Workflows First in Vertical AI

This pattern becomes a feature when the hard part is only reasoning over public information, because foundation model vendors can copy that capability faster than a startup can build a moat. The durable layer sits inside the customer’s workflow, where the system knows which document changed, what field is now inconsistent, who must review it, and how to route the next draft back into the regulated process.

• Raycaster reached this conclusion by trying public data research in life sciences legal and R&D, then shifting to internal document workflows like tech transfer packs, specs, methods, batch records, change control, and Module 3. That is harder for a general model product to replicate because the value comes from company specific context, not just better answers.
  1 sacra
• Harvey shows what moving beyond chat looks like in practice. Adoption is strongest where the product plugs into daily legal work, like contract drafting and review inside Word and document management systems, not where it simply acts as a smarter research box. The value lands when it saves real redlining time in the tool lawyers already use.
  2 sacra
• OpenEvidence shows the opposite side of the market. A public knowledge chatbot can scale fast when the workflow is low risk and avoids private records, but even there the long term path is to move from search into embedded clinical workflow. Across law, medicine, and biotech, the winning products start owning the work surface, not just the answer surface.
  3 sacra 2 sacra 1 sacra

The next wave of vertical AI will look less like asking a smart bot to research broadly, and more like clicking through a narrow, repeatable job that finishes with evidence, approvals, and audit history attached. As general models get better, the scarce asset shifts further toward workflow context, evaluation data, and trusted integration into systems of record.