Vertical AI for Last-Mile Knowledge Work
Danny Wheller, VP of Business & Strategy at Hebbia, on vertical vs horizontal enterprise AI
Hebbia is trying to own the last mile of knowledge work, not the first click of search. Horizontal tools help employees find files, answers, and snippets across the company, but Hebbia is built for the next step, where an analyst drops a data room into a workflow, compares contracts and models, runs calculations, and turns the output into a memo, pitchbook, or diligence package. That makes it easier to coexist with tools like Glean while charging for higher value, lower frequency work.
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The product surface reflects that difference. Hebbia uses a spreadsheet like Matrix interface where rows, columns, and cells can trigger document review, extraction, synthesis, and content generation, so the system is not just answering a question, it is producing work products inside a repeatable process.
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The pricing and deployment model also differ. Glean has pursued broad seat adoption as a general enterprise layer, while Hebbia sells a smaller number of expensive seats to teams in finance and legal where one correct diligence memo or contract review can justify much higher spend.
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This is the same pattern showing up across vertical AI. In legal, finance, and biotech, the winners are moving away from chat and toward packaged workflows with guardrails, source traceability, and domain specific outputs. That is where switching costs start to come from, because the software gets embedded in how teams actually finish work.
The market is heading toward a split architecture. Horizontal copilots will remain the default layer for enterprise search and lightweight assistance, while companies like Hebbia push upward into high consequence workflows where retrieval, reasoning, and document generation are bundled into one controlled system. As those workflows become standard, the durable moat shifts from model quality alone to workflow depth, orchestration, and trust.