On-Device Inference
On-device inference is running an AI model directly on the user's device — the phone — rather than sending the image to a server for analysis. It enables fast, offline-capable verification with better data privacy.
When verification runs on a server, every check needs network connectivity and a round trip: capture, upload, wait, receive. On-device inference moves the model to the phone, so the verdict comes back locally in well under 200ms. That speed is what makes a verification gate feel instant instead of a loading spinner.
The bigger operational win is offline capability. End-of-ride parking checks happen in dense downtowns and dead zones where uploads stall exactly when they're needed most; deliveries happen in basements and rural routes. With on-device inference the check completes regardless of connectivity, and the result and image sync automatically once the device reconnects.
On-device processing also helps privacy: the image can be evaluated without leaving the device for analysis. VerifyAI uses on-device inference where supported so parking, damage, and delivery verifications are fast, work offline, and keep the photo local during the decision.
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Related terms
A photo verification API is a service that accepts an image plus a policy and returns a structured verdict — typically pass/fail with the reasons and a category — confirming that the photo shows a real-world condition or that it meets a defined rule.
End-of-Ride VerificationEnd-of-ride verification is the check a shared micromobility operator runs when a rider ends a trip: the rider photographs the parked vehicle, and the photo is verified against the city's parking rules before the ride can close.
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