An automation or prediction system is only as trustworthy as the data and the constraints it is built on. Before any model or automated process is designed, we establish what the underlying data can actually support — and, just as importantly, what the physical or operational reality around it will not allow, regardless of what the data suggests is possible.

A predictive model that ignores a real engineering limit is not a more advanced solution; it is a wrong one. This is the difference between AI applied as a general capability and AI applied within engineering reality: the same judgment that shapes a feasibility or risk assessment shapes what the automation is allowed to recommend or do. The result is a system built for decision support in a specific operational context, not a generic model in search of a use case.