Top-10 Pharma Cuts Clinical Trial Prep 50% with LLM-Powered Research Assistant
Domain-tuned LLM on Databricks Mosaic AI accelerates literature review, protocol drafting, and inclusion-criteria modeling.
A top-10 pharma's clinical operations team was bottlenecked by manual literature review, protocol authoring, and inclusion-criteria modeling for new trials. Each Phase II study required 8–12 weeks of medical writers and biostatisticians before a single protocol amendment cleared committee. Generative AI was on the CTO's roadmap but a previous pilot had failed safety review.
Apptad built a clinical research assistant on Databricks Mosaic AI, grounded on a Unity-Catalog-governed corpus of 1.5M peer-reviewed papers, internal study reports, and regulatory submissions. We co-designed the safety harness with the pharma's GxP and quality teams — every output is fully sourced, validated by domain experts, and traced through MLflow for audit.
Trial preparation time cut 50%. Medical writers report a 3× lift in productive output, and the platform passed both internal validation and FDA-acceptable digital systems review. Three Phase II studies have launched on the new flow with zero protocol-amendment rework attributable to AI-generated content.
The Numbers Behind the Story
Built On
The platforms and partners that powered this engagement. Where Apptad has a dedicated alliance, the chip links to our partnership page.
“We needed AI that our regulators would accept and our scientists would actually use. Apptad delivered both — and the platform is becoming the way we work.”
