We use the most complete real-world patient data in the U.S. — national, longitudinal, every institution, never a snapshot — to turn your study into a ranked, defensible shortlist of the sites that will actually enroll it. Not just where the patients are or who performs, but both, customized to your study. In days.
We have the real-world patient data — the actual, patient-level record, not a snapshot. Where most tools stitch together EHR feeds from a handful of institutions, we track every patient across every institution and provider, nationally and over time. That completeness makes us therapeutic-area agnostic — nearly every U.S. patient, every condition.
Every competitor picks sites one of two ways — where the patients are, or which investigators perform. Each misses the other, so the "best" sites lose and patient-rich sites can't enroll. We score both, and only the sites strong on both make the cut.
Because we know exactly where the patients are — and we can predict how sites will perform. That de-risks the one decision the whole trial hinges on, and everyone downstream wins.
Our proprietary engine that compounds with every engagement — the intelligence gets sharper the more we run.
Reads the signals that define an ideal site — infrastructure, trial experience, population access, depth of research — and scores it.
The complete patient picture — tracked across every institution and provider, nationally, over time.
Our data doesn't stop at site selection. It carries through the life of the study — one source of truth, end to end.
Is it viable, and where can it enroll?
The sites that will actually perform.
De-risked activation, outreach-ready.
Fewer snags, cleaner enrollment, better management.
Our edge is how we use it — agentically, co-piloted by clinical experts — on top of our data and codified expertise. And it compounds: every engagement feeds the Knowledge Engine, which sharpens the Intelligence Engine, so the outputs get more accurate over time.
We work with real patient data, so how we handle it matters as much as what we find. Privacy, expert oversight, and confidentiality are built into the way we work — not bolted on.
We work exclusively with de-identified, aggregated real-world data. No patient-identifying information is exposed in our analysis or our deliverables — figures are reported at aggregate, therapeutic-area level.
Our AI does the heavy lifting, but clinical experts review every result before it reaches you. People stand behind the answer — material judgments are never left to the model alone.
Your protocol and study details stay yours. Every analysis is scoped to your engagement, and the signals and methods behind our scores stay confidential.
A sponsor designing a trial for a rare disease had world-class KOLs at the design table — and still no one could answer the question the whole program hinged on: how many patients would actually qualify under the draft criteria? A KOL sees their own clinic. Nobody sees the whole country. Our data does.
The draft protocol demanded a heavy, documented treatment history. Every criterion felt clinically reasonable — but no one could say what each one cost in eligible patients, or which ones were quietly strangling the enrollment pool. Expert intuition can't answer a counting question.
We translated the draft protocol into patient-level logic over national claims, then isolated each criterion and modeled protocol variants one lever at a time — measuring exactly how many patients each version gained or lost, while checking that the enrolled population stayed aligned with the trial's target phenotype and endpoints. The sponsor could see, criterion by criterion, where the protocol could safely open up and where it shouldn't move.
The final protocol qualified nearly 3× the patients of the initial draft — same scientific intent, dramatically better enrollment odds — with every criterion's cost known before a single site was activated.
A bigger eligible pool only matters if your sites can reach it — and in an ultra-rare disease, most sites, however prestigious, simply don't treat these patients. The conventional shortlist of big academic names answers the wrong question. In fact, we found that many of the KOLs and Centers of Excellence had few or no patients who met the protocol's strict criteria.
We fed the final protocol into our Quantum Intelligence Engine. Because the eligibility model and the site data live in the same database, we could attach to every candidate investigator both a measured enrollment track record and the exact number of protocol-qualified patients seen by that investigator and their institution. Patient access became a hard gate, enriching the shortlist of consistently high-performing principal investigators the engine identified.
A ranked list of sites, each pairing a consistently high-enrolling investigator with a counted population of protocol-qualified patients — including strong sites the conventional lists would never surface, and excluding marquee centers with no reachable patients. Site selection and enrollment de-risked with the same evidence, before activation dollars were spent.
Clinical trial feasibility is the assessment of whether a study can realistically enroll — where the eligible patients are, which sites can reach them, and how fast. We answer it from real, patient-level data across every U.S. institution, not questionnaires or estimates.
We rank sites on two signals most tools use separately — where the eligible patients actually are, and which sites have the track record to enroll them — using the most complete real-world patient data in the U.S. The result is a ranked, defensible shortlist customized to your protocol.
A bid defense is the meeting where a CRO justifies its proposed sites and enrollment plan to a sponsor. We give you real-world evidence for every site on the list, so you can show — not just claim — that the study will enroll.
We run on the most complete real-world patient data in the U.S. — 330M+ de-identified patients, longitudinal and linked across every institution, via a premier data partner — so feasibility and site selection are based on the actual patient record, not a snapshot.