He sold his 8-figure business to bet on a side app—then grew it to 20x in a year. | with Andrew Antos, Co-Founder & CEO of Within

0:00How fast did it grow? We did like 350k or so, maybe even a little bit more of ARR in the first, not even six weeks, like five weeks. And then we backed it up the next quarter, we did over a million. Then we did some experimentation and then it went through there. You truly find the core of the product market fit once you sell 100 customers. It doesn't mean everybody's super happy and everybody's at a huge deal size. But around 100, you actually have 100 goes at it. and then the motion kind of reveals itself. My mindset has completely shifted. I think it's like nine out of 10 things don't work and they might look like they're working incrementally, but that's really like, it means that it's not working.

0:41But the one out of 10 things works so well that like carries everything else.

0:49Andrew Antos: That's product market fit. Product market fit. Product market fit. I call that the product market fit question. Product market fit. Product market fit. Product market fit. Product market fit. I mean, the name of the show is Product Market Fit. Do you think the Product Market Fit show has Product Market Fit? Because if you do, then there's something you just have to do. You have to take out your phone. You have to leave the show five stars. It lets us reach more founders and it lets us get better guests. Thank you.

1:16Andrew, welcome to the show, man. Hey, Pablo. Good to see you. Thanks so much for having me. No problem. I'm excited for this. You just went through a big rebrand. So it's an exciting time. The timing is impeccable. We just did a big rebrand yesterday to actually reflect the product market fit that we found in the last 18 or 20 months. And so I think this is a perfect timing. So, I mean, you'd mentioned to me that you actually found Product Market Fit three separate times through your journey with this company. We're going to talk about all of them individually, but let's talk about the last time that you found Product Market Fit, because that's the beginning of, you know, kind of the latest part of the journey and, you know, leads to the rebrand and all those things.

1:58What happened? Take me to that day. Like, tell me what happened and why was like Product Market Fit to you? Yeah, so we used to run this business that was basically like a mini volunteer for complex financial workflows in enterprise. And so we were running that business. And one of the core ways how we would grow that 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 500 people, and we launch all these like cool new things for that core business.

2:35And as a part of it, we also launched a thing that was like 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 like map out really easily, you know, work 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 like new processes for us to automate for them. And so we launched all this super complicated, like cool stuff that we've been working on for a year.

3:10And everybody's like, okay, this is cool. But everybody wants to talk about that one little thing that we launched on the side. Like every single person, every single discussion was like, oh yeah, it's cool. Like all of these complicated things. It's really cool. but that little thing that you're calling the architect is really really really like what I want like how can I get my hands on that and so it was like this like really important like qualitative signal super early on because people were just like interested in talking to you about it and did you launch it as a paid product not really we didn't launch it as a private launch just like a widget kind of like on the side but people were like proactively asking us like how How much does it cost?

3:49How can I buy it? How do you license? And so on. And so we were coming up with like pricing and structure on the spot. Like the first 12 or 14 deals that we signed in the first six weeks after we launched that thing, like every single one had like completely different pricing model because we were like iterating through it, like through those conversations. And it took a while to clean up. A lot of times, you know, you launch a very like 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.

4:24I'm buying this. So I do credit card sort of thing. So what we've learned from the previous times is people oftentimes get excited, but like sometimes it's too complicated to buy right early. and so what we've done is like we basically did these like 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 you know like really unique pricing like you have lots of volume basically for like a very small price to get a signal right like do people truly mean it and the signal was overwhelming right like 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.

5:06And so I think that gave us a lot of signal. How fast did it grow? 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 over a million. Then we did some experimentation and then it went through there. This was probably, what, two years ago, so 2024, but you've been at this since 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 like the V1 idea? It was interesting. So I'm originally from Europe.

5:44The 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 U.S. 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, Nestral. And we met in this entrepreneurship class at MIT. we immediately hit it off. Like we walked around Cambridge for three hours after that. And we both had this feeling that like AI is happening and it's happening without us. Even back then, like in 2017. Yeah, because there was a lot of this behavior of like the super early people in AI.

6:20So there would be professors would have like sign up for machine learning courses at like 7 or 7.30 a.m. in the morning. And there would be students like in sleeping bags, like 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 like it's happening everywhere. Right? And so we had this like feeling like, oh, it's happening right now. And so we felt like we need to start something. That's interesting, by the way, that that would have been how you noticed AI.

