Outset launches AI digital twins grounded in individual customers, tripling revenue and team since last appearance

Speaker 2: Just do it.

Speaker 3: Do it.

Speaker 2: Up next, we have Aaron Cannon from Outset launching AI digital twins grounded in individual customers. Welcome back to the show. How are

Speaker 1: you doing? Good to

Speaker 3: see you again.

Speaker 10: I'm good. Third time on. I'm feeling special.

Speaker 2: Love it. Three, Pete. Catch us up to speed. What have we missed? What's the latest and greatest in your world?

Speaker 10: Things are great. Yeah. So we're an AI research platform, you guys probably remember. We've, since we last chatted, I think tripled revenue, tripled the team size, and now launched a very cool you. I was hoping to get at least a Gong or the

Speaker 2: We have plenty.

Speaker 10: Yeah. And now we just launched Digital So we built We we built a whole simulations lab and have now built Yeah. I'll tell you all about it.

Speaker 3: Yeah. And so you came out of YC. You were sick. Yes. You're always getting told talk to your customers, talk to your customers. You're absolutely sick of that. You don't wanna ever have to do a customer call again until you built digital twins. Is that right?

Speaker 10: It's exactly right. Yes. I I my I started my career as a researcher and I was like, I you know, I don't want to do that anymore.

Speaker 2: Okay.

Speaker 10: But but in all seriousness

Speaker 8: Yeah. Yeah.

Speaker 2: Sell me a digital twin because I've run companies and e commerce companies and it's useful to have a profile of certain customer cohorts and certain cuss but that aggregation takes a lot of the work out of it and I feel like there's a lot of platforms that that do that. It's sort of nice to talk to individual customers, but you might get a weirdo who's actually not very indicative. Sometimes the individual customers can give you some unique insight that But everyone else is walk me through the benefits of having these digital twins that are grounded in individual customers.

Speaker 10: So here's the like, to take a step back, research the whole point of research is to try to predict the future.

Speaker 1: Sure. Like, you're trying to make

Speaker 10: some decision for your business, right? Yeah. And you're looking backwards at the data. Yep. And the idea is that you can now not only look backwards, but you can look forwards. You can actually start simulating, like, if I do this new marketing campaign, how would this customer start reacting? What are the things they would say?

Speaker 3: Yep.

Speaker 10: And we're very good at research. That's what we're doing. That's what we built our business around. But now what we can do is take that research and build out the simulation platform so that you can say, okay, I can actually see into the future of how this group would react to some very big business decision.

Speaker 2: Yep.

Speaker 10: And so we train them by by by training digital twins on individuals. So, it's like a one to one. Like, can go in, talk to a digital twin, and you know that's based on John, and like, that's got all of John's experience and like all this data about John, and it can it can talk on behalf of you, right? And so that's like the the mechanism that makes it super, super useful and also like trustworthy.

Speaker 2: Yeah. What what what incentive do you have to give people to do a long form AI moderated interview? I'm if I'm buying a pair of shoes, I don't know if I want to sit down for an hour with a with an AI to talk about the shoes and how they fit.

Speaker 10: I I I think the last run we did was these were actually like seventy five minutes, the last one, and I think we paid them $200.

Speaker 2: Okay. Yeah. That's reasonable.

Speaker 10: To be clear, it's worth more than the shoes. Right?

Speaker 2: Sure. Sure. And and if you do, is that one customer per one company? So your I mean, clients include Microsoft, Uber, Google, Coinbase, you got murderers row here. Are you saying like, we are going to interview this one person about this one Excel feature or this one Excel product? Or are you saying like we're going to interview you about how you use technology? Some of this might apply to Microsoft. Some of this might apply to Google.

Speaker 10: That's right. We developed our own grounding interview, so it's not just about their use of Excel. Got it. The grounding interview, it develops what we call persona core. It's like trying to get to like who you are, what are your values, what do you care Sure.

Speaker 1: Sure. Right? So we can do that as the

Speaker 10: core and then we layer on other sets of data. So maybe Microsoft has Yeah. You know, operational data we can layer on to make it much more powerful.

Speaker 2: They know all my Exactly. Favorite

Speaker 1: Exactly. So the idea is that like we have we have extracted, you know Mhmm.

Speaker 10: As much of the person as we can.

Speaker 2: Sure.

Speaker 10: And then what we do is we we go back to that person and refresh that data regularly.

Speaker 2: Yeah.

Speaker 10: Yeah. So we'll even have them grade their own twins' answers. Like, how do our twin do?

Speaker 2: Interesting.

Speaker 10: And they can

Speaker 2: tell you. Yeah.

Speaker 10: And then and now we have a reinforcement list.

Speaker 2: Yes. They're kind of RL.

Speaker 10: And so it's Exactly. Exactly. And so so that that's that's what makes these really useful. And then we also put confidence scores. So you'd be like, obviously not all of our probabilistic, you know, out your output here is going to be the same. So we can tell you which ones you should, like, really trust and which ones need a lot more human data to to kind of bring to it.

