Nycole Rosen (10:09.698)
You have to have really good data in a good structure and you have to have standardized processes. Otherwise, you're pointing AI to a mess and hoping that it will be able to figure it out. And if we as humans can't figure that out, then we can't really expect AI to be able to do that. So Pedagogue Systems was originally formed with a platform, and we we were leaning really toward the governance of AI where we would document
everything that happens in a in a system, that would be maybe the the offer of a job or the refusal of a time card or a a credential that might expire. Every decision that's made in the staffing process has some type of record attached to that, whether it's decided by a human or it's recommended and approved through AI. And that's what that's what our platform was built to do.
And early on, we felt really good about that. We were excited about it. We felt like this is this is gonna revolutionize things because the staffing industry also is ripe for lawsuits or or problems if people don't regulate their AI. So we led with that and we found initially that a lot of people were unsure about it. It was maybe premature.
People looked at it and thought, I don't understand this. It's very technical. How is this really going to help me? And most of the people who are sitting in the demos and who are looking at technology are looking at front office facing things. How can I onboard people quicker? How can I automate the hiring process? Not necessarily how can I govern the AI underneath all of that. So we sort of
hit pause and did a pivot. We still have that platform. We still believe strongly that that's necessary. And we think that the industry is going to catch up to that when unfortunately we'll probably have something bad happen with AI somewhere out there. And auditors are going to look at that and realize that there's really no record. There's no documentation of what's happened.
Nycole Rosen (12:21.442)
And we've seen that happen in other companies too, outside of the staffing industry, where AI kind of goes rogue and there's no human regulating or governing that. So we do believe people are going to come back to that and be ready for us. In the meantime, we're going to help them get ready. So what we've done is we have pivoted from selling or starting out with the platform to really understanding a firm's needs, going in and doing an assessment of their technology. What systems do they have in place now?
Matthew Mottola (12:30.067)
Yeah.
Nycole Rosen (12:50.594)
What does that architecture look like? What standardization needs to happen across their processes? Do they have multiple brands that all operate differently, but they're using the same systems? And then what does their data look like? We have yet to find, and if there's one out there, I would love to be proven wrong, but we have yet to find a firm that has perfectly impeccable clean data where you could point an AI at it and have it work immediately.
So our focus now is to go in and understand where that firm is, make recommendations, and identify some things in their either processes or technology that could help them out. And then we work with them side by side to implement those changes, to potentially update some of their technology if needed, and clean up their data. Once that's done, then they're ready for AI.
Whether that's an AI tool that has yet to be designed, because they come out every day, there's something new, or it's something that we can help them design themselves. We do see an appetite for a lot of people wanting to do that. But that's been our pivot. And we are so excited to be able to go into organizations with our experience and our background and be able to make those recommendations to help them get ready for what's coming.
