B (3:15)

So Peak is a AI search analytics product, which means that we help companies understand how they're performing on AI search as a marketing channel. So how their companies show up on ChatGPT, Perplexity, Gemini, ET cetera.

A (3:26)

Great. Now, the business was, I mean, you launched the business like just over a year ago, is that right?

B (3:33)

Yeah. So we started in, I think 14 months ago in. In January 6th, and then we launched one month after that in February. And now we are at a little bit over 2,000 customers and 8.6 million ARR and 55 team members. So it's been a really, really intense 14 months and a crazy journey and we're super excited to be as far as we are today.

A (3:53)

That's awesome. Let's start with your story where because you have an interesting background, I want to talk a little bit about that. At one point you were doing pretty well in terms of esports and you were big time into League of Legends and then you ended up at PwC and then you're building a startup. So just tell us about that. How did you go from sort of playing League of Legends to building this startup?

B (4:27)

So I always say I have the most random life ever. So essentially how I grew up as I was really sick as a child and couldn't really go to school, and then through that, kind of played video games extensively, like throughout the day, like all day, all the time, and at some point started to take it more serious. So between like 12 and 17, I didn't go to school basically at all and played league basically from the moment I woke up to the moment I went to bed and to sleep, and then at some point got like top 100, played lots of tournaments, started streaming on Twitch. It really enjoyed my time playing esports. I think it was a really, really cool experience. Specifically getting so good at something teaches you a lot of lessons on how to get good at anything else. And I really enjoyed it and I think it really created A good foundation for me to succeed as a person.

A (5:13)

Yeah. And then you were at PwC.

B (5:15)

Yeah, so I was 17, I had a surgery, then I could live like a more normal life again and then decided like, hey, I'm done with esports, I want to have a normal career. And then got into software development. So I actually built software for like startups and scale ups, which was fun, but it never really was super fulfilling to me. Like I always liked thinking about the business problems more than the engineering problems working in startups. And I said, hey, if this is more like my natural interest, then I should pursue this direction more. So I redid all my school degrees, which was quite a grind, and then decided to study economics at the worst university in Germany because I couldn't get into any of the good ones because I had no school degree. Basically a very, very shit one. And then I randomly worked my way into a very small VC and doing some investments from pre C to series A into early stage startups. And then through that experience I managed to land a role at PwC's venture. This practice where I did mostly like, yeah, M and A, so like buying and selling early stage companies, helping founders on fundraising, but also investing in VCs as a limited partner together with family officers and corporates was really cool. So I got to see like the industry from multiple angles. Being in a startup, investing in startups, selling startups, buying startups, investing in funds. So I always wanted to start a business. That was always my dream. And then at some point I felt ready to say, hey, now I have seen this industry from all the angles that are interesting. I feel ready to go out and do my own thing.

A (6:36)

So tell me about the moment that you had that aha moment for this idea. And actually before we talk about that, you got into an accelerator, Antlers, and you went through several failed attempts to build a startup. Tell me a little bit about that. Like how many different attempts did you have before you found Peak?

B (7:01)

For sure. So does that say Antler works in a way where it's different than yse. You apply with a team and with an idea. And at Antler you can also apply as an individual with no idea or with an idea. And then I really went there because I had no team and I wasn't completely sure what I wanted to work on. And then I had this rough pitch that I would give in my Antler interview for this topic which then three weeks into Antler completely fell apart because the legislative environment for it changed dramatically in that time. And really early On I connected really well with my co founder Daniel, who was also part of the who. I think we had very similar energy and vibe and really liked each other. And he's like a super, super hard working, intelligent guy. And we then started iterating through different ideas. Our other co founder Toby was also in our antler badge, but he actually worked on like something else at the start of a different team. And Daniel me went through like legal tech which made sense because Daniel had studied law and legal tech with AI started become like a thing. Then we looked at regulatory technology with AI which actually like the idea we worked on. Now also multiple people are doing but we couldn't really find something where we had like the feeling of like, okay, we really have like a market pull for this. Which is then why we ended up kind of like always dropping it after two, three weeks. And I think what I learned the most in that time is that people really don't want to buy stuff they don't really urgently need in a B2B setting. And it's really about finding demand much more than it is about creating supply. And we really learned that firsthand by trying to sell something. So our idea was always like, hey, let's do it. Sales first. Let's always try to sell even if we don't have it. Let's just try to sell like the vision and like the idea and then build it once we have it. And yeah, you can just tell that people, you know, they are really unexcited about buying something they don't need. If it's something they really need, they're actually excited to buy it, even if it's really shit. So even if the product is like an absolute like alpha version of it, if people really need what that product can deliver, they will still pay for it.

