Rob Heiser

What we really do is we're a group of library scientists, researchers, and we look at, if you've ever seen your bank statement, you'll see a lot of hieroglyphics.

It might say Starbucks and some numbers, a store number, city.

What we do is we take all that transaction data and we do research against them.

And we have a huge rules engine that's based on machine learning, artificial intelligence, but also real human research.

And that creates a very high quality, high categories and high match rate of every transaction that runs through a banking system.

So to give you an example of that, if you see something that's like WDW underscore CRO underscore REF, most institutions won't know what that means.

What we know is that is a customer of Walt Disney World and actually a Vacation Club buyer.

So that means that they like Walt Disney World and they also have a Vacation Club membership.

So it means a lot in terms of what a bank's looking for, in terms of what their customer might be doing next.

So we sell a lot through what's called a core, a financial or fintech company.

They have core systems, a bank core system.

We sell through them, but the end customers, like a credit union, their financial institutions, we sell all the way from some of the, you know,

maybe 250 million in assets all the way up to one that has over a trillion in assets.

No, more to the bank.

Financials.

Okay.

Yeah, that's who, someone like them, but not of that scale.

You know, we're more mid-market and down market.

About a hundred billion and under is typically where we play.

Oh no, absolutely.