Hebbia
Hebbia analyzes large sets of private documents for asset managers, private equity firms, investment banks, credit investors and law firms. Deal teams use it to ask the same questions of hundreds of documents at once.
Its Matrix product lays documents out as rows and questions as columns, and agents fill in every cell by reading each page. Every answer cites the exact page, paragraph and sentence it came from, and analysts can flag or overwrite any cell. Sources range from SEC filings to FactSet, PitchBook and a firm's own SharePoint, and OpenAI's customer story on Hebbia names the OpenAI models it uses.
Customers include Morgan Stanley, Centerview Partners and MetLife. Hebbia lists SOC 2 Type 2, ISO 27001 and ISO/IEC 42001, says neither it nor its model providers train on customer data, and offers United States or European Union processing with dedicated tenants.
Capability Axes
Capability grades
15 of 15 axes rated · 12 graded A or B
Matrix breaks a complex question into structured steps and sends agents to read full documents rather than retrieved excerpts, assembling a cited answer, and the Max assistant works across a deal team's files the same way. Without the models, a document repository with an unfillable spreadsheet on top remains.
Output lands in a grid or cited answer for a professional to review rather than acting on anything, and every cell can be inspected and overwritten. Confidence per cell and sampling guidance for grids of thousands of answers are not described.
Sentence level citation ties every output to its source, and OpenAI's customer story reports 92 percent accuracy with o1 against 68 percent for standard retrieval tools. That figure has no published method or test set, and error rates by document type are absent.
Morgan Stanley, MetLife and Centerview Partners appear as logos with no figure attached. OpenAI's March 2025 customer story reports 30 to 40 hours saved per deal for bankers and a 75 percent cut in credit agreement review for law firms, attributed to roles rather than named firms.
Documents, prompts and outputs never train Hebbia's models or any model provider's, and each customer's data sits in a siloed environment isolated from the rest. Supplier retention terms and deletion timing are not stated.
Both a data processing agreement and a privacy policy are published, Hebbia staff have no direct access to customer data, and customers can see who accessed their data and when. Retention schedules and a subprocessor list do not appear on the security page.
SOC 2 Type 2 and ISO 27001:2022 are named with zero trust, just in time production access and a Vanta trust center at trust.hebbia.ai. Retrieval of reports without a request is not shown.
Adviser and broker dealer recordkeeping, research conduct rules and information barriers govern the work its users produce, and nothing explains how generated analysis enters supervised records. The privacy compliance listed covers only the company's own processing.
ISO 42001:2023, the certifiable AI management system standard, is listed, a governance framework with a named process behind it. Evaluation across document types, including scanned exhibits and poorly structured filings, is unpublished.
Cell level checks let a professional catch an error before it reaches a client. When a wrong extraction feeds an investment memo, the party harmed is usually the client of the client, and no accuracy guarantee or stated obligation covers that case.
OpenAI's customer story names o1, o3 mini and GPT 4o and explains how tasks route between them. Hebbia's own pages carry no subprocessor list naming model providers and hosts.
FactSet, S&P Capital IQ and PitchBook feed in, alongside document stores such as SharePoint and Box and expert networks such as Third Bridge. Work is read in rather than written back, and no marketplace listing or public API documentation confirms connection depth.
Storage and inference can run regionally in the United States or the European Union, and a dedicated tenant deployment is offered. On premises hosting is not offered, and cloud providers and specific regions are unnamed.
A demo request precedes any price, and a linked fair use policy hints at usage limits behind the license. Whether data connections such as FactSet need the firm's own license is not addressed.
Asset management, investment banking and legal teams each have material, with corporate strategy alongside and OHA and New Mountain Capital among named users. Bank lending, insurance operations and retail institutions fall outside an institutional investment product.
Product updates
Material product, regulatory, evidence and commercial changes at Hebbia, each verified against a live source and tagged to the capability axis it bears on. Funding rounds and awards are not product changes and are not logged.
Hebbia now integrates with Octus through the Octus MCP Connector, which links every Octus product a firm subscribes to into Hebbia at once. Credit teams can query Octus reporting and legal analysis, private company financials and capital structures, covenant and restructuring data, deal documents and court filings alongside their own deal history, with answers cited to the source.
Hebbia's October 2026 release adds memory to Max, its agent, which stores and recalls context across conversations under user controls and an org level admin switch, version history with rollback for decks and docs made in Max, and a Microsoft Word plugin for running workflows and drafting inside Word. Matrix columns can now run conditionally on a prior column's output, ICE market data extends to Tokyo Stock Exchange and ASX tickers, and Intralinks data room content and Dynamo pipeline documents now sync into Hebbia.
Hebbia introduced Headless Hebbia, an API and MCP server that brings its retrieval, orchestration and analysis into firms' own applications and into assistants such as Claude and ChatGPT. Retrieval and analysis run step by step so developers can test a workflow and users can check findings against source material, the system maps relationships across sources such as subsidiaries and differently named filings, and it supplies evidence to each model call so applications can work over large collections with smaller context windows.
Compared With
Most editorial comparisons pair two vendors the index assesses as direct competitors for the same buyer. Some pair vendors that are adjacent rather than rival, where the useful question is where one ends and the other begins. Each carries a verdict, the buyer conditions that favor each vendor, and a graded side by side.
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Pricing
Vendor-published figures are labeled as such. Figures labeled “Estimated” are derived from third-party sources and have not been confirmed by the vendor.
No pricing data has been verified for this vendor. Pricing information will be published here once confirmed through vendor disclosure or third-party estimation.