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Hebbia

AI analyst agent for financial services that reads, analyzes, and extracts insights from complex documents like SEC filings and contracts.

Starting atContact Sales (six-figure annual minimum reported)
Visit Hebbia →
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In Plain English

An AI analyst that reads and understands complex documents — processes contracts, filings, and reports that would take humans hours.

OverviewFeaturesPricingGetting StartedUse CasesIntegrationsLimitationsFAQAlternatives

Overview

Hebbia is an Enterprise Agents AI analyst platform that reads, analyzes, and extracts insights from complex financial documents at scale, with enterprise pricing available on request. It is built for hedge funds, private equity firms, investment banks, and corporate finance teams handling SEC filings, credit agreements, and M&A documents.

Founded in 2020 by Stanford and Harvard alumnus George Sivulka, Hebbia has raised over $161 million in total funding, including a $130 million Series B led by Andreessen Horowitz in 2024 at a reported $700 million valuation. The platform's flagship Matrix interface allows analysts to ask structured questions across thousands of documents simultaneously, returning answers in spreadsheet-like comparison tables. Each cell links back to the exact page and paragraph in the source document, providing the auditability that regulated financial institutions require. Hebbia processes documents in formats ranging from 10-Ks and credit agreements to earnings transcripts, indentures, and proprietary investment memos, and can ingest entire deal rooms containing hundreds of contracts in a single workflow.

Hebbia is used by major financial institutions including Centerview Partners, the U.S. Air Force, and reportedly nearly a third of the largest 50 U.S. asset managers, with customers including hedge funds and Tier 1 investment banks. Based on our analysis of 870+ AI tools, Hebbia is the most specialized financial document analysis agent in the Enterprise Agents category, distinguished by its Matrix multi-document workflow and citation-first architecture. Compared to Harvey AI (legal-focused) and Elicit (academic research), Hebbia targets investment professionals who need to synthesize quantitative and qualitative information from large document sets under deal-cycle deadlines. The platform integrates with internal data rooms and supports SOC 2 Type II compliance, making it suitable for firms with strict data governance requirements. While the enterprise-only pricing puts it out of reach for individual analysts and small firms, customers report order-of-magnitude productivity gains on due diligence and research workflows that would otherwise require teams of junior analysts working over multiple days.

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Editorial Review

AI analyst agent for financial services that reads, analyzes, and extracts insights from complex documents like SEC filings and contracts.

Key Features

Matrix Comparative Analysis+

Analyze hundreds of documents in parallel with structured query rows and document columns, producing spreadsheet-like comparison tables. Each Matrix cell is independently sourced and citable, allowing analysts to drill from a comparison view directly into the underlying document evidence.

Use Case:

Extract specific covenant terms from 50 credit agreements and receive a structured comparison table in minutes rather than hours of manual review

Financial Document Intelligence+

Native parsing of finance-specific document types including SEC filings, credit agreements, indentures, M&A purchase agreements, and earnings transcripts. The model understands financial structure conventions like footnote references, schedule attachments, and defined-term cross-references that trip up general LLMs.

Use Case:

Parse 10-K filings to identify risk factors, revenue trends, and regulatory concerns across multiple companies for investment research

Source Citation System+

Every answer includes citations linking to the exact page and paragraph in the original document, with one-click navigation to the cited passage. This citation-first architecture is a core compliance feature that enables deployment in regulated financial institutions.

Use Case:

Present investment committee recommendations with precise source references that stakeholders can independently verify

Bulk Document Processing+

Process thousands of documents simultaneously through enterprise-grade infrastructure that scales horizontally with document volume. The platform handles full deal rooms and multi-year filing histories in a single workflow rather than requiring document-by-document interaction.

Use Case:

Analyze entire deal room of documents for due diligence, extracting key terms and risks from hundreds of contracts and agreements

Custom Knowledge Collections+

Build firm-specific knowledge bases from proprietary investment memos, deal history, and internal research that remain isolated from other tenants. Collections support fine-grained access controls so different teams can maintain segregated document spaces within the same Hebbia deployment.

