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?
00:24:56 · Sagar Kadakia
[00:24:56] S0: You could take it anytime you want to. That's the beauty of of the AI interviewing, right? I think um when you look, the biggest unlock here was ultimately removing all of the scheduling, right? Um because when you're like the the concept of scheduling itself will just limit the amount of calls that can be done, right? Overall, right? If we look at the universe of calls being done, right, it's only as fast as people can schedule and then take those calls. Well, what happens when you've removed those two massive bottlenecks and now people can participate whenever they want to? There's no time zone barriers, right? Someone can take it at 2 p.m., 6 a.m. It doesn't matter, right? I don't have to try to connect with someone, you know, uh uh uh in Singapore or in India or in London or in California. The AI interviewer is always ready. It's fluent in lots of languages. And so once you start peeling back all those barriers, right, it makes it a lot easier for folks to ultimately participate. They can participate when they want to. They can participate in the language that they want to, right? In the comfort of their own home on their iPad or their laptop or their or their mobile phone. And so that's how uh we're able to work at such a speed, right? Because think about it from this perspective. If you are a traditional network, all the scheduling has to be done from nine to five. Great. There's there's there's no concept of that. I feel like half the calls we do are before work, after work, Friday at 5 p.m. on the weekends, right? People submit projects on Friday, they're done on Monday. Right? Traditional networks can't do that. And so now we've we've we've massively expanded, right, the the amount of time uh that someone can ultimately participate. And so that's that's what that looks like.
00:26:35 · Brett Caughran
[00:26:35] S1: That's interesting. How do you protect against agent to agent? So uh I'm an expert, you reach out, your agent reaches out to me. I spin up my I drop in all my contacts into a voice agent and I have my agent log in and talk to your agent. Or is that a problem?