6:52Like it just, I mean, AI has been talked about for a long time and like 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. Yeah, and I think that's actually like, I mean, this is a product market fit show, right? So I actually think that's like a very important concept for finding product market fit is look at what people on the fringes are doing.

7:27because oftentimes what starts as a fringe activity or as a fringe interest then becomes mainstream. And we've seen, there was a couple of companies that we worked with in the very early days, in the 2018, 2019 timeframe, and they had AI COEs, Centers of Excellence already. And so 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 like fringe things over time like become the mainstream and you want to be as early as possible in the fringe idea. So taking back, I took you off the timeline.

8:07So 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 like, okay, 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 like very early AI days. It didn't generalize very well, but we were able to sign some really big customers, right? Like we had Salesforce was an early customer on that and, you know, a couple of other like really big companies.

8:42And 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 complicated implementation for a fairly small problem, right? Obviously, 10 years later, now it's working. Timing is everything, crazy. Timing is everything. And so we went through that period. There was the first couple of years of the company. We were just like iterating and also like learning a lot about how do you find product market fit and also like developing our skill sets.

9:14I'm curious, like, because obviously all these like phases are, they're not super relevant today, but they're instructive. Like, what was V1 of that product? Like, what were you able to do? You know, I'll give you an example. We actually were investors in a company called Blue Jay. And they do like tax research, like 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 like a small subset of questions. And so it never became this product that the customers could rely on. When GBT came out and they kind of went on it, it worked for everything.

9:46And 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 like back then. That's exactly the same concept. So the first thing that we launched was like an NDA review tool. So the problem that a lot of big companies have is like 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.

10:15It was just an email, dedicated email ID that we would give you. So it would be like NDA at productmarketfitshow.com, you know, 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 like you would get an email back in three minutes to 15 minutes with like a reviewed and marked up NDA, right? But to your point, it didn't generalize well. Like doing it with like something that actually matters, like an MSA or DPA or something like that. It was extremely, extremely complicated.

10:50It was rules on top of rules because it didn't generalize well. The accuracy went down. And you were selling to companies or law firms? We started with law firms and made zero progress. And so we were like, okay, let's take the same product and start selling it to in-house legal departments because they're not billing by the hour. Well, this is another thing that changed dramatically is law firms were like laggards because of the billing issue 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 that's that's exactly right right and i mean like that they're also like in the direct path of of ai right but anyway so so that was kind of like the the first iteration so then we went mostly to in-house legal departments and again like it wasn't okay but it was basically like a lifestyle business right like it just didn't grow fast enough because the problem is not big enough for sufficient number of people and so like that naturally caps your growth right and that's how how we really understood like the dynamics of a market, right?

11:49You 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 like lots of deal velocity. How long did this last, this first iteration? Two and a half years. 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 going to give it up or you're going to 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 you start taking to to change you know Pretty dramatically, I assume.

12:44Yeah, so we basically just realized that this is a problem. And we always had this view of like, we just need to run a bunch of experiments, right? To basically get feedback from the market, right? Like there's no other way to do this. Like you cannot hypothesize your way out of this problem. So we would always like come up with new ideas. I think what we realized after two years, it's not just a product problem, right? Because like we were doing everything that we could with the technology at that point, but it was also a market problem. Like people just didn't want this at like high enough rate, right?

13:16Like it wasn't a 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? You know, like, why should I care? Like, that's not a thing. And so we realized it's like, okay, we need to get out of that like that buyer, right? That was kind of like a critical thing. And you're aware, by the way, AR-wise, you're doing half a million, a million AR or less? Yeah, close to a million, like maybe 800K. Okay, so you've got something. You've got some worth defending sort of thing, but not enough. Yeah, and I think it's also like the size of our ambition, right?

13:46I think for a lot of people, I think they would just call it like, oh, this is a good lifestyle business. You know, next year I'm going to be at 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. Correct. But I think like both my co-founder and I, like 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, okay, we have some tech that works well in terms of understanding documents, which was a scarce resource, you know, like looking at 2019, 2020.

14:21So we have that, we have expertise in NLP, which, you know, 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 20 years and have a great network, we can just easily sell to banks. And so we said like, okay, we don't have that kind of like an unfair advantage, nor like we're in a place that has that.

