Start with the workflow, not the model
The best healthcare AI disappears into an existing workflow. Before choosing a model we map who acts on the output — a nurse, a triage system, a patient — and what decision it changes. That decides everything downstream: latency, explainability, and how the result is surfaced. An alert a clinician can't trust or act on is worse than no alert at all.
Privacy and accuracy are the product
Healthcare data is sensitive and regulated. We design HIPAA/GDPR-aware from day one — minimizing data, encrypting it, and keeping an audit trail — and we validate models against real clinical edge cases, not just aggregate accuracy. For ML-assisted diagnostics we favor explainable outputs and human-in-the-loop review so a clinician stays in control.
What we build
Remote patient monitoring that connects wearables and devices to records; ML that spots patterns across large datasets for earlier detection; telehealth with privacy-compliant video; and wellness apps that keep people engaged between visits. We've shipped healthcare products (e.g. Ealthiness) that pair a clean patient experience with the data plumbing behind it.
Integrate, then iterate
AI in healthcare earns trust over time. We ship a focused first version, measure it against real outcomes, and expand scope as confidence grows — rather than launching a black box and hoping. That's how AI moves from a pilot to something clinicians rely on.
