A Product Market Fit Show | Startup Podcast for Founders
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.
00:13:58 · Andrew Antos
[00:13:58] S1: But I think both my co founder and I, we're extremely ambitious and so we were just like, this is clearly not what we want to do. And so they were like, we need to change who we're selling into, right? And so we looked around and we're like, OK, we have some tech that works well in terms of understanding documents. Which was a scarce resource, looking at 2019, 2020. So we have that, we have expertise in NLP, which also was a scarce resource and so we were like, who else has problems that are related to documents? And we sliced it by industries, and then we sliced it by functions. So we developed these hypotheses, we put all these sticky notes up and basically what came out of it is like we want to be function specific rather than industry specific. Because we don't have any unfair advantage in a specific industry. It's not like one of us has been in banking for twenty years and have a great network and we can just easily sell to banks. And so we said like, OK, we don't have that kind of an unfair advantage nor we're in a place that has that. So we looked at, OK, what are the functions? And so within functions, we basically realized, OK, the people that have the biggest problems with documents other than lawyers are finance teams, right? Because they're looking at the same documents, contracts, orders, POs, invoices, expense receipts. Everything is just some kind of documentation of a transaction that happened and all these finance teams needed to account for those transactions properly. And so we were like, OK, let's focus on finance teams. Let's do a research, and initial research was like, yes, you have teams that are reviewing contracts for revenue treatment. You have teams that are doing order management to book and build deals. You have teams that are doing the expensing, the expense receipts. You have AP teams that are doing invoicing. So there's a lot and those teams are pretty big. And so we were like, OK, there's a logic here. And so what we've done, we were like, OK, let's build a product. Let's spend three months on this. Let's build something on top of the technology that we already have, launch it and then we said in six months, we need to be $250k ARR. So this is 2020, so very different numbers than what you see in the market today.