In manufacturing, AI must be deterministic and error-free. A battery installed in a car needs to be the right one, every time. Large language models struggle with multiple constraints and uncertain reasoning, so their suggestions must be verified against existing production software before use.
Probably correct is not good enough. Like, I do not wanna have mistakes when my car goes down the manufacturing line. I wanna make sure that the right battery is there to put into that car. And maybe yours is next and it needs to have a different battery. Like, it needs to be deterministic. I need to know. And you're absolutely right that when we put this layer on top of it, that, you know, that's what our customers are expecting is, is all of this to make that experience even better for our customers. It's also interesting because, of course, when we're doing all this GenAI stuff, I'm playing on, you know, some large language models and I'm saying, hey, let me model something kind of relatively simple and see how far I can get. And one of the challenges is, I mean, it seems to do quite well up to a point, but I have a very hard time when I Then ask it, oh, could I get this feature and this feature together? And it, you know, does some reasoning and it's like, but is it right? Like, is it absolutely— like, can I believe it? And, you know, I'm then going back to our software to say, is that right?
