Q I want to give you some time to defend that because most people listening are going to wait. How does this lady I'm just Hearing on Nathan's show have more data than Google. So defend that a little bit. How have you gotten unique data that Google doesn't have?
A Yeah, so there's a lot of discussion around, you know, do you actually need to have large data sets in order to create, you know, impressive and accurate predictive models? You don't because you need to look at the context in which you're using it. So what we are is we've scaled the science of a structured interview. So if you think Google and Amazon, for instance, they take you through these laborious interview processes that are very rigorous, where you're being asked the same questions, and you're all measured against the same rubric. In their case, it's the leadership principles. Now you can use humans to do that, which they can afford to do because they're, you know, a well-resourced organization, but most can't. How do you actually maintain that level of rigor, but remove all the human bias by using technology? That's what we're doing by chat. The data that we have that's first party and proprietary data is the responses to those structured interviews. That's now at about eight hundred million words. It'll be at a billion words fairly soon. And that is our-
AI assessment note: “The data that we have that's first party and proprietary data is the responses”