Clinical trial design and patient stratification rely on fragmented data sources — genomics pipelines, EHR systems, biomarker repositories, prior trial data, and published research.
Traditional analytics platforms identify correlations but lack live operational context, explainable reasoning, and governed workflow execution to dynamically adapt trial parameters or stratify cohorts in real time.
Syntes AI builds dynamic context graphs linking patient phenotypes, genomic markers, treatment protocols, prior outcomes, and real-time trial data.
AI agents:
Continuously stratify patients based on evolving biomarker and clinical signals
Simulate cohort adjustments before protocol changes
Identify emerging response patterns
Recommend adaptive trial modifications
Maintain complete reasoning lineage for regulatory and IRB review
Context graphs connecting clinical, genomic, and protocol data
Real-time evidence prioritization across heterogeneous systems
Simulation before protocol modification
Policy-aware governance aligned with regulatory constraints
Full audit trail and explainable decision chains
Life sciences organizations accelerate trial timelines, improve patient stratification accuracy, reduce protocol amendments, and increase probability of clinical success — while preserving regulatory transparency.
