01
Site-level response
Keep selected analysis near the cameras when the decision needs to happen close to the physical environment.
Edge processing
Put the decision closer to the site.
Evaluate local processing when the workflow benefits from site-level inference, controlled data movement, or resilient connectivity.
AI layer activeEdge processing
The location of inference changes the trade-offs.
Edge is a deployment choice, not a universal answer. It should be evaluated against the site and workflow.
02
Bandwidth control
Consider processing selected signals locally instead of moving every raw stream elsewhere.
03
Resilient operation
Design for the reality of intermittent connectivity, local dependencies, and recovery procedures.
04
Central oversight
Use an appropriate management layer to monitor and update distributed deployments.
Edge planning
Design for day two from the start.
Distributed AI creates an operating responsibility around devices, software, security, updates, and support.
Size
Review streams, resolution, models, event rates, and available compute.Secure
Define device access, network boundaries, update paths, and data handling.Deploy
Connect a representative site and validate local workflow behavior.Manage
Monitor health and establish the process for updates and support.
A measured choice
Local does not mean unmanaged.
Edge processing can help with latency, resilience, or bandwidth, but it introduces a distributed fleet that still needs inventory, patching, credentials, observability, and a replacement plan.
The proof of concept should test both the model and the operating burden.
Evaluate the edge case
Bring the site where connectivity changes the decision.
We will help map the streams, compute, network, and operating controls for a bounded test.