14:59So we looked at, okay, like, what are the functions? And so within functions, we basically realized like, okay, the people that have the biggest problems with documents other than lawyers are finance teams, right? Because they're like looking at the same documents, contracts, orders, POs, invoices, expense receipts. Like everything is just like 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, okay, like let's focus on finance teams. Let's do a research. And the initial research was like, yes, you have teams that are reviewing contracts for revenue treatment.

15:36You have teams that are doing order management to book and bill deals. You have teams that are doing like the expensing, the expense receipts. You have teams that are doing like AP teams that are doing invoicing. So there's like a lot. And like those teams are pretty big. And so we were like, okay, there's a logic here. And so what we've done, we were like, okay, 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 like very different numbers than what you see in the market that's right we need to be a 250k ARR from minimum eight customers right then we put that guardrail as in we're finding product market fit not the fact that we can deliver services um so we said that guardrail and then we just managed to that right so we went out we tried to sell it to everybody who would be willing to speak to us and we ended up hitting eight customers, but like 350 almost, like 340K in six months.

16:37And so that told us that like, hey, where people are willing to pay for this, it's a bigger problem. So they're willing to pay more, even though they were super early. And it also kind of like scales pretty easily, right? If you can go from zero to eight enterprise customers in six months, that's like a good start. So that's kind of like the first guardrail and then the business continued to grow. And I'm curious, before we move forward, like the analysis of all the different industries and the functions and then deciding that you're better off going after a function, picking finance because they have potential problems with documents.

17:10That all makes sense. Like that's the theory side. Then you go out and you said you started talking to these customers. Like, do you have a sense? I'm curious what that was like, how deep, like how many customers, what kind of conversations, what kind of things did you ask? coming up with a story of something that sounds like it's going to work is so easy and it's so easy to delude yourself right and so i'm curious what you did ahead of it to increase the odds of the story you told yourself actually work and ultimately you know got you to the 350k or whatever i think it was just crisp articulation so there's i think maybe two components to this so once we wrote up a document right like the actual pitch like what would this actually be like if you put it in writing.

17:51And I think putting things in writing really matters because like once you see it, you start like iterating through that. But we basically wrote it down and we said, hey, your problem statement is you have teams of people who are reading documents and again and again. If we can cut that down by whatever it was, 70%, then you get this much time back. And what is the value, right? You don't have to hire as many people as you're scaling. You have better employee experience because people don't enjoy doing this thing anyway. You have better compliance because you have fewer errors. So we like wrote all of that down.

18:25And then I think I fundamentally believe there's kind of like two really magical numbers that you really need to care about. And that's 30 and 100. Because what we found is like, again and again, you want to talk to roughly 30 people to like validate an idea or invalidate an idea. 10 is not enough because you can always find 10 friendlies or 10 unfriendlies 50 or 100 it's too difficult too many right but you can easily have 30 conversations in a couple of weeks and 30 seems to be like a really good number to get that like initial validation so we talked to about 30 people truly validated and then i think like one thing that we found multiple times now is like you truly find the like core of the product market fit once you sell 100 customers it doesn't mean you know that it's like everybody's super happy and everybody's like at a huge deal size.

19:16But like, again, like around 100, like you actually have 100 goes at it. And then the motion kind of reveals itself. Is this 100 sales that you made or 100 attempts? 100 sales. Gotcha. And so that's kind of like what we saw. So pursued this business, we get it to about 100 customers. And what exactly did it do for these finance teams and these documents? It would read all these documents automatically so people don't have to. And it would kind of like fill in a checklist because all of these finance teams have like some kind of a view. Like you're looking at a revenue contract to understand if you need to account for this contract in a different way.

19:50Order management teams are like literally looking at order forms and it's like, does the data that we have in Salesforce or ERP agree to what we have in paper, right? So like we don't have like some kind of a misstatement. So it's all of that stuff. And so we would be able to usually punch out 60 % on the low end and like 90 to 93 % on the high end of all these documents that didn't have to be read by people that would drive efficiency and compliance. Yeah, and so we pursued that business. It was, you know, doing decently well. It was like 2xing, 2.5xing year over year, depending on the year. But what we found out is kind of, I think, two things.

