Hebbia offers Matrix, an AI analyst that ingests multiple files of unlimited length and responds to user inquiries in a tabular, spreadsheet-like format for large-document search and summarization. The product is used primarily by asset managers, investment banks and other financial institutions for due diligence, asset pricing and research. The company says its software is already used by 30% of asset managers and counts customers such as Centerview Partners, Charlesbank and law firm Fenwick. Hebbia was founded in 2020 by George Sivulka while he was a PhD student at Stanford. The startup reported $13 million ARR, was profitable while fundraising, and said revenue grew 15x over the prior 18 months. Hebbia plans to use the new funding to grow its team, continue selling into financial services and expand into other verticals including law firms and pharmaceutical companies. Hebbia develops AI-infused search and summarization tools and has launched a neural search engine for deep document analysis. The engine indexes and searches billions of documents — PDFs, PowerPoints, spreadsheets and transcripts — and can query both user-provided data and Hebbia's pre-indexed trusted corpora such as earnings transcripts, news, meeting minutes, SEC filings, legislation and research. Founded by a team of Stanford AI researchers and led by CEO George Sivulka, the company previously offered a Chrome plug-in called Ctrl-F before pivoting to its current product. Hebbia says the product is finding early traction in financial services for due diligence and other investment workflows. It currently counts 20 paying enterprise customers, including large private equity firms, hedge funds, consultancies and government projects. The new funding will be used to expand the engineering team, accelerate product development and scale customer acquisition in professional services industries. Hebbia is a product studio founded by George Sivulka (a Stanford PhD student on leave) and three other Stanford AI researchers and engineers, focused on applying deep learning, knowledge graphs and semantic analysis to augment knowledge work. Its first product is a Chrome plugin that upgrades Ctrl-F from simple pattern matching to contextual, question-aware search on pages; the plugin has been in private beta and is being released more broadly (currently unlisted in the Chrome Web Store). The company aims to build productivity tools for thought that help users control information inputs and outputs and make sense of their personal universe of knowledge. Hebbia positions this browser-search product as a gateway to broader applications involving knowledge graphs and summarization for professionals who process large volumes of text. The team is tuning models to improve consistency and believes better semantic search can materially boost productivity in fields like law and finance. Financially, Hebbia has completed an early external financing (see deal), and no operating metrics such as revenue or user counts are disclosed in the article.

Total raised

$161M

Funding rounds

3

Latest round

Series B

Latest activity

Jul 2024