How to Evaluate Edge AI Safety Platforms

Start with the safety decision

Do not evaluate an edge AI safety platform as a generic analytics dashboard. Start with the physical event you need to prevent or respond to faster. A forklift conflict, platform fall, smoke plume, person-down event, and restricted-zone intrusion all have different timing, camera, and response requirements.

Evaluation area 1: latency

Ask where inference runs and how end-to-end alert latency is measured. The relevant number is not only model inference time. It is the time from visible event to usable alert in the operator workflow. For safety events tied to motion, seconds matter.

Evaluation area 2: camera fit

The platform should work with the cameras already covering the risk zone. Confirm RTSP and ONVIF support, stream stability, resolution needs, lighting limits, and whether camera angles are good enough for the selected incident class.

Evaluation area 3: VMS integration

If operators live in Genetec, Milestone, or another VMS, the AI platform should route events there. A parallel dashboard may look good in a demo and fail in a real control room. Ask how alarms are named, prioritized, acknowledged, reviewed, and archived.

Evaluation area 4: privacy posture

Where does raw video go? If footage streams to a third-party cloud, the privacy and compliance review changes. If inference runs on site and only event metadata moves, the review is simpler and the risk surface is smaller.

Evaluation area 5: reliability

Ask what happens when the internet drops, when a stream restarts, when the VMS is under load, and when the model produces a low-confidence event. Safety systems need predictable behavior under imperfect conditions.

Evaluation area 6: pilot proof

A serious platform should be willing to prove value in one zone before asking for a broad rollout. The pilot should define camera list, incident class, alert path, false positive threshold, latency target, and review owner.

Evaluation area 7: evidence quality

The platform should produce evidence a safety team can use: camera, timestamp, zone, event type, confidence, clip reference, and review status. Without that structure, the deployment becomes another pile of video.

A simple scoring model

Score each vendor on five questions. Can it run locally? Can it use the current cameras? Can it route into the current VMS? Can it explain what data leaves the site? Can it prove one measurable workflow in a pilot? Weak answers in any of those categories will become deployment problems later.

The DHI view

DHI is built around the idea that safety intelligence should fit the infrastructure a serious site already runs. The cameras stay. The VMS stays. The raw video stays on site. The new layer is the local intelligence that turns high-risk seconds into events people can act on.