Hebbia
Multi agent AI platform for finance and legal work whose Matrix runs an agent swarm over thousands of documents, synthesizing cited, auditable answers that would take analyst teams days.
Hebbia is an artificial intelligence platform built for the most document heavy work in finance and law. Founded out of Stanford by chief executive George Sivulka, the company raised a one hundred thirty million dollar Series B led by Andreessen Horowitz at a seven hundred million dollar valuation, with backers including Peter Thiel, Index Ventures, and Google Ventures. Its flagship product, Matrix, is used by more than a third of the largest asset managers, along with firms like KKR, Centerview Partners, MetLife, and even the United States Air Force, and it has processed more than one billion pages.
What sets Hebbia apart is its agent swarm architecture. Rather than routing a question to a single model, Matrix decomposes a complex query into structured analytical steps and orchestrates many agents in parallel, each reasoning over full documents rather than short excerpts. The company built a distributed orchestration engine that gives large language models an effectively infinite context window, which lets Matrix reason across thousands of proprietary documents at once and, on a rigorous finance and legal benchmark, reach far higher accuracy than out of the box retrieval augmented generation.
Matrix presents its work in a transparent grid where every answer carries citations and every reasoning step is visible, so analysts can audit, validate, and co-work with the agents rather than trust a black box. It routes each task to the best model for the job across OpenAI and Anthropic, and it integrates with the financial data providers professionals already use, including FactSet, PitchBook, S&P Capital IQ, and Preqin, alongside a firm's private documents and public filings. Hebbia never trains on customer data.
The result is an AI associate that compresses work once measured in analyst days into seconds. Investment bankers use Matrix to build first draft pitch decks and confidential information memorandums that match firm templates, private credit teams extract loan terms and covenants, and private equity teams run screening and due diligence. With its 2025 acquisition of FlashDocs, Hebbia added automated generation of enterprise grade presentations, closing the last mile from research to finished deliverable.
Vendor details
Canonical URL
https://hebbia.com
Category
Enterprise operations agent
Company status
independent
Use cases & customers
In practice
A private credit team needs to pull covenants and loan terms from a portfolio of agreements before a committee meeting. Matrix reads every document in parallel and returns the terms in a cited, auditable grid within minutes.
An investment banker is prepping for a client meeting on a beverage company. They ask Matrix to synthesize key takeaways across two hundred earnings calls and filings, and it drafts a formatted brief with sources attached.
A law firm must review a hundred credit agreements for clauses that deviate from its standard template. Hebbia's agent swarm compares them all at once, flags the outliers, and categorizes each by risk level.
Agentic Index coverage score
6.0 / 14 capabilities · 43%
| Integrations & Tool CallingHebbia integrates with the financial data providers analysts already use, including FactSet, PitchBook, S&P Capital IQ, and Preqin, alongside private documents and public filings, strong domain integration short of a broad cross industry connector catalog, so partial. | Partial |
|---|---|
| Workflow OrchestrationHebbia Matrix runs an agent swarm that decomposes a complex query into analytical steps and orchestrates many agents in parallel to execute end to end finance and legal workflows, a genuine autonomous multi agent system, so full. | Full |
| Knowledge Grounding & RAGHebbia synthesizes across thousands of documents with an effectively infinite context window and returns answers carrying citations at every step, comprehensive citation grounded retrieval that outperforms standard retrieval augmented generation, so full. | Full |
| Human Oversight & GuardrailsHebbia keeps humans in control with expert review reserved for high stakes outputs and lets analysts validate and co-work with agents, a human review pattern short of a runtime governance engine, so partial. | Partial |
| Security, Identity & GovernanceHebbia maintains a trust center, states certification to relevant industry standards, encrypts data end to end, and never trains on customer data, though specific named certifications were not individually verifiable, so partial. | Partial |
| Observability & AuditabilityHebbia presents work in a transparent grid that shows every reasoning step with citations available throughout, making each workflow auditable and traceable, comprehensive observability, so full. | Full |
| Memory & State PersistenceHebbia gives models an effectively infinite context window for a single task, but persistent cross session agent memory could not be verified. | Unable to verify |
| Deployment & Data ResidencyHebbia is delivered as a cloud platform, and a self host, on premises, or dedicated data residency deployment could not be verified. | Unable to verify |
| Prebuilt Agents, Templates & PacksHebbia lets firms build custom agents in a governed sandbox and replicate firm specific processes and templates, a customizable workflow layer short of a browsable marketplace of cloneable agents, so partial. | Partial |
| Triggers & Channel CoverageHebbia Matrix is driven by analysts inside its interface, so broad multichannel or event driven trigger coverage could not be verified. | Unable to verify |
| Model Flexibility & RoutingHebbia intelligently routes each task to the best model for the job across OpenAI and Anthropic large language models, genuine multi provider routing per task, so full. | Full |
| APIs, SDKs & MCP ExtensibilityHebbia offers a sandbox to build and deploy custom agents, but a public developer application programming interface, software development kit, or Model Context Protocol server could not be verified. | Unable to verify |
| Testing, Debugging & OptimizationHebbia publishes internal accuracy benchmarks and advises pilots to test for drift, but first class user facing testing, debugging, or optimization tooling could not be verified. | Unable to verify |
| Browser & Computer UseFor Hebbia, general browser or computer use could not be verified, since its agents operate over document sets rather than controlling a computer. | Unable to verify |
The Agentic Index coverage score grades every vendor Full, Partial or Unable to verify against the same 14 buyer facing capabilities, from public evidence only. Each capability links to how all vendors in the index score on it. How this evidence is graded
Pricing
Contact for pricing
Related vendors
- 11th Estate — Agentic platform whose AI engine scans markets, matches an…
- Aera Technology — Decision intelligence platform where an always on agent executes…
- Akro AI — On premise operational intelligence that automates document heavy…
- alfred_ — AI executive assistant that triages the inbox overnight and scores…
- Algebra AI — Delivery led provider that builds and runs human governed AI agents…
- Alloy.ai — Commerce intelligence system for consumer brands that unifies four…
Alternatives to Hebbia
The closest documented capability profiles to Hebbia among enterprise operations agents tracked by Agentic Index, ordered by similarity on the same 14 point evidence the rankings use. No vendor pays for placement.
- Brightwave4.0 / 14A lighter documented profile than HebbiaHebbia vs Brightwave →
- Denki4.5 / 14Adds documented Triggers & Channel Coverage
- HackerEarth5.5 / 14Adds documented Triggers & Channel Coverage and Testing, Debugging & Optimization
- Nominal5.5 / 14Adds documented Triggers & Channel Coverage and Testing, Debugging & Optimization
- ParkourSC5.5 / 14Adds documented Triggers & Channel Coverage and Testing, Debugging & Optimization
- Certo7.0 / 14Adds documented Memory & State Persistence and Triggers & Channel Coverage, among others
Similarity is computed from each vendor's Agentic Index coverage score evidence, axis by axis, not from the totals. How this evidence is graded