Yeah.

Usually, it's Suzy in regulatory or John in R&D.

It's some person that's been there for 20 years that has this context.

And so the mistake that people make, number one, is they think that if I go to an organization, doesn't matter the size.

If you're more than 100 people, and you've been around for more than five years, you have messy data.

And so people go in, and, and the thing they say is, "Give me your data, give me your rules, and I'll create a pilot that can do the jobs of your employees." And that's a complete fallacy.

That is 100% wrong.

Yeah.

What are normally like the, you know, in sales, we talk about, like, sales objections?

M- yeah, I would say that the first objections were always around, like, "Well, who's doing it?" You know? And now we don't really have those objections 'cause we work with like, you know, Kenview, which was part of J&J, so, in consumer health, um, Nestle, so number one F&B, Danone.

So we, we work with, you know, global leaders.

And, and then the other question becomes like, "Well, why is this different than us trying to build this internally with GPT?" Uh, and it's always a software question.

So the question for me to any leader is like, "Well, one, go try it, and then, like, have it, uh, like tested against our software, first of all.

Like, how long would it take you to build? Like, would it-- is it worth you spending millions of dollars, team resources, and probably not even getting it right?" Right? "Or hiring a Silicon Valley, like VC-backed AI native company that only works on this problem.

Like, what's better, right?" And then the second part is, I think one of the flaws that people think about w- or don't think, understand is that like horizontal AI, like GPT or Claude or Microsoft Copilot, they can, their, their job is to cover as much ground as possible.

Mm.