Hebbia is an artificial intelligence-based search and analysis platform designed for knowledge workers. Founded in 2020 by George Sivulka in New York, the company offers solutions capable of answering complex queries across large document collections, particularly for regulated industries such as finance and law. Hebbia’s core product, Matrix, is used by institutions such as investment banks, asset managers, and law firms to accelerate decision-making by directing AI agents in task-specific workflows.

Founding and Development

Hebbia was founded on August 14, 2020, by Stanford University graduate George Sivulka. Sivulka, who interned at NASA from an early age, studied mathematics and applied physics. Initially developed as a prototype based on natural language processing experiments with investor documents like DEFM 14-A, the tool gained popularity among investment analysts. As a result, Sivulka paused his academic career to establish Hebbia.

Product: Matrix

Matrix is Hebbia’s primary product and enables users to conduct detailed queries across uploaded PDFs, emails, presentations, images, and other documents. The system processes each document as a row in a table and presents responses to user-defined questions in columns. It clearly indicates the source document and section for each response. This setup allows users to analyze millions of documents in parallel, displaying the results in a spreadsheet-like interface compatible with existing workflows.

Use Cases and Customer Base

Initially focused on financial institutions such as investment banks and private equity firms, Hebbia had been adopted by 30% of the top 50 asset managers by 2024. Clients include Charlesbank, American Industrial Partners, Towerbrook, Crestline, and the U.S. Air Force. The company later expanded into the legal sector, adding clients such as law firm Fisher Phillips. Matrix is used for both document analysis and workflow automation.

Market and Competitive Landscape

Hebbia operates within the global enterprise search market, which was valued at $5 billion in 2022 and is projected to reach $12.2 billion by 2032. Competitors include IBM Watson Discovery, Microsoft Azure AI Search, Glean, Harvey, and Algolia. Hebbia differentiates itself by offering a highly specialized document analysis platform for end users and by its ability to answer complex, multi-step queries.

Investment and Financial Performance

Since its founding, Hebbia has raised a total of $161 million in funding. In a Series B round in 2024, the company secured $130 million from investors including Andreessen Horowitz, GV (Google Ventures), Index Ventures, and Peter Thiel, bringing its valuation to $710 million. Prior to this round, Hebbia was already profitable with $13 million in annual recurring revenue (ARR).

Expansion Strategy and Future Outlook

Hebbia aims to extend its success in the financial sector to other knowledge-intensive industries such as legal and consulting. The company plans to enhance the Matrix platform with features such as document drafting, custom knowledge bases, and multi-modal query systems. As part of its legal sector growth strategy, Hebbia appointed Ryan Samii—former lawyer at Paul Hastings and founder of legal tech startup Standard Draft—as head of its legal division.

Data Security and Compliance

Operating in heavily regulated industries, Hebbia emphasizes data security. The platform is compliant with SOC 2 Type I and II. User data is encrypted using AES-256 and TLS 1.3 protocols during both transmission and storage. Moreover, Hebbia does not train its models on user data, and data ownership remains with the user.

Vision and Long-Term Goals

Hebbia aims to deliver greater automation in AI-assisted information processing and to develop systems that fully integrate with enterprise workflows. The company’s long-term vision is to build a fully autonomous “AI knowledge worker.” According to CEO George Sivulka, future versions of Matrix will allow users to simply upload a folder of documents, which the system will classify, analyze, and generate reports for automatically.

Hebbia’s goal is to enable work environments where complex tasks such as investment committee reports, contract reviews, and loan agreement parsing can be handled entirely by AI. Its multi-agent architecture allows different tasks to be executed in parallel, with results presented alongside their justifications. Technical innovations such as prompt caching, meta-prompting, and infinite effective context windows have significantly enhanced the speed and accuracy of responses.

This transformation is expected to allow professionals in knowledge-intensive fields to focus on more strategic areas, laying the groundwork for a work model where AI and human intelligence function as complementary forces. Hebbia’s medium-term plans include expansion into new verticals such as legal, consulting, public administration, and pharmaceuticals, aiming to position itself not only as a document analysis tool but also as a high-value decision support system.