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We teach AI how real-world medicine is done
Datasets and environments to help healthcare agents succeed at complex multi-step tasks grounded in real patient cases
A world-class founding team
PROBLEM
Medicine is not Q&A
Today’s AI models can outperform doctors on standardized tests.
But they struggle with real medicine: a long chain of decisions, detours, tradeoffs, and context, where judgement errors compound.
The training signal for that doesn’t exists on the internet or in papers — but it is a reality in every hospital.
CONTEXT
Health agents lack a verifiable reward
Human feedback made LLMs interactive.
Expert reasoning traces made them useful for professional tasks.
The biggest capability gains are nonetheless from RL with verifiable rewards, hence why math and coding have improved so quickly.
1. Supervised fine-tuning
Teaching models how experts reason across multi-step tasks such as navigating PACS, EHR, and multimodal data.
2. Reinforcement learning
Expert-crafted rubrics with real-world outcome grounding to generate scalable reward signals across realistic settings.
3. High-Fidelity Environments
FHIR-native EHR environments and PACS simulations for agent navigation, populated with de-identified real data.
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