So this issue of unstructured data, would love for you to illuminate for the audience how Clara is solving this issue, this crisis of unstructured data in institutional finance.

Yeah.

So we specifically focus on private markets.

Um, so private markets is really, uh, investment opportunities that are part of the institutional space, not disseminated necessarily broadly to every investor.

Usually an investment bank or, um, someone like that will be running the process of raising money for, this could be for a private company or venture capital or private equity, which are oftentimes the bigger asset class in this space.

And so any investors that are interested have to go through a process of signing a non-disclosure agreement, NDA, to be able to access the information that the company is providing, given that it is a private company.

And because of this, uh, process, that information transfer and discussion is very, very document heavy.

Would love to kind of understand, like, if there was a moment when you realized that these legacy systems, uh, were no longer fit for purpose, purpose being deal intelligence, and how does your approach sort of differ from the standard legacy tools that, uh, we see in the market today?

And if they aren't using you, what's the status quo? Just what you described sitting around a boardroom talking?

Yeah, I think and that's to your point on category defining or redefining and like the struggle that we have is a lot of people are using AI to help specific tasks be a little bit faster.

And with the hope that And maybe true that they will become a more faster investor.

They'll go through deals faster.

But that in of itself doesn't make you a better investor.

And I think a lot of our investors are interested in that.

But when it becomes a really unlock is that they can find a way to look at alpha and alpha or find signals that they currently don't have.

How do you think about finding that line between like those two very different messages?