AI-native oncology trial intelligence for faster FIH execution.

The answer should not come only from hospital reputation or generic feasibility forms. We compare candidate sites against matched mechanism, indication, and design contexts, then review activation history, early oncology exposure, patient access, peer-trial density, and regional execution risk.

Client output: a diligence priority view that separates sites into first-wave diligence, review as backup, and pause for the initial plan, with evidence and next questions attached.

Academic reputation matters, but it is not the same as current execution capacity. We review recent trial load, adjacent mechanism overlap, team capacity signals, activation behavior, and public recruitment activity to identify overload risk and emerging operational performers.

Client output: PI load flags, alternative investigator candidates, and diligence questions to validate with sites, CROs, or local experts.

When peer trials cluster around the same countries, cities, or centers, patient competition can slow enrollment before the trial starts. We map peer footprints by mechanism, indication, sponsor, country, city, and site to identify crowded regions and underused execution paths.

Client output: competitor footprint map, patient-competition risk notes, and region/site recommendations for first-wave planning.

The first site plan is also a future evidence story. We test whether the country mix is too concentrated, whether the patient population can be explained, whether MRCT or bridging logic may be needed, and which FDA-facing questions should be prepared early.

Client output: US/global development pathway memo, country-mix risk flags, and the clinical or regulatory questions that should be prepared before commitments harden.