A Product Market Fit Show | Startup Podcast for Founders
00:20:51 · Pablo Srugo, Andrew Antos
[00:20:51] S0: And before that was probably $350k in a year or whatever it was, like for product one. [00:20:55] S1: Yeah, six months versus six weeks, which is a huge delta and then I think the other thing that we also found. Which people talk about less, is product market founder fit and what we realized is the business that we've had before. We loved the product, we loved the business, but there was a really big component of professional services and delivery, and implementation. We were trying to crack that problem every single day for a couple of years, and we were just really struggling with it. We were not good at it and then you sometimes saw people who are really great at professional services and they just blaze through it and they do it with elegance, and they do it with this lightness. And we were just struggling with it so much. And so the combination of, oh, there's this thing that is just growing so, so, so quickly. And the combination of we might not be the right people for this particular type of business. Basically made it very easy for us and said, OK, we need to focus the business on this new product. [00:21:58] S0: And where was the existing business, in '24? How big had it gotten at that point? Like $3, $4 million? [00:22:03] S1: Oh no, it was like $10. [00:22:05] S0: OK, so it's sizable. [00:22:07] S1: Yeah. [00:22:07] S0: What do you do when you see this new product take off? As much as it's maybe more interesting and more aligned with you, do you just pause the other one? Do you do both at the same time? How do you think about that?
00:22:19 · Andrew Antos
[00:22:19] S1: So it's yes and yes. So, initially we ran both at the same time and even though the people were buying the new product, right? Which is a transformation tool that allows you to understand how people work, turn it into a company brain, then allocate the work between people and agents, right? So ultimately that you can, it's incredible. Our customers are building these dream teams of people and agents with it now. But we had these two things next to each other, and all of this was not obvious yet. We just saw people are buying it and people are using it a lot. But like I said, you need a hundred customers to actually really understand the product market fit, the market speaks to and reveals itself. And you start seeing these patterns of value propositions and why people are buying, and how they're deploying. What value they're getting and how they're expanding. How they're renewing. You start seeing all of that and early on, we didn't see that. And so we always believed to just run four to five experiments every single quarter. Especially early on, to really figure out what's the shape of the product market fit. So we launched it, a bunch of people bought it. Then a bunch more bought it the next quarter and then we're like, OK, let's build a hypothesis, right? We had one hypothesis was this could be a compliance tool for internal audit teams and SOX compliance teams. One hypothesis was this could be a risk management tool for banks and especially places that have a lot of risk. We said this could be purely a post M&A integration tool, right? Where it's like, oh, I acquire a company, I need to merge the operations of two businesses. This could be an ERP transformation thing. So, for implementing ERPs or making changes to ERPs, or it could be an AI transformation thing. This helps you build agents, right? They're like, find where you should build agents, quantify the value, and then actually build them. And so, we built this hypothesis and we went to the market, and then basically saw what's happening, right? We got a bunch of these, a bunch of interest and so on. And then you start seeing how much are people using it. Are they loving the tool or liking the tool or not liking the tool? What's the average deal size in any of these hypotheses?
00:24:26 · Andrew Antos, Pablo Srugo
And you start really seeing, you get some amount of data, and then it's easier to say, OK, we're going to go in this direction. For us, it was the AI transformation direction, right? We found actually AI transformation and ERP transformation actually pretty close to each other in a lot of ways. But that was the thing that was working better than anything else. And so then you iterate. So then you say, OK, we're not going to focus on these, we're going to focus on AI transformation, ERP transformation. OK, and then you iterate again, and then you build more hypotheses. [00:24:59] S0: When do you decide, like have you completely abandoned the finance product? And if so, when do you decide to do that? [00:25:05] S1: So what we ended up doing actually is we always want to optimize for customers and the team, right? You always need to optimize for the customer. Because the customer keeps your business alive, and then you optimize for the team. Because the team supports the customer, right? Everything that we've always built, our philosophy is to be a customer first organization and so we saw the other business just skyrocketing, and it overtook it in about a year, right? [00:25:31] S0: Crazy, how fast did that grow? From zero to, was it zero to twenty in a year and a half or something like that? [00:25:37] S1: Yeah, close to it. [00:25:38] S0: Crazy, insane.
00:25:38 · Pablo Srugo, Andrew Antos
[00:25:38] S0: Crazy, insane. [00:25:40] S1: And so, we saw that. And so, we were like, well, how do we do right by our customers, right? Because these are people who believe in you. They give you money to solve their problems. You always need to optimize for the customer first. We were weighing these options, right? Should we be supporting this internally? Should we be doing something else? What we ended up finding is we have this incredible partner that was supporting some of our implementations already. We worked with them for a couple of years. It's a professional services firm and they were like, can we take over this business and manage it for the benefit of your customers? That was a win win because they were able to support our customers really well, they were able to understand the product, they were able to continue the operations without any interruption, as well as they were able to take care of the team, right? So they literally took every single person who was supporting the product and brought them over. And so we optimized for the customer, we optimized for the team, and so we effectively sold the business to the partner, and focused the entire company on this new incredible business.
00:26:47 · Pablo Srugo, Andrew Antos
[00:26:47] S0: You love this show. You don't want to miss the next episode. Why would you? So hit that follow button. Trust me, it's in your own best interest. So what I want to go deep on now is back to that product fit moment. You spoke a little bit here and there about what that product was, but I'm curious on the origin of that product. You mentioned you built it for yourself. I'm curious, what it was, how you used it and then. How you thought about and the way that you demoed it, and launched it. Because obviously that provoked a lot of interest. Just love to learn more about that. [00:27:15] S1: So we were implementing AI in practice, right? The way I describe the business is like a mini Palantir for complex financial processes. And so, we would have the same kind of motion where we would have FDs or consultants coming in and they would spend a lot of time trying to understand the process. How do you actually do work, right? [00:27:34] S0: This is for the finance teams that you were serving.
00:27:37 · Andrew Antos
[00:27:37] S1: Yeah, yeah and it's one interview after another, right? Imagine having ten Zoom calls a day or eight Zoom calls a day. Each an hour long, and just asking people, OK, can you show me how you do work? I was like, oh, you click on this in Salesforce and what does it do next? It's like, oh, and then you do this spreadsheet and then you do this pivot table, and then you send this Slack message, and then you do that. And these complicated business processes in enterprise run on exceptions. You have one happy path and that applies to twenty percent of things and then eighty percent is an exception of some kind. It's like, oh, if you see this customer, you need to do this or if you see this partner, this button doesn't work. You need to put it in a spreadsheet and do something else. That's how the reality is and so, it was always really painful and we would just be writing that down, documenting it and so on. And then we got some AI note takers, it was a little bit better. But what we've built was this little tool. Where you could take a Zoom recording with a customer that's showing you the actual thing and turn it into a business requirements document. Basically write up, this is how the process works, with screenshots, exactly what's happening, all these exceptions. Which would be a few hours of time of an FDE and we would just do that in two, three minutes. And it felt like magic to a lot of people. So that's how it started. So we solved the problem of how do you go from, I want to automate something in enterprise, find what I want to automate, how do I want to do it, and actually build the agent or set of agents to solve it.