AI vision that redacts PII before a single frame leaves the device.
A real-time video pipeline that runs inference and strips personally identifiable information on the edge, privacy by construction, not by policy.
Why teams choose it
Privacy-safe by design
Faces and plates redacted on-device, before egress.
Real-time at the source
Low-latency inference on the camera or edge box.
Multi-stream scalable
From a single camera to a multi-channel hub.
What's included
On-edge inference
OpenVINO-optimized models running in real time.
In-frame PII redaction
Identifiers removed before any frame leaves the device.
Fleet operations
Azure IoT device management on Ubuntu Core.
How it works
01
Capture & decode
GStreamer ingest from any camera source.
02
Inference on-edge
OpenVINO models score frames in real time.
03
Redact & stream
PII removed in-frame; only redacted output leaves the boundary.
What powers it: the Pynetra platform
Our on-edge video-inference and PII-redaction engine, GStreamer and OpenVINO on Ubuntu Core, managed through Azure IoT. Here the edge is the governance: privacy is enforced by architecture, not policy. Inference runs on the device, and only redacted frames leave the boundary, so what a camera sees can be audited and what it exposes cannot. The engine is proven; your build starts at integration, not at zero.
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