00:44:05 · Sagar Kadakia
[00:44:05] S0: Yeah. So that's the that's the first product that we built, which was um qualitate was just spending its own money of millions of dollars going out and proactively interviewing folks, right? The interviewer was asking its own questions, right? It was asking qualitate questions, right? So it was going out there and just capturing. So that product still exists. Uh uh the moderator or the interviewer does thousands of calls uh uh on behalf of Qualitate, not behalf of a client, right? And so there's this massive library that's being generated, right? Uh, which will shortly be the fastest growing library in the world, right? It's just we're constantly going out there. The AI interviewer is constantly absorbing new information. It's using that information on the next call, it's compounding knowledge, right? That is the first product that we built. And then, of course, all of our clients were like, hey, I want to send that moderator out with my own questions. And then, of course, uh, that was the second product that got launched here at Qualitate, right? So now our clients can come in and tell the moderator, hey, go out and do 15 more calls for me. You have already, you know, done a couple hundred calls on this name. And so um, we do both. And so our library is unique and the only library which is totally objective because it's not investor-led calls, right? When you think about all the libraries out there, it they're all disparate call libraries or just flat files that you can search through, right? And frankly, they're not objective because investors have a hypothesis that they're trying to validate and they take someone down a path, right? And so what do you have? You have a bunch of like unobjective transcripts. This is why quality exists today. This is why hedge funds are using the product today, because you have libraries that are not objective, right? That are just an amalgamation of a bunch of like calls, right? One's a hedge fund, one's a VC, one's a corp dev. They're all asking different questions. There's no concepts of longitudinal analysis. You can search, but you cannot query. Qualitate allows you to query. It's quote unquote, one person doing thousands of calls every week, every month, right? It's the one interviewer. It gets smarter on every call. It asks a lot of the same questions.
00:46:09 · Sagar Kadakia
So you can do a lot of structured analysis, you can query our transcripts. It's the first uh platform, to my knowledge, where you can do longitudinal analysis on transcripts, where you can quantify transcripts. And so uh it was a huge unlock when we built this product because it was the first time where you could go through thousands of transcripts um numerically, right? Which is uh not something that you can currently do on with other providers. And so those are among the other products that we sell, um two of the reasons that people use qualitate. One, because all the information we already have, uh, and then of course, uh the fact that when clients launch their own interviews, those stay with them, and that's their alpha. It never goes back into the core library. We don't need to. The AI interview is doing as a lot of interviewing itself.
00:46:55 · Brett Caughran
[00:46:55] S1: I'll uh I'll tell you a quick anecdote. Um, when I sort of logic through where this all may go for um sort of price discovery markets, in 2012, I started looking at hospital stocks as potential beneficiaries to the Affordable Care Act. And the first five expert network calls you did, the sort of five hospital CFOs they put in front of people, all said the Affordable Care Act is going to be a disaster for hospital profitability. And so many investors did that. And then you sort of unpack it, and these five experts, by their disposition or by the counties and states where they were at, their existing payment structures, and it was bad for them. But the publicly traded hospital stocks that had more exposure to Texas and Florida and other markets, it was actually a huge tailwind. So there's actually that sort of observation set the bar low, right? And you had to go a few more layers deeper to really sort of see through that market, that market, um, the sort of consensus almost set by those. So it almost feels like that will that will speed up. And I agree there's sort of different duration lenses. I'm with you on that. But there are certain elements, like when a when a new drug launches, that's a part B drug where the data is very lagged, you know, the best, you know, what the best investors are sort of calling these KOLs aggressively. And now, if you know, 10x number of investors can spin up that sort of survey calling agent, it does feel like almost the alternative data prior where alpha became beta, and you you start to see prices, not in all instances for sure, because there's many unknowable questions. Um, what software companies will sort of benefit or get hit by AI? Like that's not like that's more unknowable, but how is this drug launching in real time? Like that's something that I think could be captured and priced in more of a now casting. Now that's good for you because like more demand, like Tom Ellenick always said it's better more you know, more beneficial to sell beta than alpha. But um uh just an observation. I'd be curious, curious your thought on that.
