Process and formulation R&D traditionally advances by running batches and seeing what happens. This platform lets engineers plan experiments against a model instead: propose candidate conditions, predict outcomes with quantified uncertainty, and spend physical batches only where the model is genuinely unsure.
Trial-and-error experimentation is slow and expensive: each batch consumes material, equipment time and operator attention, and the search space of process conditions and formulations is far larger than any experimental budget.
The client needed two things at once — predictive models good enough to trust, and an application disciplined enough to deploy in an industrial setting, with real users, access control and an audit trail.
The headline result is a soft sensor for a distillation process — inferring a quantity that is expensive or slow to measure directly from signals that are cheap and continuous.
On three held-out runs it reached an R² of 0.81, 0.80 and 0.67, with mean absolute error between 0.04 and 0.07 mol/mol. The third run is visibly the weakest, and the interface says so rather than hiding it.
Just as important as accuracy is calibration: the 90% prediction intervals covered 94–97% of held-out measurements, so the model is slightly conservative rather than overconfident. For an engineer deciding whether to trust a prediction or run the batch anyway, that is the number that matters.
Shipped through production hardening, with the legacy application fully migrated onto the platform. Because it is built on the kernel, every model run, parameter change and prediction inherits the same authorization, audit and observability guarantees as the rest of the client's AI estate.
Note: this case study is deliberately written without client names, product names or customer data. The model metrics quoted here are logged evaluation results on held-out runs, not estimates.