A formulation prediction is useful only when a researcher can relate it to the variables they can actually change: adhesion, release, germination, environmental compatibility, and process conditions.
Explainability helps turn a model output into a conversation. It gives the team a way to ask which inputs mattered, whether the result fits domain expectations, and what should be tested next.
SeedCoat AI is designed around that loop: explore, explain, test, and feed the learning back into the next formulation decision.