Code for imaging. AI models that keep working in the clinic.

What you see here is a CT scan of a patient with a suspected stroke. We build the AI model that analyzes a scan like this. And not only CT. We work on any medical image, whether it is an MRI, an ultrasound or a pathology slide.

We build the software that makes AI work in clinical practice, the model and everything around it. That starts with the data, and with understanding what we see in it. Collecting representative data and good annotations is the hardest part, as a model is only as good as the examples it learns from.

An AI model is never finished. When the scanners, the clinical insights or the patient populations change, the data drifts away from what the model has learned, and its performance degrades. So a model has to be monitored and updated, and sometimes rebuilt. AI development in this domain never stops.

We reason from first principles and the best code is no code: what we do not build cannot fail and what we do build is only what the job needs.