A Product Market Fit Show | Startup Podcast for Founders
00:02:04 · Andrew Antos
[00:02:04] S1: Yeah, so we used to run this business that was basically like a mini Palantir for complex financial workflows in enterprise and so we were running the business. And one of the core ways how we would grow the business is we would do two big conferences a year. Where we would invite a lot of our customers to speak on stage and a lot of existing, and potential customers. And so we have this event, it's almost five hundred people. And we launch all this cool new stuff for that core business. And as a part of it, we also launched a thing that was an internal tool before. It was an internal tool that basically solved a problem around implementing AI for complex processes in enterprise. Where we would map out really easily, you know, work and just observing the work or uploading videos and things like that. And it was just this little tiny thing because our hypothesis was, hey, we can accelerate our implementation. It can be something where customers might even find new processes for us to automate for them and so we launched all this super complicated, cool stuff that we've been working on for a year. And everybody's like, OK, this is cool. But everybody wants to talk about that one little thing that we launched on the side. Every single person, every single discussion was like, oh, yeah, it's cool, all of these complicated things, it's really cool but that little thing that you're calling the architect is really, really, really what I want. How can I get my hands on that? And so it was this really important qualitative signal super early on. Because people were just interested in talking to you about it.
00:03:38 · Pablo Srugo, Andrew Antos
[00:03:38] S0: And did you launch it as a paid product? [00:03:40] S1: Not really. We didn't launch it as a product, we launched it as a widget kind of on the side. But people were proactively asking us how much does it cost? How can I buy it? How do you license and so on. And so, we were coming up with pricing and structure on the spot. The first twelve or fourteen deals that we signed in the first six weeks after we launched that thing. Every single one had a completely different pricing model because we were iterating through it, through those conversations. It took a while to clean up. [00:04:12] S0: A lot of times, you know, you launch a very sexy new product and you get a lot of hype, and attention but it doesn't turn into revenue. How did that translate into actual people saying, you know, I actually want to buy this, I'm buying this, swipe the credit card sort of thing?
00:04:12 · Pablo Srugo, Andrew Antos
[00:04:12] S0: A lot of times, you know, you launch a very sexy new product and you get a lot of hype, and attention but it doesn't turn into revenue. How did that translate into actual people saying, you know, I actually want to buy this, I'm buying this, swipe the credit card sort of thing? [00:04:26] S1: So what we've learned from the previous times is people oftentimes get excited but sometimes it's too complicated to buy, right? Early, and so, what we've done is we basically did these early specials and we said, hey, if you really want to move forward. And you can move forward by the end of October, we're going to give you really unique pricing. You have lots of volume basically for a very small price, to get a signal, right? Like, do people truly mean it and the signal is overwhelming, right? So we took all the complexity of negotiating price and trying to sign big deals out of it. And we just said, this is the deal if you want to sign by the end of October. And people did, and so I think that gave us a lot of signal. [00:05:09] S0: How fast did it grow? [00:05:10] S1: We did like $350k or so, maybe even a little bit more of ARR in the first. Not even six weeks, like five weeks on this thing and then we backed it up. The next quarter we did like over a million, you know, then we did some experimentation, and then it went through there.
00:05:10 · Andrew Antos, Pablo Srugo
[00:05:10] S1: We did like $350k or so, maybe even a little bit more of ARR in the first. Not even six weeks, like five weeks on this thing and then we backed it up. The next quarter we did like over a million, you know, then we did some experimentation, and then it went through there. [00:05:27] S0: This was probably what? Two years ago, so 2024, but you've been at this since like mid 2017. Give me a little bit of background on just what you were doing before you started this business and then just the origin story on kind of the V1 idea. [00:05:42] S1: It was interesting. So I'm originally from Europe. The only real job that I had before starting the company was I was a corporate attorney for a couple of years and I came to the US to do grad school. And I cross registered for a startup course at MIT as part of it. And that's how I met my co founder Nischal and we met in this entrepreneurship class at MIT. We immediately hit it off. We walked around Cambridge for three hours after that and we both had this feeling that AI is happening, and it's happening without us. [00:06:13] S0: Even back then, in 2017?
00:06:13 · Pablo Srugo, Andrew Antos
[00:06:13] S0: Even back then, in 2017? [00:06:14] S1: Yeah, because there was a lot of this behavior of the super early people in AI. So professors would have sign-ups for machine learning courses at 07:00am or 07:30am in the morning and there would be students in sleeping bags sleeping overnight. In front of the rooms to sign up for those machine learning courses, to get in and so we were like, if there's this much interest and this much demand in AI. Then it must mean that it's happening everywhere, right? And so we had this feeling like, oh, it's happening right now, and so we felt like we need to start something. [00:06:49] S0: It's interesting, by the way. That that would have been how you noticed AI. I mean, AI has been talked about for a long time. We invested in AI companies back in mid 2010s, but the deployments were still not that interesting, that compelling for the most part. But in universities, it would have been an interesting place to see kind of the future just by the nature of how much interest there was in the potential of it, at least.
00:06:49 · Pablo Srugo, Andrew Antos
[00:06:49] S0: It's interesting, by the way. That that would have been how you noticed AI. I mean, AI has been talked about for a long time. We invested in AI companies back in mid 2010s, but the deployments were still not that interesting, that compelling for the most part. But in universities, it would have been an interesting place to see kind of the future just by the nature of how much interest there was in the potential of it, at least. [00:07:15] S1: Yeah, and I think that's actually. I mean, this is a Product Market Fit Show, right? So I actually think that's a very important concept for finding product market fit, is look at what people on the fringes are doing. Because oftentimes what starts as a fringe activity or as a fringe interest then becomes mainstream, right? We've seen there was a couple of companies that we worked with in the very early days in the 2018, 2019, time frame and they had AI CoE. Centers of excellence, already, right? These were big companies. Were they doing a lot? No, it was really hard to do it but they were already thinking about it as well and so you can see these fringe ideas. And oftentimes these fringe things over time become the mainstream, and you want to be as early as possible in the fringe idea. [00:08:04] S0: So taking back, I took you off the timeline.