Model

Next comes the model, the cause and effect behind the map. How each decision moves each outcome, against its objective and its constraints. These relationships are nonlinear and dynamic. They change over time, and plant data is never complete, so data alone is not enough.

First principles are built in. Nothing sits in a black box. Every relationship is visible, causal rather than correlated, and deterministic. A Deep Learning Predictor learns each unit's dynamics from years of its own data.

And the models train only on the data your engineers select, unlike a language model. Your engineers can make their own models and create their own competitive advantage.