Manufacturing & Industry

Yes. Defects are always rare while good parts are plentiful, so Neuros frames the problem as anomaly detection rather than classification. The model learns what a good part looks like and flags deviation. Operator decisions are collected during the first weeks, the model strengthens on that feedback, and once enough examples accumulate it moves on to classifying defect types.

No. Neuros builds production systems to run inside the plant: model, database and operator interface all sit on an edge server beside the line. The external connection is used only for reporting and model updates, and when it drops local operation is unaffected; accumulated records sync to the central system once it returns.

At least two sources: signal from the machine (vibration, temperature, current, cycle time) and a history of failures. The second is missing in most plants — maintenance was written on paper. Neuros therefore structures the failure log first: with signal but no labelled failures, a model cannot learn when to raise an alert.