Every vendor has an AI now. But an AI is only as good as the data underneath it — and most site-selection models are trained on registries, public data, and past trial counts. They automate the guess. Ours runs on real, patient-level evidence, so it ranks the sites that will actually enroll — and shows you why.
The model isn’t the moat; the data is. Point an AI at inferred data and you get a faster, more confident guess. Point it at real, patient-level evidence and you get a ranked list that holds up when a sponsor pushes on it. Same algorithm, completely different answer — because of what it learned from.
Not a long list to sift. A scored, ranked shortlist — where the biggest names get cut if the patients aren’t there.
Illustrative. Every site scored on real qualifying-patient access and a measured investigator track record — with the reasoning attached to each rank.
The model reads two things a sponsor actually cares about, and combines them into a rank you can defend.
The counted number of patients who meet the protocol at each site — under the investigator’s care and across their institution. Real, not inferred.
Principal investigators scored on how they’ve actually enrolled before — a measured record, not a reputation list of the usual academic names.
The two signals combine into one composite score that orders the shortlist — and every rank carries the reasoning behind it.
An AI answer you can’t explain is worthless in a bid defense. Every rank we hand you comes with its reasoning — the qualifying-patient counts, the investigator’s record, and why each site made the shortlist or got cut. You can defend the list line by line, because you can see inside it.
In a recent rare-disease program, modeling the protocol on real, patient-level data nearly tripled the eligible pool — then ranked the sites that could actually reach those patients, on one evidence base.
A ranked site list you can act on and defend — in days, not weeks:
What is AI clinical trial site selection?
It uses a model to rank candidate sites and investigators for a specific protocol. Done well, the model is trained on real, patient-level data and produces a ranked, explainable shortlist of the sites most likely to actually enroll — not a list based on reputation or historical trial counts.
How is your AI different from other site-selection tools?
An AI is only as good as the data underneath it. Most site-selection AI is trained on registries, public data, and past trial counts, so it automates inference. Ours runs on real, patient-level evidence, ranks each site on qualifying-patient access and a measured investigator track record, and explains every rank.
Is your AI a black box?
No. Every rank comes with its reasoning — the counts, the track record, and why a site made the shortlist or was cut. It’s built to be defended to a sponsor line by line, not taken on faith.
How fast is it?
Because the evidence base and the model are already built, a ranked, defensible site list for your protocol is delivered in days, not the weeks a survey-and-outreach feasibility cycle takes.
Bring us your protocol. We’ll come back with a ranked, explainable shortlist of the sites and investigators that will actually enroll it — each one backed by real patient evidence.