Brian O'Grady

And then you can click on ladder as it auto populates for you.

It takes it to a page and then it'll show you a bunch of ladders.

This has been the market.

These kind of like, you know, retail giants have traditionally served their search via elastic search, via solar, via open search, what have you.

But where Quadrant comes is that we are not focused on the text layer of the search.

We are instead focused on something that we call the semantic layer of the search.

And this is where maybe things get a bit deep.

This is where things get relatively nascent.

But there was this development in 2019, 2020 in the machine learning space where researchers were able to

transform text into numerical representation that preserved meaning.

I'm going to stop there to see if Mike, if you have any questions on that.

Yeah.

So, and this is where it starts getting into what the search engine was built for.

And this is where I'm going with my whole, you know, text having a numerical representation.

Sure.

So yeah,

The idea is that if you're just doing, say, keyword search, then that leaves out a lot of context, right?

Like if I search for a bat on a generic website, let's say I search for bat on walmart.com, it might return...

you know, stuff to do with like Halloween because thinking bats like the animal, it might return baseball bats.

It might return something else, right?