00:09:24 · Sagar Kadakia
[00:09:24] S0: Yeah. That's a really good question. And and I'll kind of break it up into a couple different pieces, which is um this concept that the the person on the other side asking those questions still needs the confidence of talking to someone because they want to see the emotion and the inflections in that person's voice. And, you know, they're gonna make a very expensive decision, likely at the end of all these calls, right? And so if you're working in private equity or public equity or credit, or you're working uh for a sales or marketing or strategy team, um, you have to get it right, right? That's ultimately why people want to do these calls themselves, right? Because they feel that, hey, I have an edge in asking questions, I have an edge in reading someone's body language. And so I think um I think those are all uh uh accurate and will continue to be the case. Um, and so when I think about AI interviews uh today, and then I'll kind of hit on how I think about how we're bridging uh human and AI interviews. When I think about AI interviews today, a few things have ultimately happened um that move beyond this concept of like, hey, I need to be on the call. You are limited in time and resources, right? You can only do so many calls right a day or at a given week. Right. Um, and so from that perspective, if you want to open up the aperture of how many ideas you can go after, or you already have a thesis that you want to validate and you want to get lots of perspectives on that very, very quickly, this is where AI interviewing can come in. Right. Where maybe you do a few calls and then you give those calls to our AI interviewer. And now the AI interviewer has knowledge of what you've done. It knows how you like to ask questions, it knows how you think and act, and it has all of the context of those calls that you did. Great. Now let the AI interviewer do another 15, 20, 30 calls for you. Because you would have never been able to do that many calls, right? It's costly, it's it's it's it's complex to justify, right? How are you gonna fit all those calls into your day? Um, and so this is how I think about AI interviewing today, which is it allows you to build higher conviction on perhaps some of your human-led calls. Uh a lot of our clients now use it for idea generation, right?
00:11:51 · Sagar Kadakia
Where they can do 40, 50 calls in a week, uh, a week and a half, where they would have never done that before. And so now we're seeing primary research getting um used much earlier in the process. And so that is where I think the the role of AI interviewing plays today. And then what we are doing now is bridging human and AI interviewing, which is I think kind of more to the question that you asked, which is like, hey, how do I still get what I want? And I know what the benefits of AI interviewing are, the scale at which you can do it at, right? The cost savings, right? The quality, all of that stuff that you can now do. And so the way that we think about bridging those two is very much where now, uh, and I kind of alluded to it earlier, you do the first two calls and then give it to the AI interviewer. Or let the AI interviewer do the first four or five calls. And then when you decide to do the next human call, you'll have the AI interviewer with you. It'll suggest questions that you can ask right right on it. And so this is what we call bridging human and AI expertise. This is a uh it's it's an overused word, but it's it's very much like a co-pilot, right? Where you are splitting up the work with the AI interviewing. It can kick off the work for you, it can do some of the work in the middle, you can get very high ends, you can take it with you on all the phone calls you're doing. So let's say you're doing a phone call with an expert. Great, that interviewer has already done 15, 20 calls with similar experts. It can guide you and say, hey, here's what I recommend you ask. You can ask your question or you can ask what the AI interviewer is ultimately suggesting. And so um over the next six to 12 months, I don't see a world where um people want less of kind of what you described. I think more so it's now I can use AI to significantly improve my process. I can still have human components through that process, but ultimately my conviction is going to go up because I can do literally a 10 to 20 XN compared to what I used to. I can do way more idea generation than I used to, right? Idea generation is something net new that we have unlocked in this space because it was too complex and costly to justify to do five to 10 expert calls on every single idea. Now you can do it with quality.
