Hebbia company profile: valuation, funding & revenue

This Hebbia company profile covers the company's valuation, revenue, funding rounds, investors and history. Hebbia works in artificial intelligence, with a focus on AI research and document analysis for finance. It was founded in 2020 and is headquartered in New York, New York, United States. Its latest valuation on record is About US$700 million (July 2024). Every figure links to a public source, and anything the company hasn't disclosed is marked as such.

Revenue

About US$13 million — Annual recurring revenue at the time of the Series B (reported; profitable), as of July 2024. Source: TechCrunch.

Funding history

Hebbia timeline

Key dates from the sources on this page, oldest first. Metric snapshots are listed after the funding events.

• 2020 — Hebbia is founded in New York by George Sivulka (CEO).
• Jul 2024 — Series B: US$130M, valuing the company at About US$700M. Investors: Andreessen Horowitz (lead); Index Ventures, GV, Peter Thiel.

Investors

Overview

Hebbia builds AI software for professionals who analyse large volumes of documents, especially in finance. Its main product, Matrix, lets users ask questions across thousands of files at once, such as data rooms, contracts, filings, earnings transcripts and research reports, and returns answers organised in a spreadsheet-like grid with citations to the source text. Private equity firms, asset managers, investment banks, consultants and law firms use it for due diligence, investment research, deal screening and contract review. Hebbia positions itself as an AI analyst that can do in minutes work that would take junior analysts days.

George Sivulka founded Hebbia in 2020 while pursuing a PhD at Stanford, which he left to build the company. Sivulka previously worked on research at NASA and in academic machine learning labs. Hebbia is headquartered in New York, close to its financial services customers.

Products

• Matrix: a multi-document AI workspace that breaks complex questions into steps, reads every relevant file and fills a grid of answers with citations.
• Agents: AI agents that carry out multi-step research and analysis workflows.
• Integrations: connections to data rooms, document management systems and market data sources.
• Presentation tools: after acquiring FlashDocs, tools to turn analysis into slides.

Funding

Hebbia raised a US$130 million Series B in July 2024 at a valuation of about US$700 million, led by Andreessen Horowitz with Index Ventures, GV and Peter Thiel participating. According to TechCrunch, it had about US$13 million in annual recurring revenue and was profitable when pitching investors, implying a valuation of roughly 54 times ARR. Total funding was about US$161 million. We haven't found a newer priced round.

Business model

Hebbia sells enterprise subscriptions, typically to financial institutions and professional services firms, priced by seats and usage. Contracts can grow significantly as teams across a firm adopt the product. Hebbia uses models from leading AI labs combined with its own retrieval and orchestration technology.

Competition

Hebbia competes with financial AI and research platforms such as AlphaSense, Rogo and Brightwave; with legal AI companies such as Harvey for document-heavy work; with enterprise AI search tools such as Glean; and with general AI assistants from OpenAI, Anthropic and Microsoft. Its advantages are its focus on complex, multi-document analysis and its traction with demanding financial customers.

Hebbia at a glance

Hebbia is a New York AI company founded in 2020 by George Sivulka. Its Matrix product helps finance and legal professionals analyse thousands of documents with cited answers. It raised a US$130 million Series B at about US$700 million in July 2024, led by Andreessen Horowitz, when it reported about US$13 million in profitable ARR.

Timeline

• 2020: founded by George Sivulka.
• 2022–2023: raises seed and Series A and grows with investment firms.
• 2024: launches Matrix; raises US$130 million at about US$700 million.
• 2025: acquires FlashDocs and expands agent capabilities.

Why finance adopted AI quickly

Investment professionals read enormous amounts of material: confidential information memoranda, financial statements, legal agreements, expert calls and market research. Much of the work involves finding specific facts and comparing them across documents, which AI does well. Speed matters in competitive deal processes, and firms are willing to pay for tools that save analyst time. At the same time, accuracy is critical, which is why citations back to source text are a key feature.

Who uses Hebbia

Hebbia's customers include private equity firms, hedge funds, asset managers, investment banks, consulting firms and law firms, along with corporate development and finance teams.

Risks

• Competition: many well-funded AI tools target finance.
• Model providers: general AI assistants are improving at document analysis.
• Accuracy: mistakes in high-stakes deals could damage trust.

Questions for buyers

• How does the tool cite sources, and how easy is it to verify answers?
• How is confidential deal data secured and isolated?
• Which data rooms and systems does it integrate with?

How Matrix works in practice

A typical Matrix workflow starts with a folder of documents, such as a virtual data room for an acquisition target. The analyst adds columns that represent questions: What is the change-of-control clause? Who are the top ten customers? What were revenue and EBITDA in each of the last three years? Are there any pending lawsuits? Hebbia then reads every document and fills each cell, linking each answer to the exact passage it came from. The analyst can scan the grid, click through to check sources, add new questions and export results to Excel or slides. Work that once required a team of associates reading for several days can produce a first draft in an afternoon, leaving people to focus on judgement, negotiation and checking the most important findings.

The same pattern works for other tasks. A credit fund can compare covenants across hundreds of loan agreements. A consulting team can pull pricing and product details from dozens of competitor filings. A legal team can review leases for renewal dates and rent escalators. Because the grid format is familiar to anyone who uses spreadsheets, adoption is easier than with a chat-only interface.

Why citations and auditability matter

Financial and legal professionals are personally accountable for the numbers and conclusions they present to investment committees, clients and courts. A tool that produces plausible but unsupported answers is risky. Hebbia's design emphasises showing its work: each answer is traceable to source text, and the system breaks large questions into smaller steps that can be inspected. This approach doesn't eliminate errors, but it makes them easier to catch, which is essential for winning trust in regulated, high-stakes industries.

Valuation context

The July 2024 valuation of about US$700 million implied a high multiple of the roughly US$13 million in ARR reported at the time, reflecting investor expectations of rapid growth in AI for financial services. Since then, peers in vertical AI have raised at higher valuations, but Hebbia hasn't announced a new priced round that we can verify, so we keep the 2024 figure. Readers comparing it with newer companies should keep the date in mind. Our guide to SaaS valuation multiples explains how revenue multiples are used.

How Hebbia fits the vertical AI trend

Hebbia is part of a broader wave of vertical AI companies that focus on a specific profession rather than general productivity. Legal AI, medical scribing, insurance claims and financial research have all attracted large rounds because customers in these fields pay well for time savings and need domain-specific accuracy, security and integrations. The challenge for such companies is to build enough specialised workflow and trust that general-purpose assistants can't easily replace them.

Latest developments (October 2026)

Our October 2026 review found no confirmed new round for Hebbia after its 2024 Series B of about US$130 million led by Andreessen Horowitz at roughly US$700 million, according to Verdict. The company continues to sell AI document analysis to investment firms, banks and law firms, a market where competition from general-purpose AI assistants has increased.

What to watch

Watch for a new funding round, revenue growth, agent capabilities and competition from financial data incumbents.

Takeaways

Financial professionals can analyse large document sets faster with cited answers. Founders can see the value of focusing AI on a demanding vertical. Investors should note its last valuation is from 2024.

Sources

Every figure on this page comes from the public sources below. Where sources disagreed, we used the company's own announcement or a primary news report. Private-company data changes; check the date beside each figure.

1. TechCrunch