01What the models watch
Bearings, seals and motors fail with signatures: rising vibration at characteristic frequencies, current-draw anomalies, temperature drift. A model trained on those signals detects the developing fault long before a threshold alarm would.
02Why the edge
Running inference on the node keeps latency low, works through network outages, and avoids streaming raw vibration data to the cloud. The cloud gets the verdicts and trends, not the firehose.
Common questions
The ones we're asked on every first call.
It varies by failure mode, but bearing and seal faults often surface days ahead — enough to schedule the fix into planned downtime instead of reacting to a stoppage.
Want this run on your numbers?
Send us your current stack and what it needs to do. A controls engineer replies with a candid one-page teardown — no deck, no sales call.
Request a teardown →