Alan

You would never be able to tell there's a fire.

But with that, the algorithm can say, oh, there's something here.

And when you see the video play out, yeah, there's a tiny signal of smoke over there.

And the interesting part is, how do you differentiate smoke from fog or from somebody doing a barbecue?

There's a lot of challenging aspects that come with developing these models.

You start to generate some false positives.

And if your system generates too many false positives, then nobody will pay attention to that.

So you have to kind of balance it out in a way that it's actually useful.

But it can be really accurate.

And these companies expanding internationally, they're having great success.

Nice.

I'm not sure.

We developed the initial version of that solution and then left them to continue.

Because that's something that we do.

It's not just about building the solution and staying there forever.

It's like we can do what's called project delivery with knowledge transfer.

So we will train their own teams to continue developing the solution through the years.

That's a great question.

I think the fire one is particularly tricky because it's like the data collection effort that needs to undergo of that in order to have a nice ratio of false positive that is manageable is really tricky.

There's like...