Every vendor has an AI now. Most are trained on registries, public data, and past trial counts, so they automate the guess. Ours runs on actual patient records, not estimates. 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.
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 measured on how they’ve actually enrolled before — a measured record, not a reputation list of the usual academic names.
A site has to hold up on both signals to make the list, and every rank carries the reasoning behind it.
Not a long list to sift. A ranked shortlist, where the biggest names get cut if the patients aren’t there.
Illustrative. Every site ranked on real qualifying-patient access and a measured investigator track record, with the reasoning attached to each rank.
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.
Regional Medical Center
Chosen · high qualifying-patient access
The patients are here, and the investigator has enrolled this population before.
Illustrative example — no real site or investigator data.
In a recent rare-disease program, modeling the protocol on actual patient records 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. Delivered by a forward-deployed team, not a tool you have to run:
What is AI clinical trial site selection?
AI clinical trial site selection 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.
Is this a tool our team has to run?
No. It is a forward-deployed solution: you send the protocol, our team runs it and hands back the ranked list with the reasoning behind every rank. There is no software to install and nothing to learn. A self-serve option is available as you scale.
Does your AI site selection use real-world data?
Yes. It runs on real, de-identified, patient-level real-world data, so each rank rests on counted qualifying patients and the record of each investigator on similar trials, not on reputation or public trial counts. You receive the ranked shortlist and the reasoning, not the raw data.
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.