20:30So the first one was this moment where we launched this other thing that just like took off immediately, right? And if you're looking at it, it's like the numbers are actually very similar. We ended up doing like 12 or 14 customers in the first like five and a half weeks compared to doing eight customers and getting 350k in six months. And before that was probably 350k in a year or whatever it was like for product one. 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 like the business that we've had before was like we loved the product, we loved the business, but there was a really big component of like professional services and delivery and implementation.

21:19We were trying to crack that problem every single day for like a couple of years. And like we were just really struggling with it. We were not good at it. And then you sometimes saw like people who are really great at professional services and they just like blaze through it. and they do it with like elegance and they do it with this like lightness and we were just struggling with it so much. And so the combination of, oh, there's this thing that like 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 like, okay, we need to focus the business on this new product.

21:58And where was the existing business like in 24? How big had it gotten at that point? Like three, four million? Oh, no, it was like 10. Okay, so it's sizable. Yeah. 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? Like, do you just pause the other one? Do you do both at the same time? How do you think about that? 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 like a transformation tool that allows you to understand how people work, turn it into a company brain and then allocate the work between people and agents right so ultimately that you can it's incredible like our customers are building these like dream teams of people and agents with it now but we had these two things like next to each other but all of this was not obvious yet we just saw like people are buying it and people are using it a lot but like i said like you need 100 customers to actually like really understand and like the product market fit the market speaks to and like reveals itself.

23:00And 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. Like you start seeing all of that. And early on, we didn't see that. And so we always believed to just like run four to five experiments every single quarter, especially early on to like 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, okay, let's build the hypotheses, right? We had one hypothesis was this could be a compliance tool for internal audit teams and like SOX compliance teams.

23:37One hypothesis was this could be like a risk management tool for banks and especially like places that have a lot of risk. We said this could be like pure like 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 a ERP transformation thing. So like for implementing ERPs or making changes to ERPs, or it could be like an AI transformation thing. This helps you build agents, right? Or like find where you should build agents, quantify the value and then actually like build them. And so we built these hypotheses and we went to the market and then basically saw like what's happening, right?

24:15And we got a bunch of these, bunch of interest and so on. But 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? And like you start really like kind of like seeing you get some like amount of data. And then it's easier to say like, okay, like we're going to go in this direction. For us, it was the AI transformation direction, right? We found actually like AI transformation and ERP transformation actually pretty close to each other in a lot of ways. But it's like that was the thing that was working better than anything else.

24:47And so then you iterate, right? So then you say, okay, we're not going to focus on these. We're going to focus on an AI transformation and ERP transformation. Okay, and then you iterate again and then you build more hypotheses. When do you decide, like have you completely abandoned the finance product? And if so, like when do you decide to do that? So what we ended up doing actually is we always want to optimize for customers and the team, right? Like 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?

25:20Everything that we've always built, our philosophy is to be a customer first organization. And so we said like, as we saw the other business just like skyrocketing and it overtook it in about a year. Right. Crazy. How fast did that grow? Like from zero to, was it zero to 20 in like a year and a half or something like that? Yeah. Close to it. Crazy. Insane. And so we saw that. And so we were like, well, how do we do right by our customers? Right. Because like, these are people who believe in you. They give you money to solve their problems. like you always need to optimize for the customer first.

25:53We 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 like manage it for the benefit of your customers? That was like 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 an interruption, as well as they were able to take care of the team, right?

26:31So 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. 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 100 fit moment you spoke a little bit here and there about what that product was but i'm curious on kind of the origin of that product you mentioned you built it for yourself i'm curious like what it was how you used it and then how you thought about and the way that you you demoed it and launched because obviously that provoked a lot of interest just love to learn more about that so we were implementing AI in practice, right?

27:18Like the way I described 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 kind of 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? This is for the finance teams that you were serving. Yeah, yeah. And it's like one interview after another, right? Like imagine having 10 Zoom calls a day or eight Zoom calls a day each an hour long and just asking people is like, Like, okay, can you show me how you do work? I was like, oh, you click on this in Salesforce.

27:50What 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 a Slack message and then you do that. And these complicated business processes in enterprise run on exceptions. You have like one happy path and that's like, that applies to like 20 % of things. And then 80 % 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, Like you need to put it in a spreadsheet and do something else. Like that's like how the reality is. And so it was always really painful.

