We extract multimodal signatures from neuro-immune brain organoids, align them to large-scale human datasets, and build AI models that help pharma predict which compounds will work — in humans, before the clinic.
Neuro-immune organoids → Multimodal disease signatures → Human-anchored AI → Clinical prediction
CNS drug development has the highest failure rate in medicine — and the root cause is a data problem. Preclinical models don't capture human neuro-immune biology, so compounds that look promising in the lab routinely collapse in trials. The field has been flying blind, optimizing for the wrong biology in the wrong species. What's been missing is a system that generates human-relevant disease signatures and validates them against real patient data — before a single dollar is spent in the clinic.
We generate patient-derived brain organoids that faithfully recapitulate the neuro-immune interactions driving CNS disease — biology no animal model can replicate.
We extract deep, multimodal readouts from our organoids — spanning transcriptomics, proteomics, and functional phenotypes — to build rich, disease-specific biological signatures.
Our AI mines large-scale public human datasets to identify where organoid signatures map to real patient biology — anchoring our models in clinical reality from the start.
We partner with pharma and biotech to integrate proprietary clinical and trial datasets, continuously enriching our models and expanding their predictive power across indications.
Every dataset added makes the platform smarter — a compounding AI foundation model that gives pharma a human-grounded lens to predict drug response before entering the clinic.
We bring together neuroscience, AI, and translational thinking to build systems that bridge discovery and the clinic.
CEO — Neuroscience & AI
CBO — Strategy & Operations
Our advisors ensure biological rigor, clinical relevance, and translational focus as we scale the platform.
Organoid Biology
Interested in learning more about the platform or exploring a partnership? We'd love to hear from you.