Industrial environments have the least reliable connectivity and the most immediate consequences. AI that depends on a round trip to a datacentre is the wrong shape for the problem.
Plant floors, remote sites and moving assets lose connectivity routinely. A hosted call that fails is a capability that was never really there.
A decision arriving late is often the same as no decision when machinery is involved.
Edge devices have modest processors and no GPU. Most AI tooling assumes otherwise.
NC AI is designed for CPU execution with a model measured in tens of megabytes, so it can run on the hardware already deployed rather than requiring an accelerator.
Inference runs locally with no account and no outbound dependency, so the capability survives a network outage instead of disappearing with it.
NC compiles to a single binary with no runtime dependencies — a meaningful advantage when updating hundreds of devices you cannot easily reach.
HiveANT investigates anomalies across many signals in parallel, with a digital twin used to predict the effect of an intervention before it is made.
| Product | Role here | Why it fits this sector |
|---|---|---|
| NC AI | Edge inference | CPU-only, small model, works offline |
| NC | Build layer | One binary; simple update and rollback |
| HiveANT | Anomaly investigation | Parallel investigation across many signals |
| NC UI | Local interfaces | Static output; runs on a device with no runtime |
Tell us the specific version of it you have. If we are not the right answer we will say so.