Workflows First AI for Life Sciences
Levi Lian, CEO of Raycaster, on why vertical AI is workflows first & chat last
The real barrier is not model quality, it is trust in how regulated work gets created, checked, and traced. In biotech, the important material is not public web data, it is internal protocols, batch records, specs, methods, and submission documents that carry IP and regulatory risk. A general chat box is too loose for that job, because teams need page linked evidence, role based review, audit trails, and outputs that fit existing document workflows inside systems like Veeva and IQVIA.
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Life sciences teams do not just ask questions, they move a chain of dependent documents. A protocol change can force edits to consent forms, analysis plans, case report forms, and trial master file content. In CMC, specs feed methods, batch records, deviations, stability reports, and Module 3 sections, so one bad edit creates real rework and review friction.
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That is why the winning product shape looks more like workflow software than chat. Veeva became critical by owning the regulated document system of record, including eTMF, RIM, QualityDocs, and submissions. Raycaster is aiming at the layer above that, finding what is missing, drafting the next artifact, and routing review with sources rather than leaving users to prompt from a blank box.
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The comparison to Harvey and Hebbia shows the pattern across regulated or high stakes work. Harvey moved from a legal model story to preconfigured workflows with enterprise security and content integrations. Hebbia built a control surface for running work across large document sets. The durable value sits in orchestration, guardrails, and evaluation, not open ended conversation.
The next step in life sciences AI is software that quietly sits inside document production and review, not a standalone chatbot tab. As these systems learn an organization's templates, approvals, and failure modes, they become the operating layer that prepares documents faster, catches inconsistencies earlier, and makes regulated teams more willing to let AI handle progressively higher value work.