28:21And we would just be like writing the down, documenting it and so on. And then we got some like note takers, you know, 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 like a business requirements document. Basically write up. This is how the process works with screenshots, like exactly what's happening, all these exceptions, which would be like a few hours of time of an FDE. And we would just do that in like two, three minutes. And it felt like magic to a lot of people.

28:56So 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 a set of agents to solve it. Is it similar to like a scribe AI we had her on the show? Not really. I think about it more like a standard operating procedure tool. Right, yes. This is how people do. I want to produce a standard operating procedure. The way we think about it is more like we're like a context layer. We're like a company brain of it's like a new data plane. How work gets done, how people make decisions, how they get approvals, how long things take.

29:35and do that at scale through observation rather than, you know, kind of building SOPs to help people on board. And so that is what you demoed at that event? That is what we demoed. It was like a super light thing. You know, it could do like one small process at a time, right? Didn't have all this like sophistication. We have customers that have tens of thousands of processes in the system now. Didn't have any of the sophistication and like clearly it hit a nerve. Like that's what people actually want. That's what they need. Given that you've gone through this three times, and obviously had different levels of inflection with each product launch.

30:09One of the things I've noticed, speaking with a lot of founders that found product market fit is, product market fit has more often than not little to do with go-to-market. Like VCs especially, and I think a lot of early stage founders, first-time founders obsess over go-to-market. How are we going to grow? Like, should we optimize this, optimize that, optimize that? And a lot of the people who are in the position that you were with, especially your first product, where it's like growing, but just not growing that fast, their first idea is like, it must be a go-to-market problem. Like, it just must be like, I'm just not that good at sales.

30:37I need a new salesperson. It must be that I just don't know how to get demos. Oh, my demo close rate is just not high enough. It's got to be something in the demo. And I'm like, my view has become, yes, you can optimize from 40 % to 50 % through better go-to-market, let's say on a demo to close. You can do that. You're not going to get from 10 to 50. You know, that's not going to happen. I'm curious what your reaction to that is. I totally agree. I mean, that was our first reaction too, was like, okay, let's figure this out. I ultimately and the team sometimes makes fun of me because like I wake up early every day and like oftentimes like think about the market and the team makes fun of me that like I say is like well the market speaks to you right or like things like the market the team is saying is like or like Andrew is just listening to the market in the morning.

31:19but I completely agree with you because like as long as you're showing it to people right as long as there's a way for them to discover there's like some basic level of discoverability and you're doing something that people really want then it takes off right and then I think like and then if you're good at sales or good at marketing and good at building product that's like a difference between good and great. Yes it's an accelerant. Exactly, but it's not a difference between good and bad. So let's go into two tactical areas that we're going to go deep on. The first one actually gets away because we're talking about listening to the market.

31:57You know, everybody talks about listening to your customers, like listen to what your customers say, voice of customer, all these sort of things. You've kind of built that into almost a science. I mean, you told me about a whole process that you have of making sure that you're constantly getting what your customers are saying and how your customers are talking about your product. Tell me kind of what you've said out there, and then maybe we can go in more detail. Yeah, basically, so what we do is like, so we set up kind of like, we have every single conversation with customer is recorded, right?

32:25Like obviously you always have consent, but like everybody's being recorded now, right? So whether it's a Zoom meeting or Google Meet meeting or whatever that is, like there's some kind of like recording, typically GANG or something else. And then it all goes into Salesforce. And then from Salesforce, what we then do is like we take all of the data and we enrich it with like usage data We enrich it with how many tickets and what type of tickets through linear people are submitting, how many times they're reaching out to support over email or like over a phone. Like we basically take all of the like communications with customers and we put it into what we call C360, customer 360 database.

33:04So it's a super rich database of basically all interactions with a customer and how much they're using the product and what ways they're using the product. So we have that like data plane. And then we basically built a bunch of different agents on top of it that are constantly looking and giving us these weekly reports and insights. What do people hear? So do they have a positive sentiment or a negative sentiment? You can then slice the data. Is financial services trending positive or it's trending negative? Is software trending positive or it's trending negative? Are you hearing specific phrases?

33:38One of the things that you can do now is give me basically an analysis of similarity of how the customer is talking to us versus is it highly variable. And industries where the customer is saying the same words effectively again and again are usually industries where you can grow faster, segments where you can grow faster than when you see a lot of entropy. They're describing the problem differently and so on. So we have this kind of like monitoring layer that gives us these continuous insights or continuously tweaking the messaging, the decks, the way we speak, the way we describe the problem, the way we ask questions, and so on.

