Hebbia
Hebbia helps knowledge workers extract insights from document libraries using AI agents.
Hebbia is an AI co-pilot platform that helps knowledge workers in finance, law, government, and pharmaceuticals extract insights from massive document libraries through natural language queries. Matrix, their flagship product, uses an 'agent swarm' architecture powered by OpenAI o1 to break down complex multi-step questions, route tasks to optimal AI models, and synthesize answers with full citations. The platform processes entire documents rather than excerpts and handles larger LLM jobs than competing tools, delivering measurable ROI: investment bankers save 30-40 hours per deal, and law firms reduce contract review time by 75%.
Problem solved
Knowledge workers spend excessive time manually searching, synthesizing, and analyzing information scattered across hundreds of documents, delaying critical business decisions and increasing operational costs.
Target customer
Investment banks, asset managers, law firms, pharmaceutical companies, and government agencies with high-volume document analysis needs. Particularly strong with top-tier financial institutions managing complex deal workflows.
Founders
G
George Sivulka
Founder & CEO
Stanford PhD candidate in machine learning and neuroscience who left at 23; previously Associate at Boston Consulting Group; Forbes 30 Under 30 (2024); Bachelor in Mathematics from Stanford.
L
Lukas Schmit
Co-Founder
Limited public information available.
T
Tim Lupo
Co-Founder
Limited public information available.
Funding history
Seed
Unknown
2020
Led by Peter Thiel, Floodgate
Series A
$30M
2022
Led by Index Ventures
· Radical Ventures, Jerry Yang, Ram Shriram, Marty Chavez
Series B
$130M
April 2024
Led by Andreessen Horowitz
· Index Ventures, Google Ventures (GV), Peter Thiel
Total raised:
$161M
Pricing
Lite: $3,000-$3,500/seat/year for output consumers and predefined agent users. Professional: $10,000/seat/year for unlimited reasoning, agent building, and advanced integrations. Enterprise pricing not publicly available.
Notable customers
Centerview Partners, American Industrial Partners, Oak Hill Advisors, Charlesbank, Fenwick & West, US Air Force; 33% of top global asset managers by AUM
Integrations
OpenAI o1, PitchBook, CapITable, broker research platforms, custom enterprise data sources
Tech stack
MobX (JavaScript libraries)
jQuery (JavaScript libraries)
core-js (JavaScript libraries)
LottieFiles
Linkedin Insight Tag (Analytics)
Google Analytics (Analytics)
Google Font API (Font scripts)
Google Workspace (Email)
Unpkg (CDN)
Google Hosted Libraries (CDN)
Amazon S3 (CDN)
Salesforce Marketing Cloud Account Engagement (Marketing automation)
Linkedin Ads (Advertising)
Webflow (Page builders)
Amazon Web Services (PaaS)
Website
Competitors
Harvey
Direct competitor offering document retrieval and automatic document drafting with industry-specific best practices, but less advanced reasoning architecture.
IBM Watson Discovery
Established enterprise search tool with broader functionality but less specialized for complex financial and legal reasoning workflows.
Microsoft Azure AI Search
Cloud-based search service with broader enterprise integration but less focused on multi-step reasoning across document libraries.
Amazon Kendra
Enterprise search service with AWS integration but lacks specialized agent orchestration for complex analytical tasks.
Why this matters: Hebbia demonstrates exceptional market traction in knowledge work with $161M raised, $700M valuation, $13M ARR, and profitability at mid-2024 Series B. The company is driving 2%+ of OpenAI's daily API volume and has achieved remarkable penetration in investment management (33% of top asset managers), signaling potential to redefine how high-value professionals analyze information.
Best for: Large financial institutions, law firms, pharmaceutical companies, and government agencies that need to analyze hundreds of complex documents and reduce decision-making cycles while maintaining audit trails.
Use cases
Investment Banking Deal Preparation
Investment bankers use Matrix to extract key terms, financial metrics, and strategic insights from hundreds of company filings, contracts, and presentations in hours rather than days. The platform automatically synthesizes information for pitch books and client meeting materials, saving 30-40 hours per transaction.
Legal Contract Review
Law firms query credit agreements, NDAs, and commercial contracts across entire portfolios to identify risks, extract obligations, and flag non-standard terms. Review time is reduced by 75% compared to manual analysis, translating to $2,000+ per hour in cost savings.
Regulatory Compliance Analysis
Government and pharmaceutical organizations use Matrix to track regulatory filings, identify compliance gaps, and synthesize requirements across thousands of documents. Full citations ensure audit-trail transparency for regulatory submissions.
Due Diligence Data Room Analysis
Private equity firms ask complex multi-step questions across entire data rooms to identify red flags, verify representations, and accelerate investment decisions with confidence and documented reasoning.
Alternatives
Harvey
Better for legal teams focused on document drafting and generation; less specialized for financial analysis workflows.
Relativity
More established in e-discovery and litigation support; less focused on real-time analytical insights.
Kroll Ontrack
Better for data recovery and forensics; not designed for continuous analytical insights across document libraries.
FAQ
What does Hebbia do? +
Hebbia is an AI co-pilot platform that helps knowledge workers ask complex, multi-step questions across document libraries in finance, law, and government. Its Matrix product uses advanced reasoning and agent orchestration to break down queries, intelligently route them to optimal AI models, and synthesize answers with full citations.
How much does Hebbia cost? +
Hebbia offers two public pricing tiers: Lite at $3,000-$3,500/seat/year for users who consume outputs and run predefined agents, and Professional at $10,000/seat/year for unlimited reasoning and custom agent building. Enterprise pricing is available upon request.
What are alternatives to Hebbia? +
Key alternatives include Harvey (legal-focused document generation), IBM Watson Discovery (enterprise search), Microsoft Azure AI Search (cloud-based semantic search), and Amazon Kendra (AWS-native search).
Who uses Hebbia? +
Hebbia serves investment banks, asset managers, law firms, pharmaceutical companies, and government agencies. Notable customers include Centerview Partners, American Industrial Partners, Oak Hill Advisors, and the US Air Force. The company claims 33% of top global asset managers by AUM as customers.
How does Hebbia compare to Harvey? +
Hebbia excels at complex analytical reasoning across large document sets with an agent swarm architecture, while Harvey focuses more on document drafting and generation with industry best practices. Hebbia is better for finance and data analysis; Harvey is better for legal document creation.
Tags
document analysis
AI agents
knowledge work
financial services
legal tech
agent orchestration
LLM reasoning
enterprise search