, Projects, and the broader platform.
Hebbia announced an integration with ICE Data Services, making institutional-grade equity and fixed-income pricing data available directly inside finance professionals’ AI analysis workflows.
The integration with SS&C Intralinks DealCentre AI brings secure, governed deal content directly into Hebbia’s AI workspace for financial diligence.
Seyfarth announced a strategic partnership with Hebbia that expands its use of Hebbia’s AI-powered Matrix platform for deal execution and diligence.
Hebbia announced an integration with Preqin, giving platform users direct access to a major source of private-markets data.
Hebbia announced the opening of a San Francisco office in SoMa and welcomed Aabhas Sharma as its Chief Technology Officer.
Through FactSet’s AI Partner Program, the partnership brings FactSet market, company, and estimates data into Hebbia, allowing users to combine structured financial insights with internal and external documents.
Hebbia announced an integration of GPT-5 through Microsoft Azure AI Foundry to support financial analysis and enterprise AI workflows.
Active Roles
23Business Model
Hebbia sells an enterprise AI platform through demo-led, custom quote-based contracts, with enterprise customization and minimum one-year terms. Public pricing is not disclosed; third-party estimates indicate roughly $10,000–$15,000 per seat annually, with larger deployments reaching approximately $100,000 or more per year.
Products
Customers
Tech Stack
Similar Companies
Competitors
Key Investors
Andreessen Horowitz; Index Ventures; Google Ventures; Peter Thiel