Darren Patterson

So I get the chance to work with all sorts of different types of organizations.

In the Pacific Northwest, I work with an organization that runs a pet adoption center.

Oh, wow.

So this is a pet adoption center.

It's not necessarily the big enterprise use case you think of, but it's like how make gets applied in the real world.

And there's like one guy who's volunteering his time to help them set up.

Because if you think about it, if you work at a pet adoption center, you don't like literally they have Salesforce to track people and track the adoptions process, et cetera.

They don't want to spend time in Salesforce.

And so literally simplifying that process, I was blown away that...

One of the oldest school problems that make an AI does really well at is actually matching.

So what I would call fuzzy matches.

So within adoptions, they actually have a do not adopt list.

So they have people who, for whatever reasons in the past, have shown themselves not capable of successfully taking care of

of a patent.

So the do not adopt list, it's name a person, maybe an address, phone number.

And so if you think about that business process, so to speak, the business process is making sure that a person that comes in is not on that do not adopt list.

Fuzzy matching is like a hard software problem to solve normally.

Like is this, is Tom Thomas and Thomas Tom, like, are these the same person?

That's hard to solve.

And AI does a really good job at it.