Use Case:

Create a searchable database of past investment memos and due diligence reports to inform future deal analysis

Pricing Plans

Enterprise

Contact Sales

  • ✓Matrix multi-document comparative analysis
  • ✓Unlimited document ingestion and processing
  • ✓Financial document parsing (10-K, 10-Q, credit agreements, indentures, contracts)
  • ✓Source citations with exact page and paragraph references
  • ✓Custom knowledge base creation from proprietary documents
  • ✓SOC 2 Type II compliance and enterprise security
  • ✓SSO via Okta and enterprise authentication
  • ✓Dedicated onboarding and customer success support
  • ✓API access for workflow integration
  • ✓Custom seat counts and deployment configurations
See Full Pricing →Free vs Paid →Is it worth it? →

Ready to get started with Hebbia?

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Getting Started with Hebbia

  1. 1Contact sales team to discuss enterprise pricing and requirements
  2. 2Upload sample documents to test Hebbia's analysis on your document types
  3. 3Define key research questions and comparison metrics for your use cases
  4. 4Train team on Matrix feature for multi-document comparative analysis
  5. 5Integrate with existing document management and research workflows
  6. 6Establish verification processes for AI-generated insights and citations
Ready to start? Try Hebbia →

Best Use Cases

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M&A due diligence: ingest an entire deal data room of 500+ contracts and extract change-of-control clauses, MAC definitions, and indemnity caps into a single comparison matrix in hours rather than days

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Credit research: parse 50+ credit agreements and indentures to compare covenant packages, restricted payment baskets, and EBITDA add-back definitions across a portfolio of borrowers

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Investment research: analyze 10-K filings across an entire sector to identify common risk factors, revenue concentration disclosures, and management commentary trends for thematic investing

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Earnings analysis: process transcripts from a quarter's earnings calls across a coverage universe to extract guidance changes, capex commentary, and forward statements into structured tables

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Internal knowledge management: build a searchable proprietary knowledge base from years of investment memos, deal post-mortems, and committee materials to inform new deal evaluation

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Regulatory and compliance review: scan large filing sets for specific disclosure language, related-party transactions, or compliance risks with full citation trails for audit defense

Integration Ecosystem

15 integrations

Hebbia works with these platforms and services:

🧠 LLM Providers
OpenAIAnthropic
📊 Vector Databases
Pinecone
☁️ Cloud Platforms
AWS
💬 Communication
Slack
📇 CRM
Salesforce
🗄️ Databases
snowflake
🔐 Auth & Identity
Okta
📈 Monitoring
Datadog
🌐 Browsers
chrome-extension
💾 Storage
S3
⚡ Code Execution
python
🔗 Other
bloombergfactsetapi
View full Integration Matrix →

Limitations & What It Can't Do

We believe in transparent reviews. Here's what Hebbia doesn't handle well:

  • ⚠Enterprise-only commercial model excludes individual analysts, students, and small boutique firms
  • ⚠Strong specialization in finance means it underperforms general document tools for legal contracts outside finance, academic research, or technical documentation
  • ⚠Initial document ingestion and collection structuring requires meaningful setup time and IT involvement
  • ⚠Complex document parsing can still produce errors on heavily footnoted or non-standard financial structures, requiring analyst review
  • ⚠No public pricing or self-serve trial makes evaluation a sales-led process that small teams may find slow

Pros & Cons

✓ Pros

  • ✓Matrix interface uniquely suited for cross-document comparison at scale (dozens to hundreds of documents simultaneously)
  • ✓Every output includes verifiable source citations down to page and paragraph level — critical for regulated finance workflows
  • ✓Backed by $161M+ in funding from Andreessen Horowitz and Index Ventures, signaling enterprise-grade stability
  • ✓Native handling of complex finance-specific document types (indentures, credit agreements, S-1s) that general-purpose LLMs struggle with
  • ✓SOC 2 Type II compliant with deployment options that meet institutional data governance requirements
  • ✓Used by roughly one-third of the top 50 U.S. asset managers, validating production-grade reliability

