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?
00:26:52 · Sagar Kadakia
[00:26:52] S0: Absolutely, it's a problem. Uh um, you know, uh fraud is a huge problem um in in in this industry, um, especially with the advent of AI tools, right? Um, someone could read off Chat GPT, sorry, someone could read off of Claude, right? And so those are real concerns. Someone can fake their expertise, right? So, how do you verify and authenticate them? I think one of the biggest pieces of feedback that we get from clients today is just how much fraud there is in the industry, right? How many times do you start talking to someone and you're like, I know you're reading off of something, or like you're definitely not an expert, right? You have some peripheral knowledge. And so that's actually AI interviewing is actually probably the best form factor for removing fraud. And I'll tell you why. Uh, when the AI interviewer is interviewing the expert, um, the way all the follow-ups work is obviously we record all the audio data that the expert is saying, right? Uh we transcribe it, it goes into an LLM. Well, guess what happens when it goes into the LLM? We're checking on whether or not that content was AI generated, right? And so if someone is talking, it's actually very easy for our interviewer to know uh whether or not that person is using uh tools because there's no inflections in their voice, it sounds like they're reading. Um, a lot of AI tools have very um uh they don't sound like humans. They do things like they list reasons out, right? Like humans don't talk like, you know, the reason I'm spending more is because our users are increasing, our workloads are going up, and XYZ is halfway. That's how ChatGPT talks. That's not how humans talk. And so uh because we are recording the audio and analyzing the audio, we have the ability immediately to determine whether or not that is an authentic um uh uh um uh uh piece of audio. So that's piece one, and that's hard to do in real time if you're a human talking to an expert. It's actually quite easy to do when an AI interviewer is talking to the expert because we have the benefit of being able to analyze that data in in real time. And of course, if they use it, the engagement shuts off. So that's kind of piece one. And then piece two is um we've built a lot of uh safeguards on the back end. So once we capture all that audio, right, we have lots of checks.
00:29:14 · Sagar Kadakia, Brett Caughran
We we we still have humans in the loop that that's still very important right now, right? Where the most important thing is the quality of the data that we're passing back to this person is authentic. It's high, it's high quality. And so we have humans and models that are reviewing all this uh uh for those um against those, you know, uh, against those checks. And so that's how we kind of do it today to prevent to bet to prevent fraud. [00:29:42] S1: What about um MMPI? I mean, this has been sort of a massive issue in the hedge fund world with expert networks over the years, uh sort of for a period of time, a flu sort of a fluid and shifting regulatory environment. Um and one of the one of the um one of the sort of serious footfalls doing these calls is if you sort of uncover MMPI and then that stock gets put on the restricted list across a firm, uh it's the right thing to do, but it's sort of people are annoyed with you. How do you keep these AI systems from accessing information that would put a stock on the restricted list? Or even worse, sort of not flag that as MMPI, and then that firm transacts transacts on that piece of information, um, which is a real problem.