Low-latency On-site Processing
On-device inference usually feels snappier where milliseconds matter.
Solutions & Services
Edge AI Deployment
When the cloud is not always reachable, we scope inference, control, and events so they can stay on-site.
Field sites often have shaky networks, tight latency needs, or limits on sending data out. Keeping decisions closer to the device is often the easier path to live with.
We weigh device performance, power, how people run the site, and what remote care is possible, then tune on-device AI in stages.
Sizing models after CPU, power, heat, and storage
Edge boxes hit limits on CPU, memory, heat, storage, and power all at once. Lifting a server-sized model across as-is is often painful.
We pick runtimes and compression that fit the box, and line up events so they reach control and screens without extra hops.
Sending only logs or summaries upstream while keeping decisions local tends to leave something usable when the link drops.
Build Priorities
Device-specific Optimization
Models and runtimes are trimmed or swapped to fit SBCs and embedded targets.
Offline Continuity
Splitting responsibilities makes it easier to keep core behavior when the network goes away.