00:14:18 · Sagar Kadakia, Brett Caughran
And so that's how I think about uh uh this concept of human calls and human moderation is it's very much going to uh still be a part of the process. Um, but quality, it's kind of bridging those two together. Uh, and I think it makes for an overall better, better, better, better end-to-end process. Yeah. [00:14:38] S1: I guess it's sort of our archetypal example on the tracking side too, was um, you know, for Avis and Hertz were public companies, enterprise was a private company. And so every, you know, every, you know, uh, every analyst who covered the rental cars would check in with Enterprise, you know, once once a month or twice a month to get an update on pricing. And yeah, so I would assume just for a broader net for tracking, hey, is anything changing? Um one question I have in that, like, how do you how do you ingest the baseline? Because like, how do you protect my context? Like, if I have a thesis on Avis and I want to check in with Hertz, like or check in with enterprise, it's really valuable to update my thesis, my prior calls, maybe calls with management. So the AI system can say, hey, Brett just met with Avis, CFO, two weeks ago at this conference, and the CFO said this. What do you think about that? How do you how do you build that loop while protecting that context from other clients? Because if that leaks out to other clients, then that's that's not optimal.
00:15:43 · Sagar Kadakia
[00:15:43] S0: Exactly. And so to answer your second question first, um uh all the context that our clients put into the platform, it it stays with them. Everything is walled off, right? So there's there's no training on client data, uh, there's no using client data for another interaction. We've built an AI interviewer or AI moderator that is unique to every single person, right? And so every person that we work with, every firm that we work with, right, that moderator has different context. It has our information and it has your information, and everything is really nicely kind of walled off. Um, you kind of brought up this concept of a like a what would be like a channel check as an example. And Qualitate has built the best version of that channel check. And I think this is like a great kind of use case to jump into, right? If if you're a fund. How do channel checks work? Um, every month or every quarter, your goal on a given company is to interview likely the same panel of folks with the same questions because you're ultimately trying to measure some type of demand momentum, pricing changes, new product adoption, whatever it may be. How does it work today? Uh, if you're a big firm, you probably have lots of folks calling. Uh uh, right? And and you could have 20, 30 people on a primary research team and they're doing all these calls, and that's how it works, say, very manual process. How does it work in the qualitate world? Uh, we've had a few dozen firms now move over substantial amounts of their channel checks business onto qualitate. So, how does that work? Um, you uh come on, you you pick a company, uh you just work with the agent to indicate here's all the questions uh that I have that I want to you know ask quarterly or monthly. Uh, here are uh the types of folks that I want to speak to, right? Whether it's cybersecurity experts or um you know folks in advanced manufacturing, whatever it may be, you'll build your panels, right? All through natural language, and then you will schedule that check. Uh and what will happen is every month, every three months, the AI interviewer, using all the context that you put in, will go out and do all of those checks for you. Now, here's where uh we've taken it to the next level, which is the persistent memory that the moderator has.
00:18:02 · Sagar Kadakia
So when it goes and does the second set of checks, it has knowledge of the first checks. That is now how we are. That's that's human-like. But what happens if the person on on your team leaves who's doing those checks? Well, great, now you put someone else in, they don't have prior memory, they're looking at call logs, all that stuff. The AI interviewer solves all of that. And so um persistent memory, um, context that is unique to that client, it's what makes the interview experience very unique to what you're doing. And then we have essentially been able to uh with agents and automation replicate the end-to-end workflow of that, right? We have some funds that are doing, I mean, across 15, 20 companies, you know, five to 10 checks per name, right? It's all being done through AI interviewing. And every set of checks gets better because it has the memory of the prior. And that's how you get great follow-ups, and great follow-up uh questions generate great answers from from from from those folks. And so this is an example that is one of our fastest growing use cases kind of within kind of like the you know, let's call it like the hedge fund space. Um and this is what we want to keep building on because AI interviewing or AI moderation is a point solution, but it's hey, how do you actually work that into the workflow of someone? And that's one example of how we have done it.
00:19:27 · S3
[00:19:27] S3: Sagar, in that example, can the analysts also feed in digital context to enhance the um the capabilities of the interviewer, the AI interviewer, like models or prior, you know, memos, things like that?