Take the guesswork out of your next trial with AI-driven precision.

Sponsors hold extensive datasets across programs and therapeutic areas, but these assets are difficult to leverage in a unified framework, leaving valuable insights untapped and limiting their ability to use past evidence to inform study design and strategic decision-making.

Transform structured and unstructured historical data assets into harmonized, analysis-ready assets in a searchable, query-ready format. Empower teams to explore subpopulations, patient-level outcomes, and cross-trial patterns to turn past studies into a reliable evidence resource for trial design, indication strategy, and label expansion.

Teams can spend months locking down key protocol details. Limited evidence for selecting endpoints, time points, and eligibility criteria increases the risk of underpowered analyses and costly redesigns. The result is slower progress, tougher cross-functional alignment, and lengthy internal iteration cycles.

Design smarter trials with unified evidence synthesis and simulation tools that benchmark plans early and stress-test endpoints, eligibility, and sample size assumptions for stronger confidence in trial outcomes.

Clinical development is hindered by slow enrollment and high variability that obscures true treatment effects. Early-stage studies often lack reliable comparators that yield unclear go/no-go decisions, while late-stage trials that can't afford to fail may fall short with noisy signals.

Digital twins are AI-generated forecasts of an individual trial participant’s clinical outcomes. They provide credible external comparators for early-stage and open-label studies, reduce variability for clearer early signals, and strengthen portfolio decisions by improving the detection of treatment effects.