Problem
Pharma is unable to reliably predict patient-specific clinical outcomes, leading to high clinical trial failure rates and suboptimal use of approved therapies. This is driven by fragmented and unstructured data and the absence of robust, scalable patient-level prediction capabilities.
Solution & Use Cases
Our novel platform accurately predicts patient-level drug response, side-effects and disease state, leveraging agentic-AI-based processing of multi-modal real-world and trial data backed by proprietary datasets, combined with therapeutic-area-specific ML prediction models. This enables optimized trial enrichment, in-silico therapy comparison, and precision targeting at the point-of-care - driving better success rates, time to market, and commercial outcomes.
Traction / Validation
Our recognitions include BioNJ’s Most Innovative AI Solution, Meet The Drapers NY, TiE50, and selection to JLABS. Our approach is validated across 11+ proof-of-concept studies, with multiple conference presentations and publications accepted and 9 more submitted. Our customers include leading biopharma partners and academic medical centers across metabolic disease, immunology, dermatology, and oncology.
Future Plans
Going forward, we expect to deepen penetration in our core therapeutic areas and expand into neurology and respiratory, while driving broader platform adoption with existing and new pharma partners.
Management
Vinodh Balaraman, CEO / Founder [email protected]
