Vertical AI Owns Workflow Layer

The core bet is that vertical AI will not beat frontier labs at raw reasoning, it will own the missing layer between a generic model and a regulated workflow. In practice that layer is the company specific setup, which documents matter, which systems the agent can touch, what checks must pass, who signs off, and what happened after each draft. That is what lets a vertical product plug better models in over time without losing the workflow itself.

• Raycaster is building around internal document work, not public data search. The product sits on top of systems of record like Veeva and IQVIA, reads specs, methods, batch records, and Module 3 files, flags inconsistencies, proposes edits, and routes them to the right reviewer. That makes the moat operational context, not model weights.
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• The same pattern is visible in other vertical AI winners. Harvey moved from legal reasoning as the product toward packaged workflows and high touch deployment. Hebbia argues its edge is orchestration over real work, not generic enterprise search. The category is converging on workflow software powered by interchangeable models.
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• Meeting in the middle means the labs and the verticals are supplying different ingredients. MCP standardizes how models connect to tools and data, and trace grading turns tool calls and outcomes into structured evals. A company like Raycaster can feed domain specific tests, traces, and pass fail outcomes into that stack while benefiting each time the base models improve.
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This points toward a split market structure. Horizontals will keep absorbing generic research and drafting, while verticals that control enterprise context, approval logic, and outcome data will become the execution layer inside regulated industries. The winners will look less like chat apps and more like deeply embedded workflow systems that happen to run on the best available models.