An AI system trained on product models suggests configuration options like battery sizes and feature combinations. A deterministic layer then maps those suggestions to concrete rules, showing instantly which features can be paired together. This lets teams iterate and validate product models fast.
You let the AI, along with the tool that is trained in product models, what is it that these look like, and bring that together. It will make suggestions for you. Based on this information, it seems that there's a battery and the battery comes in different sizes. Oh, and there's a relationship. And it turns out there's a relationship to towing and maybe there's a relationship to the heated seats and maybe there's a relationship to all sorts of other things, of course. And Let's start building up this product model from that information. And so there we're using AI to make suggestions into how we could— what you might want as rules. But here it's also super important that this VT technology is also saying, by the way, if we write these rules, Here will be the output of them. Here are the combinations that will be allowed or are disallowed. And that is sort of that determinism backbone, right? So AI suggests something, the determinism of the underlying technology maps it to you and shows you straight away, this will be the impact if you enter that rule into the system.
