Workflows Not Chat in Vertical AI

Hebbia found fit by selling analysis that can survive partner and committee scrutiny, not by selling a better search box. In finance and law, the job is to read whole deal rooms, contracts, filings, and memos, connect facts across them, and produce work product like diligence matrices, investment memos, and pitch decks. That pushed Hebbia toward full document reasoning, spreadsheet style agent workflows, and hands on onboarding with ex bankers and lawyers.

• The core user is not just searching for a fact. Teams are screening virtual data rooms, comparing agreements, pulling numbers from financial statements, and checking how one clause or executive change affects an investment or legal judgment. Hebbia built Matrix as a tabular interface because many of these workflows already live in spreadsheets.
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• This is the same pattern showing up across vertical AI. Harvey in law also sells expensive seats plus high touch deployment, with ex lawyers helping firms configure workflows and drive adoption. In both markets, product market fit comes from fitting into real professional work, not from generic chat.
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• The broader category is moving from chat to packaged workflows with guardrails. In regulated markets, buyers care about audit logs, permissions, citations, and human review because the document itself is often the asset. That makes orchestration and context design more defensible than any single underlying model.
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The next step is expansion from finance and law into other document heavy functions that look the same underneath, corporate finance, in house legal, and eventually R&D and regulated operations. The winners will be the companies that turn AI into a repeatable work surface for experts, with traceable outputs and workflows that get better every time a team uses them.