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Hebbia

Hebbia is the leading AI platform for finance. Founded in 2020 by George Sivulka, Hebbia is a generative AI company backed by Andreessen Horowitz, Peter Thiel, and Index Ventures. Investment banks and over 40% of the largest asset managers by AUM use Hebbia’s AI agents to drive investment decisions and automate financial analyst workflows. Users can instantly surface insights over filings, research, and millions of internal documents, enabling citation-backed research, AI-driven document, powerpoint, and spreadsheet generation, and AI driven origination, screening, and diligence. Learn more at hebbia.com.

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Founded

2020

Location

New York, NY

Employees

139

Funding

$160M+

Hebbia: AI Research Agents for Finance and Legal Teams

Hebbia builds AI research agents for high‑stakes workflows across finance and law. Its flagship platform, Matrix, orchestrates multiple agents to read, reason over, and produce work from large document sets—unlocking faster diligence, filings analysis, contract review, and citation‑backed research. Hebbia positions itself as an AI platform that replaces hours of junior analyst and associate work with reliable, auditable outputs used by leading asset managers, banks, law firms, and Fortune 500s. Sources: [Homepage](https://www.hebbia.com/), [Product](https://www.hebbia.com/product), [About](https://www.hebbia.com/about)

  • Founded: 2020
  • Founder and CEO: George Sivulka
  • HQ: New York, US
  • Team size: 51–200 (LinkedIn shows ~139 listed employees; 44k+ followers). Source: [LinkedIn](https://www.linkedin.com/company/hebbia/)
  • Focus: AI agents for finance and legal research; deep document reasoning. Sources: [Hebbia](https://www.hebbia.com/), [Law industry page](https://www.hebbia.com/industry/law)
  • Flagship product: Matrix multi‑agent platform. Sources: [Product](https://www.hebbia.com/product), [Matrix + OpenAI o1](https://www.hebbia.com/blog/matrix-and-openai-o1-smarter-ai-agents)
  • Markets served: Asset managers, investment banks, law firms, Fortune 500s. Source: [Hebbia](https://www.hebbia.com/)
  • Security: Dedicated Trust Center. Source: [Trust Center](https://trust.hebbia.ai/)
  • Notable claim: Firms using Hebbia represent $14T AUM. Source: [Product](https://www.hebbia.com/product)
  • Positioning: “The leading AI platform for finance.” Sources: [LinkedIn](https://www.linkedin.com/company/hebbia/), [Blog](https://www.hebbia.com/blog)
  • ---

    What Hebbia Does

  • Matrix orchestrates multiple agents for:
  • Reading and comprehension across large document sets
  • Reasoning and cross‑document synthesis
  • Structured extraction (tables, entities, metrics)
  • Writing and packaging outputs (cited answers, spreadsheets, and slides)
  • Emphasis on:
  • Tight grounding to sources with citations
  • Auditable, reproducible workflows tailored to finance and legal tasks
  • Architecture that Hebbia says outperforms standard RAG on core tasks
  • Sources: [Product](https://www.hebbia.com/product), [Matrix + OpenAI o1](https://www.hebbia.com/blog/matrix-and-openai-o1-smarter-ai-agents), [Deeper Research agent](https://www.hebbia.com/blog/inside-hebbias-deeper-research-agent)

    ---

    Who It’s For

  • Asset managers, investment banks, and PE/VC teams doing deep diligence and research
  • Corporate strategy and competitive intelligence teams
  • Law firms and in‑house legal teams handling contracts, depositions, patents, discovery
  • Sources: [Hebbia](https://www.hebbia.com/), [Law industry page](https://www.hebbia.com/industry/law)

    ---

    High‑Impact Use Cases

  • Finance
  • Origination and screening
  • Sector research and comps
  • Diligence memos
  • Model‑ready outputs: spreadsheets and slides
  • Sources: [Hebbia](https://www.hebbia.com/), [Product](https://www.hebbia.com/product)

  • Legal
  • Contract review and redlining support
  • Deposition analysis and patent landscapes
  • Case law and filings research with citations
  • Sources: [Law industry page](https://www.hebbia.com/industry/law), [Deeper Research agent](https://www.hebbia.com/blog/inside-hebbias-deeper-research-agent)

  • Enterprise Research
  • Synthesize large internal and external corpora with grounded answers and exportable outputs
  • Sources: [Hebbia](https://www.hebbia.com/), [OpenAI partnership](https://openai.com/index/hebbia/)

    ---

    Integrations and Data Sources

  • Works across public and internal documents, filings, and research repositories
  • Typical enterprise sources discussed: Google Drive, SharePoint, VDRs, and public filings (e.g., EDGAR)
  • Outputs are citation‑backed with multi‑source grounding
  • Sources: [Hebbia](https://www.hebbia.com/), [Law industry page](https://www.hebbia.com/industry/law), [OpenAI partnership](https://openai.com/index/hebbia/)

    Note: Hebbia does not publish a full public connector catalog; treat specific vendor integrations as sales‑confirmed.

    ---

    Security, Compliance, and Deployment

  • Enterprise‑grade controls and reliability outlined in a dedicated Trust Center
  • Source: [Trust Center](https://trust.hebbia.ai/)

  • Enterprise deployment focus, with governance for high‑stakes finance and legal workflows
  • ---

    Partnerships and Third‑Party Coverage

  • Highlighted by OpenAI as a multi‑agent platform that automates most finance and legal workflows end‑to‑end
  • Source: [OpenAI partnership](https://openai.com/index/hebbia/)

  • External overview aligns with finance/legal research positioning
  • Source: [Wikipedia](https://en.wikipedia.org/wiki/Hebbia)

    ---

    Buyer Notes: Strengths and Trade‑offs

    Pros (user sentiment)

  • Recognized in finance circles as a strong current use case for diligence and research speed
  • Sources: [Reddit thread 1](https://www.reddit.com/r/private_equity/comments/1n5yusc/what_is_your_experience_with_ai_tools_in_the/), [Reddit thread 2](https://www.reddit.com/r/private_equity/comments/1ek1y40/ai_tools_hebbia_dili/)

  • Purpose‑built for complex document review and filings search (PE diligence context)
  • Cons (user sentiment)

  • Mixed value relative to baseline GPT; one PE user felt drive‑style search underdelivered in practice
  • Source: [Reddit thread 3](https://www.reddit.com/r/private_equity/comments/1e16mn4/is_hebbia_any_good/)

  • Price sensitivity; at least one user was underwhelmed by demo value vs. cost
  • Source: [Reddit thread 1](https://www.reddit.com/r/private_equity/comments/1n5yusc/what_is_your_experience_with_ai_tools_in_the/)

    Note: Public review coverage on G2/Capterra was not surfaced in this research window. Consider requesting references and case studies from sales.

    ---

    Pricing and Trial

  • No public pricing
  • No public free trial
  • Enterprise sales motion implied
  • Sources: [Hebbia](https://www.hebbia.com/), [Careers](https://careers.hebbia.ai/)

    ---

    Why It Matters

  • For finance teams: Accelerates origination, diligence, and model‑ready deliverables with citation‑backed outputs.
  • For legal teams: Expands the scope and depth of contract, deposition, and filings review with auditable reasoning.
  • For enterprises: A multi‑agent alternative to standard RAG designed for complex, high‑stakes document intelligence.
  • For more, explore Hebbia’s [Product](https://www.hebbia.com/product), [Blog](https://www.hebbia.com/blog), and [Trust Center](https://trust.hebbia.ai/).

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