Edge AI.
Why detection belongs on hardware you own, next to the camera.
A fab makes silicon, not a deployable edge system
Tesla and SpaceX are putting an initial 16.8 billion dollars into a Texas fab, partly for edge inference chips. One number in that announcement does not survive scrutiny, and the harder problem is not wafer supply anyway.
Our thermal model's evaluation was wrong twice before it told us the truth
A thermal perception backbone looked broken, then looked useless, then turned out to be neither. The bug was never in the model. It was in the test we used to judge it, and in a data pipeline that let RGB photos into a corpus we called thermal.
Who can turn your AI off?
In June 2026, the most powerful AI models on the market were gated, pulled, and partially reinstated by parties their customers don't control. For most software that's a policy story. For safety-critical systems, it's an ownership question.
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.
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.