Speaker 3: Where are you seeing the most pull from the market? Like, what what kind of businesses Yeah. Theoretically, every business should care about trying to predict the future and forecast how different customer segments, but I'm sure some care more than others.

Speaker 1: The the biggest pull right now is that companies have really hard to reach audiences. Right?

Speaker 10: And so these are like, maybe there's, you know, maybe there's CRM buyers or there are, you know, kind of, some kind of somewhat obscure part of the population. And so the incremental value there is massive because they can't actually go out and do constant research with that group. So they have, like, very few signals today. So the incremental value is big enough that they're, like, adopting, I think, earlier and first while there's still, you know, more kind of questions in in in the market, but but I think that's I I think we'll continue to see a bunch of demand from like these specialized audiences that that companies are sharing.

Speaker 2: What's the what's the opposite? What what what is what's a company that's like drowning in customer feedback data? Is that like some

Speaker 1: Well, I'll give you a a

Speaker 3: kind of there there's there's basically certain business decisions and like strategy decisions that you would make that you don't need to forecast. Right? Like a business doesn't need to say like, oh, what if we made customer support wait times shorter? Right? Like, no one is sitting there being like, wish when I called my internet service provider that I wish it took an hour Yeah. Instead of thirty minutes. Right?

Speaker 10: Yeah. Or or maybe a good example, and this is not a real one yet in terms of simulation, so I'll share it as an example, but like Nestle does a lot of consumer research. Consumers are reasonably easy to go out and talk to. It's reasonably cheap to get more feedback, but if you were actually trying to do if you're at Nestle and you're trying to do research on retailers and what they think about your brand, That's actually a much harder group to go find and do research with. You're not drowning in feedback from your retailers. Sure. Right? And so that would be audience.

Speaker 2: And and Right. I'm sorry. Is is it the pitch when you when when you signed Nestle? Because I imagine that like AI is very impressive, but I don't know that I would trust an AI to tell me which milk chocolate tastes the best because like the taste data just isn't in the model at all yet. And for a lot of consumer research on Nestle products, would want to watch someone taste the chocolate and tell me what they thought of it.

Speaker 10: Yeah. Yeah. Well, so so when we, you know, initially pitched Nestle, it's actually about our, you know, AI research platform, which AIs are just doing the interviewing. You guys are asking the questions. Human data. Yeah. Right? And that's our core our core thing, and that's what we've had for the last couple of years. But simulations is an extension to that, and that's where we we start talking. It's a it's a the kind of the the best kind of two elements of the pitch. Number one is hard to reach audiences. Right? Not able to get to them. We'll build those twins of them. You can talk to them anytime at, you know, unlimited capacity.

Speaker 1: Yeah. Number two is

Speaker 10: we say that, like, anybody in your company can access them. It's like if your finance person's working on new pricing model, you you don't want to like give your finance person like the ability to go research with real consumers necessarily. But but now they can actually like, you know, test ideas, test new pricing and see how how things would adapt Yeah. You know, and and stimulate them.

Speaker 2: Yeah. It makes sense. Last question for me is, I mean, feels like the Digital Twin launch is like an expansion of the product portfolio and I'm interested in else the product expansion can go. I'm I'm I know that there are companies that they get a lot of feedback but they struggle to bridge the gap between the feedback that they're getting and then actual ideas to go and implement. And I'm wondering Yeah. How if you're going to wind up being more prescriptive in the results. Hey, okay. We did all this research and you really should change the color from red to green.

Speaker 10: Right. Right. Right. Well, look. So research is this thing where like humans go and decide to go run research. Yeah. Right? What we are building towards, and that's our original product, right? We're building towards is something where the model can go do research for you before you've even asked the question, right? And so, it is constantly trying to figure out the answers to the things that you don't even know matter Right? It's like, hey, there's a you know, your revenue's down in that sector. You don't even know that yet. We're gonna send agents to go talk to all the people in that in that part of the economy and, like, go figure out what's going on, talk to some simulations if we already have them built, and then go take action. Mhmm. And that that action is where I think where you like where we can ultimately have more impact than just collecting and synthesizing feedback.

Speaker 2: Makes sense.

Speaker 10: We should be able to like send specs to cognition and like

Speaker 1: let let let them go build it. Right?

Speaker 2: Yeah. No.

Speaker 10: Totally. And that's like really something that's happening.

Speaker 2: Yeah. I love it. Well, on the launch and thank you so much for coming on the show.

Speaker 3: Very cool.

Speaker 2: We're third guys. We'll talk to soon.

Speaker 3: Have a good one. Great stuff.

Speaker 2: Goodbye. Let me tell you about Figma. Agents meet the canvas. Your AI agents can now create and modify your Figma files with design system context. Thank you Tyler for kicking off some clapping.