The Dhi Blog.
Perspectives on edge AI, video privacy, and deploying safety analytics on the cameras you already have. No hype, just how this actually works.
Your "edge AI" might just be the cloud with extra steps
A cloud provider just discontinued its edge-vision product. Here's what it reveals about how most camera AI is really built — and the one question that separates real edge from rented edge.
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.
You don't need new cameras
Rip-and-replace is what kills safety projects. How to add AI to the CCTV and VMS you already run — starting with a single camera.
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.