34:19And all of it is just like about, okay, how do you go from that good to great? How can you like increase conversion rates? How can you support your customers better? How can you drive more value? What are the ways that you drive value to the customer? So that's kind of like the heart of the company now. So let's kind of break that down. I mean, the first one is you got to get this data. Obviously, everybody's recording everything these days. So that's a no brainer. But it's more about when are you reaching out to customers? Why are you reaching out to customers? And when you do that, what sorts of things are you purposefully asking?

34:48Because at the end of the day, we're just going to record a conversation. If the conversation is of little value, then you're going to get a transcript that's of little value. So you got to elicit that. So I'm curious what you do on that side to capture stuff that matters. You need to have that like enrichment layer. I think people made the mistake that they're like, oh, I just record everything and then I'm going to reason on top of everything. Because also a lot of people don't want to reject people, right? And so you might have a very positive sales discussion, sales first meeting, but it doesn't translate into the next step, right?

35:17And so you actually need to build the data layer with all these other types of data. So you can see, okay, well, whatever existing customer is telling you has the highest priority, the highest weighting, right? Whatever a customer that's quickly progressing through the pipeline or progressing at the rate that you want them, we also treat that at a very high rate. If you have somebody who just gives you a first meeting and then it goes away, then we're discounting that opinion. We're still digging into it because it could have been something, could have been our targeting, it could have been the way we pitched somebody from that specific industry and so on.

35:53So we still look at that, but in aggregate, that's a little bit less valuable than what the people who are already working with you are telling you. That is interesting, obviously. Stitching it together, I could see the value in that because then you can slice it by, you know, what you just said, but also, hey, of the customers that are sending us the most, you know, customer support tickets, you know, who are they and also what are they saying and so on and so forth. But let me ask it like this, like, what sort of things have you gotten from there? Like in terms of end outcomes that because you're doing this, you've seen this or you've heard that.

36:24I'm curious to get that kind of tangible piece of it. I mean, so for example, like one of the core things is like, so if you look at our website, like in the entire story, right, is really like three steps. We understood how customers are like talking about the problem that we're solving for them, right? Like that's the most important thing for every company is like to actually truly understand the nature of the problem. And you can start seeing these patterns of like people are like, yeah, I, you know, was running this transformation project and it took$5 million and took, you know, 18 months.

36:58And it took us six months just to like interview 150 people in my organization to try to understand what they're doing. And it's still, it was interviews. It was not the actual thing. And then we built something and it was the wrong requirements. And it's just like, and so you can kind of like the most important part of this is actually like understanding the customer pain, right? People are really good about talking about things that they know, right? And it's their problems. And so actually like deeply understanding the problem across hundreds or thousands of customer conversations and seeing are there any changes to the trend?

37:32Is the pain evolving in any way? I think that's critical. And so our ability to articulate the pain to the customer and be like, is this what you're experiencing? Is this your problem? If yes, we can help you. If not, we don't want to waste your time. So that's number one. Number two is like the way people talk really matters in terms of how do you articulate the problem in their own words then. I think that's really important as well, right? We had all these like ideas initially. We were like, oh, you know, should we be in this business of adaptive operations or like an intelligence layer and so on.

38:06And then the customers were just describing as like, it's a transformation problem. And they were describing as like, we would describe like technically what it is. We built a context graph of how work gets done. And they're like, oh, so it's like a company brain. And they're like, yeah, it's like a company brain. And they're like, why don't you just call it company brain? Now I can explain it to other people at my company. So you get these tidbits, because oftentimes when you understand the problem or your own technology in a lot of detail, you want to describe it precisely. But what the customer cares about is like, okay, what's the representation that I can then take and go and talk to other people about?

38:40And how do you use this regularly in the business? Like, is it like every Monday morning, everybody sits there and looks like, you know what I mean? Like, how do you build this into the process? So each team, right? So marketing team, sales team, value delivery, customer success, product, they have their own agents where they're running the types of insights and the type of data that they want from this, right? So for example, one of the things that we really care about right now is driving the usage across our customers of our MCP connector because people who are using MCP are shipping more agents than people who are not.

