Connecting production equipment to decisions that matter.
Industrial deployments differ from consumer IoT in one decisive way: the equipment was not designed to be connected, and cannot be replaced to suit the project. The work is therefore integration against machinery of varying age and protocol, in an environment where downtime is expensive and a wrong reading is worse than no reading.
Data from equipment of varying age and protocol, brought into one place without disrupting the line.
Visual quality checks at line rate. Lighting and dataset design decide accuracy far more than model choice does.
Component-scoped models where failure history supports them. We would not claim whole-machine prediction, because the data rarely supports it.
Where time is genuinely lost, measured rather than estimated — often the analysis that reorders the improvement plan.
Processing on the line where latency or connectivity requires it, within real power and thermal limits.
Production data joined to planning and finance systems, since the valuable questions cross those boundaries.
Usually, via OPC UA, Modbus or direct sensor retrofit depending on what the equipment exposes. The audit comes first because it determines what is possible without replacing machinery, which is rarely an option.
Scoped to specific components, often yes. It requires enough recorded failures to learn from and maintenance records clean enough to label them. If a vendor offers whole-machine failure prediction without asking to see your maintenance history, treat that as a warning.
It should not, and that constraint shapes the approach. We pilot on one line, read-only where possible at first, and validate against known conditions before anything influences a production decision.