Hebbia is a New York-based enterprise AI company founded in August 2020 by George Sivulka, Lukas Schmit and Tim Lupo. It raised a $130M Series B in July 2024 led by Andreessen Horowitz, with GV, Index Ventures and Peter Thiel participating, at a valuation of roughly $700M — reported at the time on $13M of ARR and described as profitable. Total disclosed funding is approximately $161M.
The flagship product is Matrix, which abandons the chat interface for a grid: documents are rows, questions are columns, and every cell is an answer linked back to the exact passage that produced it. That structure suits the work Hebbia's customers actually do — running one question across thousands of SEC filings, credit agreements, CIMs or data-room documents and comparing results side by side. Underneath sits Iterative Source Decomposition, which Hebbia positions as a replacement for retrieval-augmented generation, decomposing a request into sub-tasks and iteratively retrieving across sources rather than embedding-matching a single query.
Hebbia sells exclusively to enterprises: no free tier, no trial, no published pricing. Named customers include Morgan Stanley, MetLife, Centerview Partners, Oak Hill Advisors, New Mountain Capital and Latham & Watkins, and the company reported serving roughly 30% of the top 50 asset managers by AUM as of mid-2024. Third-party reporting puts seats somewhere between $3,000 and $15,000 per year, with large deployments reaching the mid-to-high six figures — figures Hebbia has never confirmed.
Key Benefits
- Scale that chat interfaces cannot match: One prompt fans out across an entire document corpus, with results laid out for direct comparison rather than buried in conversation.
- Auditability by construction: Cell-level citations mean an analyst can verify any answer against its source in one click — essential where output feeds an investment memo or legal opinion.
- Finance-native data: Integrations with FactSet, Capital IQ, PitchBook, Preqin and ICE bring market data alongside the documents, not in a separate tool.
- Deliverables, not just answers: Recent releases generate editable Excel models and branded slide decks directly from a prompt, and an Excel plug-in carries citations into the spreadsheet.
Use Cases
- Private equity and M&A diligence — Run standardized question sets across an entire data room, surfacing change-of-control clauses, customer concentration and covenant terms across hundreds of documents at once.
- Public markets research — Compare disclosures, risk factors and guidance across years of filings and earnings calls for a whole coverage universe in a single Matrix.
- Credit and fixed-income analysis — Extract covenant terms from credit agreements and pair them with ICE bond pricing, spreads and yield history inside the same workflow.
- Legal document review — Law firms use Matrix to analyze contract sets during diligence, though drafting, redlining and negotiation remain outside the product's scope.
