Palantir-style embedded engineers for workflows

Raycaster is building around services first because regulated AI adoption breaks at the workflow level, not the model level. In biotech, the hard part is not generating text, it is mapping who approves what, which document feeds the next one, and how edits stay tied to source pages and SOPs. That is why Raycaster starts by embedding engineers to codify a customer’s actual document process before turning it into repeatable software.

• Raycaster in short is AI for life sciences document work across development. It drafts, processes, edits, and reviews artifacts like tech transfer packages, specs, methods, batch records, and Module 3 sections, where a missing field or inconsistent version can trigger rework and regulatory delay.
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• The Palantir comparison is concrete. Early deployments involve mapping inputs and outputs, wiring repositories, templates, permissions, and review roles, then piloting against a live workflow. Airtable used a similar model upmarket, with implementation specialists shaping each customer setup to increase retention and expansion.
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• The founder motivation came from seeing highly trained scientists spend their time reconciling Word files and spreadsheets instead of doing science. Levi Lian traces the idea to family exposure to pharma, Stanford, and repeated conversations with teams that showed drug development was slowed by document coordination more than by missing software systems of record.
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This model points toward a familiar arc for vertical AI. The first sale is labor heavy, then the repeated pieces become packaged modules for use cases like tech transfer authoring, QA review, and sponsor packet ingestion. The companies that win will turn custom onboarding work into standard workflow products without losing the auditability and trust that got them in the door.