39:12So we really care about driving the MCP usage across our customer base. And so the product team that's working on MCP gets a report every day in the morning and says, this is how much usage there was. This is the sentiment. This is the feedback that the customers are giving. These are the types of queries. like all of these things. And so every single day, they can just prioritize and iterate and making the product better for the customer in the right direction. Now let's jump to the second kind of tackle area you wanted to deep dive into. You mentioned earlier that the way that you perfected, like you needed that launch and then the way to really perfect it and hone in on what you now do, which is transformation, was through this idea of four to five experiments a quarter, four to five hypotheses that you're testing out and you have like leading indicators that you look for.

39:58Yes. Tell me how all of that is set up. So we do quarterly planning sessions. We don't believe in like longer than like 90 day cycles at the company. So we do these quarterly review sessions. It's basically with like an extended executive team. We actually use this database like thoroughly and basically see like, okay, what are the core patterns? What are the either the core problems or what are the things that are working really well? And like, how can we double down on them and make a short list, right? And then circulate it to everybody. it, then we talk about it, then we typically pick a couple of bets.

40:32And some of these bets have more like a go-to-market or like customer success shape. Some of them have more like product shape. And we say like, okay, this quarter we're going to validate these. Usually it's four to five hypotheses. We assign it, we say like, okay, what would success look like? I think that's critical. I think the qualitative stage of product market fit happens only very, very early, right? You launch it in an event and everybody wants to talk about it. It gives you conviction to take the next steps. But once you become a little bit bigger, once you launch more product and so on, then it becomes tricky.

41:06And you need to be very quantitative about it. And so we typically just spend a lot of time, what's the startup metric that would give us confidence to basically resource it on an ongoing basis? So one of the things, for example, when we launched RIMCP, we're like, we need 100 users in 90 days. Otherwise, we're not going to consider this to be a huge success. Maybe we just are not that useful. And then we focused, we had a clear person owning it, and we're like, okay, your job is to figure out how to get 100 users on this. And we defined the users like somebody who uses it this much, this frequently, and so on.

41:43And so setting that initial metric directionally, so then you can clearly say it's working or it's not working. So you can either kill the bet or you can resource it more heavily as critical. What are some other examples of experiments that you've run, especially things that, you know, to improve? I imagine like once you're over 10 million and you're kind of scaling, you're taking every single part of your funnel and you're trying to tweak things. You're like, how can we get a little more leads? How can we improve conversion from, you know, MQL, TESTQL? How can we improve lead? Whatever it is, right?

42:10Like I'm curious on maybe some of those where you're like, oh, this part of the funnel is not great. Let's try two or three experiments. And then kind of how you thought about that. I can actually give you an example. I'm sitting in one of our experiments right now. So there you go. So we did this rebrand, right, to really show, A, we're not finance only, but we're supporting HR and sales teams and operations teams, marketing teams, kind of like across the entire enterprise operation. And then the second thing is like, I think it's reflecting our ambition, right? Like we have a hundred billion dollar ambition that, you know, we think we can really build this like company brain for every single Fortune 500 for every single global 2000 company.

42:48And so we were like, okay, what are some of the unique ways we can communicate these two ideas? And so one of the ideas that we had was, so we did this report where we surveyed a few hundred, you know, enterprise executives. And we built this like entire report. It's called the state of agent tech work. And so we built this report, but like so many companies do reports like this, and then they end up like a brochure that you can pick up somewhere, or they end up as like a PDF that you can download behind the gate. And so what we've done is we took the 12 most interesting data points or learnings.

43:23We commissioned artists to create like a visual representation of each of these 12 metrics. We've rented a space at the University Avenue in Palo Alto for an entire month of August. It's like a pop-up store. It's a pop-up data art gallery. Everybody should come, by the way. It's open every day, 12 to 6 for the month of August. and just like experience the problem statement that we're solving and the solution in kind of like a completely different way, right? And we're tracking very closely now how many people come in, how many are our ICP versus how many people are just visitors. And ICP we're tracking both for hiring and for enterprise.

44:05And so you continuously need to do kind of like interesting, unusual things. And nine out of 10 fail, but the ones that work, they work exponentially better. So that was one of the big bets for our Q3. Got you. I guess the other question I was going to ask is, I assume every single experiment has at least one metric that's assigned to it, that's specific, that kind of has a clear kind of failure success bar. Yeah, correct. And you can see the ramp usually very early, right? I think it's like doing this for a while, like, you know, usually you don't actually need to see all 90 days for something to play out.