00:49:04 · Sagar Kadakia
[00:49:04] S0: Yeah, I think that's a it's that so that's that's a great observation. And so uh one lens to look at qualitate is like, hey, you guys are taking something that was very costly and complex to do, and that itself generated alpha for lots of people uh because the information was very hard to get and and and and and opaque. Um and now qualitate is enabling uh everyone to get primary research very, very quickly at a fraction of the cost. And so then what it actually becomes about is how fast can you generate the right questions and how fast can you launch uh uh that uh that study, right? Um and I think you have to have the right architecture in place. So remember, I was I was I was talking about that uh the channel checks process earlier. How are you gonna compete with a firm that's using a tool like Qualitate where it's doing all 150 channel checks in a week or two? You can't because that firm has built the systems, they develop the questions, the panels, the AI moderator has persistent memory and it's doing it all for you very quickly. And so, yes, if every firm does it, well, guess what? Now it's gonna be about hey, how do we get agents to do it? Because agents can move faster than humans on UI. And so it's all about who is gonna develop the architecture first to get primary research fastest. And that is now the cycle that we are moving into. That's what I'm seeing uh as we work with funds. Uh, I'm hearing lots of architectures that I knew it was going to happen uh because I wouldn't have started qualitate without the vision or the idea that it would eventually come to this, but this is what it is. I mean, we have people running 50, 60, 70 calls in like two weeks. I just it's I mean, this is how people are using qualitate today. And it's because if you want to be first to market and you want alpha, you can't do all the calls yourself. And also, if you want to go into these opaque markets where it's hard to find experts, it takes time to schedule calls. Well, guess what? The AI interview is gonna do it way faster than you can. Um, and the quality of the AI interviewing is obviously very good. It's it's it's it's why Insight Partners just backed our Series A. They uh during the investment process, obviously uh put a bunch of companies they wanted to capture information on in qualitate.
00:51:30 · Sagar Kadakia, Brett Caughran
They ran those same companies uh across all the other networks, and and guess what happened? Uh we obviously got that information uh uh faster, higher ends, higher quality, right? And so this is uh where the world is going. Is if you want alpha, you have to have the systems and the infrastructure to launch primary research very quickly, get that data back very quickly, which qualitate does, and then understand what that information means very quickly, and then make an action on it. That's where the alpha is going to be. And in six to 12 months, if the majority of your checks are being done through humans, you know, uh there will absolutely be no alpha there, right? Uh, because by the time you finish those checks, the market will have already moved. [00:52:17] S1: And so I wonder, you know, it's like in with with Yipit, for example, there was a sort of first order chapter, and now it's a second order game where it's like Yipit says the data's good, top line's good, but it's because they're discounting, so they're gonna beat on top line and miss on gross margins, and everyone's positioned long, so it's actually a short, right? Yeah, like I wonder if there will be a counter, that will be a good problem for a qualitate because that means you sort of reach market adoption. But I wonder if there'll be a counterpunch sort of dynamic where hedge funds are thinking about qualitate more as setting the bar and then having to find some differential view of why that bar has some fallacy embedded.
00:52:17 · Brett Caughran, Sagar Kadakia
[00:52:17] S1: And so I wonder, you know, it's like in with with Yipit, for example, there was a sort of first order chapter, and now it's a second order game where it's like Yipit says the data's good, top line's good, but it's because they're discounting, so they're gonna beat on top line and miss on gross margins, and everyone's positioned long, so it's actually a short, right? Yeah, like I wonder if there will be a counter, that will be a good problem for a qualitate because that means you sort of reach market adoption. But I wonder if there'll be a counterpunch sort of dynamic where hedge funds are thinking about qualitate more as setting the bar and then having to find some differential view of why that bar has some fallacy embedded. [00:52:58] S0: I I think uh, and Yipit's a great business. I think uh the difference with Yipit is Yipit is presenting the same information to everyone. [00:53:05] S1: Yeah, right.