Most industrial AI tools give one number: the predicted moisture, the expected time to failure, the forecast energy use. That number looks precise, but it hides the question that matters on the shop floor: how far can I trust it today?
A forecast with a stated range answers that question. If the model predicts a bearing failure in 12 to 15 days, the maintenance team can plan. If the range is 2 to 40 days, the right action is to inspect, not to trust the model.
Conformal prediction is one practical way to produce such ranges. It wraps an existing model and uses held-out data to calibrate its intervals, so that a range labelled 90% contains the true value about 90% of the time. That guarantee rests on stated assumptions, which we check and document.
Physics helps too. A model that respects the laws governing the process (heat and moisture transfer, wear, flow) stays within physically possible values and needs less data to be useful.
Our rule is simple: no forecast leaves our hands without its range and the evidence behind it. That is what we mean by decisions you can verify.