AI formulation search lets a chemist describe a target, such as a coating with a given gloss, cure time and VOC limit, and see the closest formulations your company has ever made, what they achieved in testing and why they were dropped. It then suggests the next experiments most likely to close the gap, ranked with their uncertainty. The chemist chooses what to run, and every result goes back into the record.
It works for coatings, adhesives, polymers, lubricants, personal care and agrochemical formulations alike, because the method is the same: join recipes, raw-material data and test results into one record, then search and model over it. Suggestion methods range from similarity search and property models to Bayesian optimisation, chosen to fit how much data you have.