A Product Market Fit Show | Startup Podcast for Founders
00:08:04 · Pablo Srugo, Andrew Antos
[00:08:04] S0: So taking back, I took you off the timeline. [00:08:07] S1: No, so we're there, right? We're like, we need to start something and so the only thing that I knew was the legal world, right? Because I was an M&A attorney and so the first iteration of the company was, OK, what if we can automate contract review for law firms and in house legal teams? And so, we launched that and we built kind of a product. It was like LSTMs and CRFs. It was very early AI days. It didn't generalize very well, but we were able to sign some really big customers, right? We had Salesforce was an early customer on that, a couple of other really big companies and it reached some level of success. But not a huge level of success because the market just, people were not very interested and the product was really janky, right? Because AI just wasn't generalizing very well. So it would be a complicated implementation for a fairly small problem, right? Obviously, ten years later, now it's working. [00:09:02] S0: Timing is everything, crazy. [00:09:04] S1: Timing is everything and so we went through that period. That was the first couple of years of the company. We're just iterating, also learning a lot about how do you find product market fit and also developing our skill sets.
00:09:04 · Andrew Antos, Pablo Srugo
[00:09:04] S1: Timing is everything and so we went through that period. That was the first couple of years of the company. We're just iterating, also learning a lot about how do you find product market fit and also developing our skill sets. [00:09:14] S0: I'm curious, because obviously all these phases are, they're not super relevant today but they're instructive. What was V1 of that product? What were you able to do? I'll give you an example. We actually were investors in a company called Blue J, and they do like tax research. Answers for tax accountants, for example, and they've been around since like the mid 2010s. For a long time it was a struggle because they were able to answer a small subset of questions and so it never became this product that the customers could rely on. When GPT came out and they kind of went on it. It worked for everything and they got that growth that they always expected to have. And now it's been, because it just works flawlessly across the board. I'm curious kind of what your experience was back then.
00:09:53 · Andrew Antos, Pablo Srugo
[00:09:53] S1: That's exactly the same concept. So the first thing that we launched was an NDA review tool. So the problem that a lot of big companies have is you're doing business, your customer sends you an NDA to even have conversations and you have to have an expensive lawyer review that. And so, what we would do is, let's say that you're using the product. We didn't even have an application that you would log into, it was just an email. Dedicated email ID that we would give you, so it would be like nda.productmarketfitshow.com, for example. And so, every single time a customer would send you an NDA, you would just forward it to that email address. It would hit our service, it would get reviewed, it would get marked up, and you would get an email back three minutes to fifteen minutes with a reviewed and marked up NDA, right? But to your point, it didn't generalize well. Doing it with something that actually matters like an MSA or DPA or something like that was extremely, extremely complicated. It was rules on top of rules because it didn't generalize well, the accuracy went down. [00:10:55] S0: And you were selling to companies or law firms? [00:10:57] S1: We started with law firms and made zero progress. And so we were like, OK, let's take the same product and start selling it to in house legal departments. Because they're not billing by the hour. [00:11:07] S0: Well, this is another thing that changed dramatically is law firms were like laggards. Because of the billing issue. [00:11:12] S1: Yes.
00:11:12 · Andrew Antos, Pablo Srugo
[00:11:12] S1: Yes. [00:11:13] S0: And that conflict. And the latest wave, they've just seemed to say, it's kind of you have to do it. Because everybody's doing it and that's unlocked a lot of spend. [00:11:21] S1: That's exactly right, right? And I mean, they're also in the direct path of AI, right? But anyway, so that was the first iteration. So then we went mostly to in house legal departments and again, it wasn't OK, but it was basically a lifestyle business, right? It just didn't grow fast enough because the problem is not big enough for a sufficient number of people and so that naturally caps your growth, right? And that's how we really understood the dynamics of a market, right? You need to be number one problem for lots of very senior people. If you're selling into enterprise, so you can grow fast enough, so you have high enough deal size and you have lots of deal velocity. [00:12:01] S0: How long did this last, this first iteration? [00:12:03] S1: Two and a half years.
00:12:03 · Andrew Antos, Pablo Srugo
[00:12:03] S1: Two and a half years. [00:12:04] S0: So this is where I'd really like to go deep on the story. Because it's incredible the number of founders that, this is like one of the most common problems. I mean, you put something out and it doesn't work at all. It's very clear, and at some point you're gonna give it up or you're gonna try something else. If it just takes off, amazing, good for you, right? But being in this situation where you've got something that kind of works, just enough to keep you going but really not enough to make it worth your while is such a common pitfall. What do you do, like two and a half years in. When do you start to realize that it's an issue and what sort of actions do you start taking to change, you know, pretty dramatically, I assume?
00:12:44 · Andrew Antos, Pablo Srugo
[00:12:44] S1: Yeah, so we basically just realized that this is a problem and we always had this view of, we just need to run a bunch of experiments, right? To basically get feedback from the market, right? There's no other way to do this. You cannot hypothesize your way out of this problem. So we would always come up with new ideas. I think what we realized after two years, it's not just a product problem, right? Because we were doing everything that we could with the technology at that point. But it was also a market problem. People just didn't want this at a high enough rate, right? It wasn't that thing, and most of our discussions with lawyers were like, hey, we're doing AI for law. And they're like, why would I do that? Why should I care? That's not a thing and so, we realized we need to get out of that buyer, right? That was kind of a critical thing. [00:13:33] S0: And you're where, by the way? ARR wise? You're doing half a million, a million ARR or less? [00:13:36] S1: Yeah, close to a million. Like, maybe $800k. [00:13:39] S0: OK, so you've got something that's worth defending sort of thing but not enough. [00:13:43] S1: Yeah, and I think it's also the size of our ambition, right? I think for a lot of people, they would just call it like, oh, this is a good lifestyle business. You know, next year, I'm going to be at a million and the year after that, I'm going to be at $1.3 and the year after that, I'm going to be at $1.55. [00:13:57] S0: Correct.