AI at the point of care
Bring AI to medical hardware while keeping it private and under your control. Wendy runs imaging, monitoring, and inference directly on the device, so patient data can stay in the room.
Inference runs locally, so imaging, vitals, and recordings can stay on the hardware instead of crossing a network to a cloud service.
Run segmentation, detection, and guidance models on endoscopy, ultrasound, and surgical video with low latency on Jetson-class GPUs.
Fuse vitals, waveforms, and camera streams at the bedside to catch deterioration early. Alerts come from the device itself, so they keep working if the hospital network goes down.
Workloads run on the device rather than a shared cloud, so the software you validate is the software that runs at the point of care.
Each device gets its own certificate, every connection uses mutual TLS, and apps run sandboxed with only the hardware access they declare. Encrypted app storage is available as an opt-in on Jetson.
Apps log inputs, model versions, and decisions to an OpenTelemetry collector on the device, and the agent records app versions and resource use, for your own traceability and audit process.
WendyOS installs each update to a second partition. If the new system fails to boot or fails its health checks, the device returns to the previous version on its own.
Wendy Cloud, now in beta, shows which devices are online and reaches them through an outbound tunnel, with no inbound ports opened on the hospital network.
Ambulances, rural clinics, and field hospitals rarely have reliable connectivity. Wendy devices run fully offline, and you can deploy, update, and debug them over USB-C or the local network.
Medical products live for a decade or more. Build on NVIDIA Jetson and Raspberry Pi hardware with an open-source stack your team can pin, audit, and maintain.
Common questions
Direct answers about local inference, privacy, traceability, and predictable physical AI for 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 predictable for validation and field use.
WendyOS fits imaging and vision systems, bedside monitoring, alerting, and offline-first medical hardware that needs local compute, controlled OS and app updates, and logs kept on the device.
Apps can log model versions, inputs, and outputs to the OpenTelemetry collector on each device, and the agent records app versions and device state. Teams can use those records in their own traceability, audit, and post-market review processes.
Tell us about your device and regulatory path, and we’ll show you imaging, monitoring, and inference running fully on your hardware.