Applied AI
Clinical Trials AI
Compressing the timeline from longevity hypothesis to human evidence — using AI to design smarter, faster, more decisive trials.
What we study
We apply machine learning to every phase of the clinical trial process — from in-silico cohort simulation and adaptive protocol design through to real-time biomarker monitoring and dropout prediction. Our models are trained on historical trial data spanning hundreds of longevity and age-related disease studies.
Why it matters
Longevity interventions face a unique challenge: the endpoint — extended healthspan — takes decades to observe. AI lets us design trials around surrogate biomarker endpoints, identify the patient subgroups most likely to respond, and detect efficacy signals in months rather than years — compressing the path from hypothesis to human evidence.
Our approach
We build reinforcement learning agents that simulate trial arms, optimise adaptive dosing schedules, and flag safety signals in real time. Combined with natural language models that mine trial registries and literature for prior art, our platform gives researchers a full AI co-pilot from protocol inception to regulatory submission.
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