Workflows First, Chat Last

The key point is that Raycaster is not selling a chatbot first, it is using onboarding like a services engagement to discover the repeatable workflow that later becomes software. In practice that means embedding with biotech teams, mapping how specs, methods, batch records, tech transfer packs, and approvals move between people and systems, then turning those steps into a reusable module with built in checks and validators.

• This looks Palantir like in the narrow sense that engineers start close to the customer’s actual work. They connect to systems like Veeva, IQVIA, SharePoint, and LIMS, define templates, roles, and acceptance criteria, then run pilots until the process is stable enough to package.
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• The extra twist is domain validators. Raycaster adds former CMC, QA, and regulatory writers to review outputs early, which matters in life sciences because documents drive audits, submissions, and manufacturing handoffs. That human layer helps customers get value before the software can be trusted to handle more steps alone.
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• This is becoming the standard vertical AI playbook. Hebbia uses ex bankers and lawyers to configure templates and change workflows, and Harvey uses ex lawyers in customer success to drive adoption. The pattern is that software wins only after a high touch phase teaches the product what a good workflow looks like.
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Over time, the companies that keep winning will be the ones that turn each hands on deployment into a cleaner product module, faster onboarding, and better evaluation data. In regulated markets like life sciences, that is how vertical AI moves from expensive implementation work to a durable system of record adjacent workflow layer.