Definition
Edge inference
Running AI model decisions on local hardware near the camera feed.
What it means
Edge inference means the model analyzes video on a local node rather than sending every frame to a remote service first. The output is a local decision and event route.
Why it matters
Local inference reduces dependence on internet latency and external service availability for the first safety alert.
Evaluation questions
Which hardware runs the model?
How is latency measured from camera to alert?
Can the node continue working during internet interruption?
Continue Exploring
Platform architecture
Review how DHI runs inference, event routing, and camera ingest.
Edge AI safety evaluation guide
Use a structured checklist to evaluate platform fit before a pilot.
Pricing and pilot scope
Understand what changes the final pilot and rollout scope.
Validate edge inference in a real pilot.
Use your current cameras, VMS, and response workflow to test whether the concept works in one defined zone.
The checklist is built for operators evaluating a live pilot in the next 30 days.
Request a demo
See the flow on a real operating scenario and scope a pilot around one facility or corridor.
See deployment architecture
Review camera ingest, edge inference, alert routing, and what stays on-premises.
Get the implementation checklist
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Talk to an engineer
Bring camera count, VMS constraints, latency expectations, and privacy requirements to a technical review.