Privacy.
How on-site processing makes video privacy the default, not a promise.
The real divide in camera AI isn't capability. It's control.
A US city wants facial recognition on its buses, and the fight that broke out misses the point. The line between safety and surveillance was never the technology. It is who controls it, where it runs, and what it is pointed at.
Where does your video actually go?
The question that quietly stalls AI camera projects, and why keeping footage on-site turns privacy from a liability into the default.
Use the guide, then validate it on your cameras.
Don't let the guide be the end of it. Take the checklist, a clear deployment path, and a direct line to ask implementation questions.
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
Download the deployment checklist buyers use before green-lighting an industrial AI pilot.
Talk to an engineer
Bring camera count, VMS constraints, latency expectations, and privacy requirements to a technical review.