44:39You see the ramp, right? So you know, like, okay, my expectation is this. This would be success. and I think my mental model has changed a lot. I thought like when we started 10 years ago, my mental model was most things will work incrementally, right? And some things will not work, but like most things will work incrementally. And my mindset has completely shifted. I think it's like nine out of 10 things don't work and they might look like they're working incrementally, but that's really like, it means that it's not working. but the one out of 10 things works so well that like carries everything else.

45:16It is very, I mean, most people will talk about it like there is no silver bullet. You just have to do everything. But it's almost like you do have to do everything in order to find the silver bullet that really does kind of move the needle. Yeah, exactly. And it's a silver bullet for part of the business, right? And so you do one, so you need to build a machine that finds one or a few silver bullets every quarter. But it's like, for example, your problem is specifically I need to get more demos booked. Like once I get a demo, it's great. Let's say, okay, you're going to try 10 things, but it's not like every single thing is going to get, like most things are going to barely move the needle.

45:54And therefore, in your opinion, they're basically useless. And then you're going to find one or two things. Those are going to move the needle. And then you just, you go all in on those until they stop working. Exactly. And I think it's because people are applying how big companies are working, right? So if you have lots of scale, if you have 100 ,000 employees, you're a really big company, then like doing these like incremental things really matter. And you need to cover all your bases. You need to be omni-channel and you need to have 10 different routes to market and you need to have 50 different support models and you need to do all of that.

46:27That's true for a big company. But when you're a smaller company and you want to grow fast and you want to become that big company that can do everything, you need to figure out these things that work exponentially better because i mean to your earlier point you cannot increment your way out of like slow growth right it's usually something to do with the market structurally like you have some kind of a problem so even if you increase your conversion rate by five percent and you do all of these things it's not going to magically 10x your business and so so you need to continuously finding these like new things completely novel ways to address the market to get that outsized distribution and value benefit.

47:07I see this especially, I would say, on marketing. I even experienced it firsthand on the Product Market Fit Show in the sense that I could do 30 things to try and grow it. Let's cut it into shorts. Let's put it on YouTube and LinkedIn and Twitter and this and that. And I see it in startups all the time where it's like you hire VP marketing and they're like, okay, let's do all the obvious stuff, right? And nothing wrong with trying it, But the idea has to be in order to cut everything and then do one or two things well. Because doing 20 things that are all incremental that also everybody else is doing, it's not going to get you where you want to go.

47:39Exactly, right? Like, for example, we don't run any Google Ads. We have zero ads, right? Yeah, and exactly. Like a traditional VP marketing comes in. It's like, oh, you're not running ads. That's part of the playbook. You need to be running ads, right? You know, one of the things that we're finding is like, well, you know, we'd rather like allocated capital to do events, right? Whether our own events or third party events, we spend enormous amount of our marketing budget on events because that's the number one way to do it. And so, and sometimes I can interview these people, I'm telling them about the business and like, well, do you worry about like the fact that everything is one channel?

48:16I was like, no, because the channel is like working super, super, super well. So I don't worry about that. I would worry if it was not working. Perfect. Well, listen, let's stop it there. I'll ask the question that we tend to end on, which is what would be like your top piece of advice for an early stage founder that's still looking for a product market fit? Listen to the market. I think people, and it's kind of like an abstract idea, but I feel like if you have enough repetition, like when I was talking about the 30 and 100, really focus on these numbers. Always when you want to validate an idea, talk to 30 people, and then you can only start feeling reasonably secure about the nature of your product market fit once you have 100 customers.

48:54It doesn't matter if it's 100 customers who are paying you$10 a month or like 100 enterprise customers, but unless you get to like that scale, it's very difficult to feel good about your product market fit. Perfect. Well, Andrew, thanks so much for spending the time, man. It's been great. Thanks so much, Babu. This was fantastic. Thanks for having me. Wow, what an episode. You're probably in awe. You're in absolute shock. You're like, that helped me so much. So guess what? Now it's your turn to help someone else. Share the episode in the WhatsApp group you have with founders. Share it on that Slack channel.

49:26Send it to your founder friends and help them out. Trust me, they will love you for it.