Hebbia

What is Hebbia?

Hebbia is an institutional AI platform built for investors, bankers, advisors, law firms, and other teams making high-stakes financial decisions. It brings private documents, public filings, financial data providers, and team work into one environment so research can become shared institutional knowledge rather than remain in disconnected files or individual workflows.

The platform is designed to reason across large document collections and present structured findings for review. Its homepage shows a Matrix-style analysis of earnings calls and deal documents, as well as workflows that can research companies, analyze expert-call transcripts, create summary slides, and send completed material. Teams can encode repeatable processes once and run them continuously.

How to Use Hebbia

1. Connect or add relevant private documents, public filings, and supported financial data sources.
2. Organize the material around a project or institutional research question.
3. Build a Matrix or workflow that defines the documents, questions, and outputs to analyze.
4. Review structured findings, red flags, opportunities, and source-level results with collaborators.
5. Automate recurring steps such as company research, transcript analysis, slide drafting, and delivery.

Core Features

• Large-scale document analysis: Reasons across extensive collections of financial and legal material.
• Shared institutional context: Keeps projects, files, analyses, and team knowledge in a collaborative workspace.
• Automated workflows: Encodes repeatable research processes and runs them continuously.
• Financial data connections: Supports private files, public filings, earnings calls, investor materials, and specialist data providers.
• Structured Matrix analysis: Organizes documents and questions into reviewable rows and columns.
• Enterprise security: Lists end-to-end encryption, no training on user data, SOC 2 Type II, ISO/IEC 42001, CCPA, and GDPR controls.

Use Cases

• Investment research: Asset managers analyze companies, earnings calls, filings, and market opportunities across many documents.
• M&A review: Banking and legal teams compare deal terms, negotiation levers, and risks across transaction documents.
• Credit analysis: Credit teams examine financial materials and identify risks or changes across issuers.
• Expert-call synthesis: Analysts structure transcripts by company, topic, and research question.
• Recurring research production: Teams automate research, analysis, slide preparation, and internal delivery.

Frequently Asked Questions

Which sources can Hebbia connect to?

The homepage lists sources such as Snowflake, Amazon S3, FactSet, Guidepoint, PitchBook, Box, Dropbox, SharePoint, S&P Capital IQ, filings, earnings calls, investor materials, podcasts, and email.

Does Hebbia train on customer data?

The security section states that user data is not used for training.

Is Hebbia only for asset managers?

No. The site also presents use cases for investment banking, legal, credit, corporate, consulting, real estate, and other institutional teams.