Inference at the edge
Bring model inference closer to devices while retaining security controls.
Explore this feature →AIsense / AI skill lifecycle
Under Development
Treat AI skills as governed software
The AIsense design connects model intake, evaluation, signed artifacts and controlled delivery.
What this means for your team
- Evaluate model/artifact provenance, license and hardware suitability.
- Package and approve device-specific skills through the intended shared supply-chain foundation.
- Design staged delivery, health checks and rollback before expanding a deployment.
- Bind authorization and revocation behavior to the device’s real connectivity and trust state.
A practical example
An updated skill is released to a small device group before a wider rollout.
How it works
Select a step to read its explanation
- Choose a candidate AI skill for the device task and hardware profile.
- Evaluate its provenance, suitability and intended quality or safety criteria before release.
- Apply the designed approval and signing controls to a device-appropriate artifact.
- Deliver the approved skill in controlled stages rather than assuming every device should update at once.
- Observe the rollout and use the intended rollback or revocation controls when needed. The AIsense lifecycle is under development.
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This example illustrates the journey. Select any step to explore it at your own pace.
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Live edge context
Connect edge inference with live operational context.
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