Victor Szczerba

Five and six million.

So Yeti Data makes what is called a virtual data warehouse that unifies customer touchpoints

and make them actionable using AI.

And our revenue model is a classic enterprise model using a SaaS go-to-market.

Average customer is paying us around $250,000 to $500,000 a year.

No, not at all.

In fact, we love universal usage of our data with inside of a customer, right?

The more they use, the more valuable we are to them.

And so we don't want to put in any kind of artificial barriers and saying, oh, my God, there's only this many people get these reports, et cetera.

Right.

What what they get is they get an all you can eat model.

They point us at all the data that they have about their customers.

We unify it for them and we run it through our models and we say, hey, listen, this is what you want to do about these customers to maximize your revenue and maximize your ROI.

And so what we do is we use all kinds of crazy machine learning, all kinds of crazy AI to kind of tease out things, patterns that are in the data that a lot of humans won't find.

So AI itself is a is a blanket term.

And yet we use it a little bit.

I would say we overuse it.

Machine learning is something very, very specific and not very hocus pocus.

I mean, machine learning was something that I don't know.

actuaries inside the insurance industry have been using for you know a hundred years well so define it as machine learning kind of the machine makes itself smarter the more data you feed it well i mean what's the difference right i mean what you do is you sit there and you make a guess saying hey listen um let's let's do something very specific and and let's not say that nathan is churning let's say that we have a very good idea that nathan is actually going to buy this