TraceDraft at a mid-cap CRO — CSR turnaround cut, source-traceable AI adopted across 14 active studies

Pain points before the engagement

• Writer capacity was the binding constraint on CSR turnaround, and hiring was slow
• Sponsors were increasingly asking about auditable AI use — with no consistent answer
• AE listings, SAS datasets and protocols lived in different systems; assembly work ate hours per section
• Safety narrative appendices took weeks — each one hand-authored from the listing
• QC caught issues late because reviewers could not easily see which claims were well-grounded and which needed more attention

Problem

The CRO ran on writer throughput. Every additional study meant either hiring writers on tight timelines or pushing back CSR delivery. Sponsors were opening the AI conversation and expected a coherent answer. The internal team had experimented with AI drafting, but nothing survived a sponsor audit because there was no way to trace claims back to source. TraceDraft was evaluated against three alternatives; the source-traceability model was the deciding factor.

Approach

We rolled TraceDraft out across two therapeutic areas first — oncology and metabolic — before scaling to 14 active studies. Source ingestion was standardised at the study-start milestone so writers saw coverage before they drafted. The review queue routed sections with the source-link count visible up front, so reviewers focused their attention where AI grounding was thinnest. Sponsor documentation was drafted alongside the rollout: how the AI was governed, how outputs were reviewed, and how the evidence pack travelled with every export.