Welcome to another bonus episode of the TechMean Right Home podcast, another portfolio profile

episode. As always, I'm your host, Brian McCullough. And hey, look at this. Our friend is back.

Hey, I'm back. Chris Messina is here. Hey, Chris. Hey, hello. It's been a while, but we're here because we are going to talk about a company that Chris

and I invested in through the Right Home AI Fund.

This company is Leap Labs, and we're speaking to Jessica Rumbolo, Jessica.

And Jugo Patel.

Hello, again. Hi there.

And you are the founders of Leap Labs.

So this is going to be a more getting into the tech and what you're actually doing.

This is more, this company is making it an advancement than, oh, we have a product.

So, but before we do that,

just tell us a little bit about Leap Labs, what you're attempting to do, and then let's get into the science of it. Yeah. So we are automating scientific discovery from data. There's

a lot of data in the world. Companies spend huge amounts of money gathering data,

doing R&D, but the outcomes from this process are like pretty uncertain, pretty noisy,

pretty path-dependent. There are lots of good reasons for this, which I'm kind of excited to

talk to you guys about. But what we're able to do, basically, is extract even complex, combinatorial,

nonlinear patterns from arbitrary data sets at incredible speed and scale. And we've made a bunch

of novel scientific discoveries doing this. Science being the key, and we're going to get into all this specifically, but just in a broad sense, you've even written about this online, is there a sense that the current models of ML and especially LLMs, they're not exactly perfectly designed for scientific research and

the like?

Yeah.

Yeah.

So there are a couple of major problems here.

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