Predicting the future
to change it.
We are moving healthcare from "Sick Care" (treating symptoms after they appear) to "Health Assurance" (predicting and preventing disease before it starts).
Our AI Architectures
Clinical Transformers
Fine-tuned Large Language Models (LLMs) trained on de-identified EHR data to synthesize unstructured clinical notes and extract longitudinal patient histories.
Graph Neural Networks
GNNs map complex relationships between molecular pathways, genetic markers, and environmental factors to predict disease progression trajectories.
Causal Inference
Moving beyond correlation to causation. Our models simulate "what-if" scenarios to determine the most effective preventive interventions for each patient.
Initial Research Targets
Type 2 Diabetes
Our models analyze continuous glucose monitoring (CGM) data combined with lifestyle patterns to detect micro-anomalies in insulin sensitivity years before A1C levels rise.
Cardiovascular Disease (CVD)
Predicting silent arrhythmias and arterial plaque formation by correlating wearable heart rate variability (HRV) data with genomic risk scores.
Oncology
Developing multi-modal models that ingest radiology scans, pathology slides, and genetic data to identify early-stage biomarkers often missed by human review.