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Hebbia — Enterprise AI Agents · fobi

Live

Coming soon

Agriculture Tech
Autonomy
Avatars & Digital Humans
Biotech / Synthetic Biology
Blockchain / Crypto
Brain-Computer Interfaces
Climate Tech
Cloud & Edge Computing

Commerce
Cybersecurity
Defense
Digital Identity
Education Tech
Energy
Fashion & Textiles
Food Tech

Healthcare Systems
Longevity & Human Enhancement
Materials Science
Mobility
Quantum Computing
Smart Homes
Space Tech
Wearables

Hebbia

Hebbia builds AI software for knowledge work, letting users run complex multi-step queries across massive collections of structured and unstructured documents. Its flagship product, Matrix, uses a spreadsheet-like interface to build agents that complete end-to-end research tasks over filings, PDFs, transcripts, and audio. Founded in 2020 by George Sivulka, who left a Stanford PhD program to start the company, Hebbia sells primarily to asset managers, investment banks, and other financial and legal institutions.

Hebbia raised a $130M Series B led by Andreessen Horowitz at a roughly $700M valuation in 2024, reportedly on profitable revenue, making it a standout among enterprise AI-agent companies serving finance and legal knowledge work.

hebbia.com Enterprise AI Agents

United Statesprivate

At a glance

Total raised

$161M

last round series b

Headcount

—

no public estimate on record

Headquarters

New York

United States

Founded

2020

6 years active

Status

Private

Enterprise AI Agents

Latest valuation

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AI bull / bear

Bull case

Hebbia built what most AI-agent companies still only promise: a product that profit-obsessed, skeptical financial institutions pay for at scale. Matrix's spreadsheet-like interface is the core insight — analysts do not want chat, they want every document decomposed into rows and columns with traceable sources — and Hebbia shaped agentic document analysis into the artifact finance already trusts. Selling to asset managers, investment banks, and legal institutions since its 2020 founding, the company raised a $130M Series B led by Andreessen Horowitz in July 2024 at a roughly $700M valuation, reportedly on profitable revenue — a phrase nearly extinct in this sector. The buyer base is the best in software: institutions with enormous willingness to pay, brutal compliance requirements that screen out flimsy rivals, and workflows — diligence, filings review, transcript analysis — where errors are expensive and auditability is mandatory. George Sivulka's team turned that trust barrier into a moat, and the conservative valuation leaves uncommon room to grow into rather than out of.

Bear case

Hebbia's core loop — querying large document sets and returning traceable answers — sits directly in the blast radius of frontier-model progress. Million-token context windows, native file analysis inside the major assistants' enterprise tiers, and every bank's internal AI team all shrink the gap between Matrix and good-enough defaults, while frontier labs court financial institutions directly. The July 2024 round at roughly $700M is now an old mark in a market where vertical-AI peers re-rated to multi-billion valuations; either Hebbia has quietly compounded or it has stalled, and the silence since is itself information. Concentration risks stack: one flagship product, a founder-CEO singularly identified with the company, and a customer base of consolidating financial institutions that cut vendors ruthlessly in downturns. Profitability discipline is admirable, but in a land-grab market it can simply mean ceding territory to rivals who outspend, out-hire, and out-announce — and enterprise software rarely rewards the cautious second act.

Generated by editorial-sourced · June 12, 2026Refreshes quarterly · see disclosures