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?
00:19:43 · Sagar Kadakia
[00:19:43] S0: Uh uh yes to the latter. And so any type of like uh like written context, uh transcripts, audio recordings, meeting notes, um all of that can now be uploaded right in and will ultimately um uh uh change how the moderator asks questions because the context window and the context that it has is now different. And so uh this was something that uh it took time to build because you know, things like you know, I'll use something uh very specific to funds here, like there's like a really recency curve that matters, right? Uh, which is you want to make sure you're asking uh or pulling from information that's very recent, not from six months ago or 12 months ago, because that's gonna have less relevance here. Um, and so uh yeah, like that's kind of what we wanted to build was uh an interviewer that kind of again, I mentioned earlier, like that kind of like thinks and feels and acts like you do. That has like a lot of the same knowledge that you do, right? But what's amazing about AI, obviously, is the ability for it to tap into that data in virtually real time, right? And and ask the best follow-up, right? And that's something that you know, as a human, you can't do that on every single call, right? Uh, it's just not possible. Uh, and and that's I think uh as we kind of get into now the benefits of and the superiority of AI interviewing is you know, no human could do 150 calls in a week, right? 10 checks across 15 companies, but this can, and it will be as good on the first call as it is on on the last call.
00:21:14 · Brett Caughran
[00:21:14] S1: Can we just walk through like a a worked example or an articulated example? Like say I'm looking at Nike, uh, just to pick a random name, and I'm trying to understand the curve of market share gains uh in in in their various end markets. How do I work with quality? Like, how does that so like what's the A to Z? What's the what's the system, the scheduling, the recruiting, the execution? Yeah, like what's that what's that experience look like?
00:21:41 · Sagar Kadakia
[00:21:41] S0: Yeah, I think um, you know what we wanted to do at Qualitate uh was ultimately uh replace what is a service with a product. And so um the way it's done in the qualitate world or the qualitate application is just through natural language uh prompting. Um, and letting the agent design a lot of that, right? From hey, here's who you described you want to reach out to. Is this accurate? Um, uh, so like a panel perspective, or hey, here's the questions that I've built based on the objective uh that you have. And here are the folks ultimately that we're gonna reach out to and the cadence that we're gonna reach out to them. So it's very much done in a natural language way, um, which to me is very product-oriented, right? Because today the the way the world works is very services, right? You would email someone at a consulting firm or an expert network, you know. I bet the longest email chains you have are with folks from those companies because you're going back and forth on is this the right expert? Is this the right questions? Here's a bunch of pre-qualls, uh, the the first interview is done. Well, now I want to change all my questions. It's it's it's it's a services business, right? And the the approach that Qualitate took was hey, if you want to understand this information around Nike's market share or momentum or the competitive landscape, um, rather than working with a human over dozens of emails and drafting the questions and finding all the experts, our system and our agents can ultimately do it. So we have things like a survey design agent, right, that designs the survey for you, right, based on all the objectives you have. We have things like um sourcing and verification agents, right, that identify uh uh the right people based on the requirements that you provided, right? That authenticate these individuals, right, from an expertise perspective. And so that's how it works in the qualitate world. That's how you would live, what you said, you literally would just in natural language come in and say, hey, here's the project that I want to create. I want to do XYZ, I want to understand whether Nike is gaining or losing share versus it competitors. I'm trying to understand whether their new products are resonating or XYZ. Build that whole project for me. Uh, and then here's like a whole bunch of other questions that I have.
00:23:56 · Sagar Kadakia, Brett Caughran
And in less than 60 seconds, all of these agents uh that I described will kick off. They'll build a panel for you, they will uh build the entire uh uh survey for you or interview for you. You'll review everything, you'll hit launch, the agents will begin the outreach process. The interviewer will then have those discussions with them, and then you'll get all that data back in a few business days, and uh it will all be analyzed for you. So you'll have summaries, you can ask natural language questions against it, you'll have like executive briefings and visuals, all those things that you know you would get with like a very expensive consulting engagement. It's all there, it's all for you, it's all real time. And so that's what like the qualitative experience looks like. It's a product, not a service. [00:24:44] S1: Interesting. And um the the the can the experts just log in and start answering the questions anytime, or is it classic, you know, Zoom scheduling, et cetera? Like how does that how does that process work?