Edge inference hardware
Compute is sized against camera rate, model memory, thermal limits, and the site's support conditions.
CAPABILITIES / VISION AND EDGE
Edge deployment
Inference on the device so alerts fire in milliseconds and the site keeps working when the link drops.
What this covers
Model optimization
Quantization and runtime profiling reduce latency while validation checks the accuracy cost.
Offline and intermittent operation
Local queues keep decisions moving and synchronize records after the network returns.
Fleet rollout and rollback
Signed releases move through staged device cohorts with health gates and a previous version ready.
Telemetry back to the platform
Health metrics and sampled evidence leave the device under explicit bandwidth and retention rules.
How it shows up in the work
ENTERPRISESCatch defects before they move downstream.Inspection that happens on a schedule finds problems late. Cameras and agents at the line can watch continuously and route what they find to the people who fix it.NATIONSBuild the trusted national data and AI grid.Institutions hold data that would be worth more together, and they're right to be careful about sharing it. A grid makes sharing governed rather than informal.
WHERE IT FITS
Hardware and network discovery comes first; a site proof precedes any fleet rollout or operate term.
