Generate clinically realistic patient cohorts, even when real-world data is scarce.
In rare diseases, early-phase programs, and underrepresented populations, the fundamental challenge is not analytics, it is data. Cohorts are too small, longitudinal records are incomplete, and the patients you need to study may not exist in any available dataset. Teams are forced to make critical development decisions with incomplete evidence or wait months for data to accumulate. Neither option is acceptable when your program timeline is on the line.
Our approach is different. Built on a world model architecture, we generate high-fidelity synthetic patient profiles without requiring massive training datasets. While most methods need substantial real-world data to "learn," ours can generate clinically coherent patient trajectories from minimal input, preserving the statistical structure, clinical relationships, and temporal patterns that matter for downstream analysis.
We have demonstrated this capability by generating synthetic cohorts for amyloidosis, a rare condition with extremely limited real-world data. The resulting cohorts maintained clinically realistic lab value distributions, comorbidity profiles, and disease progression patterns, validated through comprehensive fidelity auditing against reference literature.
Synthetic data is a rapidly evolving frontier. While FDA and EMA frameworks are maturing, we position our current offering for internal decision support, trial design, and exploratory analysis. As regulatory acceptance grows, organizations already validating synthetic evidence will be positioned to move first.
You make better decisions with the data you have today. You unlock analytical capabilities in rare disease and small populations that are simply not possible with real-world data alone, avoiding expensive mistakes before they happen.
Catch what edit checks miss. Lock your database faster.
Traditional clinical data management relies on predefined edit checks that only catch what they were programmed to find. Meanwhile, data managers spend thousands of hours writing manual queries, reconciling safety and clinical databases, and chasing sites for corrections. The result: delayed database locks and spiraling costs.
We deploy reasoning-capable AI agents that analyze your clinical data holistically. They do not just run rules; they understand clinical context. They cross-reference across forms, detect medical inconsistencies, and draft site queries in plain language, ready for your team to review and send.
Your data never leaves your control.
Our offering operates on a zero-retention architecture. We process your data within a secure, isolated environment and retain nothing after delivery. Every engagement begins with a data handling agreement tailored to your security requirements.
You find more errors, fix them faster, and lock your database weeks earlier. Your data management team is freed to focus on judgment calls, not data entry.
Spot design risks before they become enrollment problems.
Protocol complexity is one of the largest silent drivers of trial failure. Overly restrictive eligibility criteria inflate screen failure rates. Excessive visit schedules burden sites and patients. And most design decisions are still made based on internal precedent rather than systematic evidence. By the time enrollment problems surface, your timeline and budget have already absorbed the damage.
We apply AI to the largest body of publicly available trial design data, including registry records, published outcomes, and therapeutic area benchmarks to give your clinical development team an evidence-based assessment of your protocol's design risks.
We are transparent about scope. Our initial release focuses on design benchmarking and complexity analysis using public data. We do not offer predictive enrolment forecasting or site-level performance modeling; these are on our roadmap and will require validated proprietary benchmarks before we offer them.
You catch design issues in weeks instead of discovering them six months into enrolment. Simpler protocols recruit faster, retain more patients, and reduce the risk of costly mid-study amendments.