00:19:04 · George Sivulka
[00:19:04] S0: It's scientific, and it's it's actually an empowering act if you're really, like, investing. And my favorite thing on the net new analysis sides are when people are looking over more data than they otherwise would have looked over and drawing a journey. They will go into a bunch of expert calls and pull out the fact that maybe the customers are unhappy with one specific feature. Maybe based on that, they'll go into a variety of like technical documentation and pull out like what's changing about that feature and notice that there's something in the supplier agreement that needs to also change and that is bottlenecking that. And they'll go on these journeys through the supplier agreements and through all the data public and private about a company and actually come up with theses or hypotheses that they otherwise could not have done. And the feeling of discovery is, I think, the best part of being an investor, and I think we now impart that with a variety of our use cases. But I want to make it very tangible in terms of the use cases. And I'll just give you two examples where I'll go into an asset manager and I'll say, hey, these are two things we know people do with Hevia. And the first is, hey, people are reviewing... Associates, analysts are reviewing marketing materials every single week, sometimes, like, you know, for entire days of the week, just going through opportunities, trying to figure out if something matches your investment criteria or not. And the most common outcome of reviewing a marketing material in private equity would be a SIM. In credit, it might be an offering memorandum, is you come out and you say it's not qualified. Doesn't meet our investing criteria. As you spend all this time researching a company, reading through this, like, thing that a banker really tried to obfuscate to, like, tell you that it's worth your it is. It's a beautiful opportunity. And the majority of a junior's life could end up just going and saying, hey. Not worth our time. Not worth our time. And the
00:20:51 · Alex Immerman
[00:20:51] S1: deals just die. You could focus on something that actually is gonna have legs.
00:20:55 · George Sivulka
[00:20:55] S0: And what Hebia does is the minute you get marketing materials, just a simple use case, put it into the platform. It'll compare it to every other opportunity you've looked at. It'll spread it out across your investing criteria and give you a go, no go. So something that would used to be the bane of your existence, it's literally meaningless labor. You're just pushing the bulls are up the hill, is is now instantaneously done. And you can spend more time, as you mentioned, on the deals that matter. So that's one use case screening. We can screen a 137% more opportunities. Like in a given period of time, which is like, you know, and you... It's with the same depth. Another use case maybe a little bit later in the diligence process. Every time there's documents or a lot of the same document, like a lot of credit agreements or a lot of expert network calls, you'll often find juniors or even VPs scouring and reading through every single one to understand and synthesize an insight across the entire thing. And with Hebia, in seconds, what used to take, you know, 200, 300, 400 pages of reading, you can instantaneously get the insights on a per call basis and then over every single call or on a per credit agreement basis and the entire capital structure of a company. And when people see that and they experience that, it's not only like an emotional experience. They just realize that they've spent twenty or thirty hours every single deal process doing something that can now be done fast, their eyes light up.
00:22:18 · Alex Immerman, George Sivulka
[00:22:18] S1: It's amazing. One of my favorite parts when I'm using Hevia is just leveraging the templates. And, of course, you know, I can customize them, and maybe there's some questions that I want to ask that not everyone else does. But if it's my first time doing an earnings call review using Hevia, I can do it very quickly out of the box, like time to value is [00:22:36] S0: high. [00:22:36] S1: Yes. [00:22:37] S0: There's a library over a thousand different templates. I think it's like now closer to 2,000 different templates, which are all effectively agents. One of them is a credit agreement agent. One of them is a earnings call agent. One of them is a, you know, expert network agent or a screening agent. And those are the use cases. And that's actually why it's so measurable. If last year was a year of everyone experimenting with AI and not knowing what to use it for, now we have like a screening template where we can measure the amount of time it takes you with Hebia and before Hebia, and prove to a financial services firm, this is tangible ROI. [00:23:11] S1: All these examples that we've gone through primarily on the buy side. I think you guys work with a bunch of advisers. What do their use cases look like?
00:23:19 · George Sivulka
[00:23:19] S0: I can't speak to individual customers, but in general, if you can go in and understand not only from the materials that a client gives you, but then also the entire world, the Internet of information, and are able to pitch their company better, create better marketing materials, that's a massive use case. There's also just pieces in a standard deal process when you're responding to the buy side and responding to a due diligence questionnaire. Being able to answer that, that's a perfect agentic workflow, but with checkpoint steps for every single piece. And we can automate really large part of that, which is traditionally one of the the largest time sucks of a, you know, junior or first year out of college investment banking analyst. And, you know, eventually, we're gonna put the logos on the slide in the right format, and they'll be perfectly centered and everything else. But a a lot of the most tedious parts of the job in banking, completely automated. And on law, people are trying to use things like our infinite effective context window to actually look over entire libraries of formerly negotiated agreements. And live during a negotiation actually come with better terms or a better understanding of what is market over previously, like, very obfuscated data where you'd have to be a partner with many years of experience to understand exactly, you know, what the right thing was to land on for client.
00:24:36 · Alex Immerman
[00:24:36] S1: So in 2023, certainly through much of 2024, AI was about experimentation. The board was fixated on what are you doing in AI? Today, we're in 2025, we've gotten to a place where it's kind of a given. We're assuming all of our companies are leveraging Gen AI in And some so the question is becoming, where is the value? What is the value? Is the return I'm seeing on all this AI investment I'm making? Yeah, you're right.