✗ Cons

  • ✗Enterprise-only pricing with no public tier or self-serve option — inaccessible to individual analysts or small firms
  • ✗Narrowly focused on financial services use cases — limited utility for legal, academic, or general business research
  • ✗Requires upfront document ingestion and collection setup before generating value
  • ✗Outputs still require human verification for high-stakes investment decisions, especially on edge-case extractions
  • ✗No transparent pricing on the website makes ROI evaluation difficult before sales engagement

Frequently Asked Questions

What types of documents can Hebbia analyze?+

Hebbia handles the full range of financial documents including 10-K and 10-Q SEC filings, credit agreements, indentures, earnings call transcripts, investment memos, M&A documents, S-1 prospectuses, and arbitrary proprietary contracts. The platform was specifically engineered to parse the dense, footnote-heavy structure of financial documents that general-purpose LLMs often misinterpret. It can ingest both structured tables and unstructured prose from PDFs, Word documents, and scanned files. Documents can be uploaded directly or pulled from connected data rooms and internal repositories.

How does Hebbia ensure accuracy and prevent hallucinations?+

Every answer Hebbia generates includes a citation linking to the exact page and paragraph in the source document, allowing analysts to verify outputs directly against the original text. The Matrix workflow is built around a retrieval-grounded architecture that constrains responses to information actually present in the user's document set, reducing the risk of fabricated content. This citation-first design is one of the primary reasons regulated institutions like investment banks and asset managers can deploy the tool in production. That said, Hebbia recommends human review for any output used in critical investment or transaction decisions.

How much does Hebbia cost?+

Hebbia uses enterprise-only pricing with no public tier and no self-serve signup, meaning all pricing is custom and negotiated through the sales team based on seat counts, document volume, and deployment requirements. Industry reporting suggests deals typically range from six to seven figures annually for institutional customers, putting it firmly in the enterprise software bracket. There is no free trial advertised on the website, though the company does offer guided demos and proof-of-concept engagements. Compared to general-purpose AI document tools, Hebbia commands a significant premium justified by finance-specific accuracy and security.

Who are Hebbia's main customers?+

Hebbia is used by hedge funds, private equity firms, investment banks, asset managers, and corporate finance teams, with reporting indicating roughly one-third of the top 50 U.S. asset managers as customers. Notable disclosed users include Centerview Partners, and the company has also expanded into government work with the U.S. Air Force. The product is designed for analyst, associate, and VP-level workflows where reading and synthesizing large document sets is the primary bottleneck. It is not currently positioned for retail investors or non-finance enterprises.

How does Hebbia compare to ChatGPT or Claude for document analysis?+

While general-purpose models like ChatGPT and Claude can analyze individual documents, Hebbia is purpose-built for the multi-document, citation-required workflows that finance professionals run daily. Its Matrix interface allows structured queries across hundreds of documents simultaneously, returning spreadsheet-like comparison outputs that consumer LLMs cannot natively produce. Hebbia also enforces strict source-grounding to prevent hallucinations on numerical data — a known weakness of general LLMs. For one-off summarization tasks, ChatGPT may suffice; for production due diligence and research at institutional scale, Hebbia is the specialist choice.
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What's New in 2026

Hebbia continues to expand its Matrix workflow capabilities and has broadened from pure financial services into adjacent enterprise verticals including government (notably a U.S. Air Force engagement) and large-cap asset management. Following its 2024 Series B led by Andreessen Horowitz at a reported $700M valuation, the company has been investing in larger-scale agentic workflows that chain together multi-step analytical tasks across document sets.

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Quick Info

Category

Enterprise Agents

Website

www.hebbia.ai
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