Clinical-grade AI at the point of care
Bring AI to regulated medical hardware — private, deterministic, and fully under your control. Wendy runs imaging, monitoring, and inference directly on the device, so patient data never leaves the room.
Inference runs locally, so imaging, vitals, and recordings never leave the hardware. No cloud round-trip means no PHI in transit — a dramatically simpler HIPAA and GDPR posture.
Run segmentation, detection, and guidance models on endoscopy, ultrasound, and surgical video with deterministic, sub-frame latency on Jetson-class compute.
Fuse vitals, waveforms, and camera streams at the bedside to catch deterioration early — alerts fire in milliseconds, even if the hospital network goes down.
No shared cloud tenancy, no variable network latency. The device behaves the same in validation and in the field — exactly what regulators expect of a medical device.
Hardware-backed device identity, signed and verified OS images, and encrypted storage keep every device authenticated and tamper-resistant across its clinical life.
Tamper-evident, on-device logs capture inputs, model versions, and decisions with full context — evidence for design history files, audits, and post-market surveillance.
Push signed application and full-OS updates through controlled rollouts. Updates are atomic — a device is never left half-updated or bricked mid-shift.
Monitor every device across hospitals, clinics, and labs from one console: versions, health, and status in real time, without touching patient data.
Ambulances, rural clinics, and field hospitals rarely have reliable connectivity. Wendy devices run fully offline and sync state when a link returns.
Medical products live for a decade or more. First-class support for NVIDIA Jetson and Raspberry Pi class hardware keeps a validated stack stable for the long haul.
Common questions
Direct answers about local inference, privacy, traceability, and deterministic physical AI for regulated medical hardware.
Medical-device AI often handles imaging, vitals, recordings, or other sensitive data at the point of care. Running inference locally can reduce data movement, avoid variable network latency, and keep device behavior more deterministic for validation and field use.
WendyOS fits imaging and vision systems, bedside monitoring, alerting, audit trails, fleet health, controlled updates, and offline-first medical hardware that needs local compute and traceable software behavior.
WendyOS can keep model versions, application versions, inputs, logs, and device state tied to the device. That helps teams build the evidence trail needed for audits, post-market review, and controlled software rollouts.
Tell us about your device and regulatory path — we’ll show you imaging, monitoring, and inference running fully on your hardware.