Reliable representations of how the healthcare physical world behaves, for the software systems being built on top of it.
Our models learn by prediction in representation space rather than reconstructing pixels. They build an internal model of anatomy and how it behaves, then predict the parts they cannot directly see. This makes them robust to the noise of real clinical signals, efficient where labels are scarce, and non-generative, so they never fabricate detail they were not shown.
We sell access to these models and the tools around them to hospital teams working on robotics, medical imaging understanding, and autonomous decision-making, where a dependable representation of the physical world is the hard part.
A foundation model for endoscopic and cross-sectional imaging of the upper airway and skull base. Detects pathology, stages lesions, and reads functional anatomy in real time.
The same latent-predictive architecture extended to pulmonary imaging and bronchoscopy, bringing consistent, function-aware assessment to respiratory medicine.
Hospital and research groups building medical imaging understanding, surgical robotics, and autonomous clinical decision-making start from the same bottleneck: a trustworthy representation of the physical world. ClearJEPA provides that layer, on the equipment clinics already operate.
Our aim is for these models to become a standard of care everywhere, including the emerging clinics across Asia, Africa, and South America where specialist coverage is thinnest and an accurate, early diagnosis matters most.