A (8:59)

So I often see founders trying to validate an idea and maybe spending six months, even 12 months trying to do that. And you guys were going through this ideas in two or three weeks. So tell me about how you did that, how you were able to get to a point that you were like, okay, now we know enough that we can kill this idea and move on to something else.

B (9:24)

We also read all these books that are kind of out there about the mom test and all these things. I think they were all actually pretty helpful. I think actually what helped me really the most is there's one guy, I think he's from Harvard, I can't recall his name right now. And he really developed a lot of these ideas that we then also incorporated as into you cannot really create something on the market. There has to be a very, very strong pull from the market. And that's kind of what we were looking for. And you can tell that actually pretty early on in your journey of validating idea, if people are just excited about it, if they just want to talk about it. For example, with AI search, at the time when we started working on that idea specifically, it was already a topic for the, I would say 1 2% of the best SEO people on the world. They were already very actively discussing and thinking about it. So if you would catch one of them, they would get very excited about the topic and they would always pitch us like, hey, our field is really going to change type of idea. And that made us really excited because we were like, if the top people already believe it, then we believe that would also trickle down from there to the rest of the ecosystem and the industry.

A (10:26)

So with some of those earlier ideas, were you doing, like, cold email outreach and everything?

B (10:34)

Yeah, we really did everything. So our idea was, obviously, we'll do whatever it takes. If it's going to be like doing like 100 cold calls per day, then that's what we were going to do. So we tried everything from meeting people in person, writing people on LinkedIn, writing cold emails, sending people loom videos. So we would just record like a loom video. And that's like this hack. I think many people do this now, but at the time it was new. Where you pull up someone's LinkedIn, you'd be call it a loom video, with their profile open, and then you send them to it. And then, you know, in their preview, they see their own LinkedIn profile and you talking about it. And this would always make them click on it, you know, because, like, people want to see what you say about the LinkedIn profile, so they're like, very likely to click. I think at the moment, like now it's already done. I think people have done this too much now, but at the time it worked really well. So just like, finding like these really hacky ways to get in touch with people, I think at the start is very much required. And, like, you just got to be creative.

A (11:22)

So it's easy to kill an idea when you send out 200 emails or do cold calls and no one's interested. Right. That's an easy signal. But quite often what happens is you get mixed signals. You get a lot of people who are not interested, but you get some people who seem kind of interested. And I want to understand how you figured out whether There was enough demand there to keep pushing the idea. And then maybe after that, we can talk a little bit about how it was different when you started talking to people about Peak.

B (12:00)

Yeah, I think the counterintuitive realization that we had is that to validate an idea, what you need to be doing is you need to not pitch the idea. So basically, if you want to understand, hey, is AI search the number one topic that SEO people think about? Then the worst thing you can do is be like, hey, this is like my AI visibility product. Would you like talking about it? Because then you a, only select the people that are interested in already that solution, and B, your feedback will be very skewed. What you should do instead is you should be like, hey, you're an SEO professional. I'm trying to build something in the SEO space. Do you have like, five minutes to chat and then be like, hey, what is, like, the number one thing you're thinking about right now? And if reoccurringly you hear the number one topic that they think about be AI Search, then you know you really have a topic that people actively want to work on. Right. And then you can start asking questions like, what have you tried to solve this problem? Like, how many AI search products have you looked for? Have you looked for any? Have you tried to buy one of the ones that you bought? What did they not deliver well? So you need to kind of get them to organically produce this kind of output versus then asking directly about it. And that really gives you very honest feedback. And if you talk to 50 people and for no one of them AI searches their number one topic, then it's just not interesting enough.

A (13:12)

Great. And then, so when you came up with the idea for Peak, the response was clearly different. And how, how soon were you able to say to yourselves, with confidence, this is the thing we need to pursue?

B (13:27)

At some point we just decided like, hey, this is as good of a shot as we were going to get from the response we've gotten back from the markets. We just decided to run with it, and I think it was good enough for us to commit. So we just said, hey, like, this is. This is good enough. And you will never get to, like, a complete 100% confidence, especially at the start. So you also have to believe in it somehow. And we believe kind of like in the fundamental idea that AI search should really change the search environment. So we believed in that macroeconomic trend and we saw the market demand, and then we were like, okay. These factors combined make us confident enough to kind of double down. And of course at the start the market wasn't there yet completely, so it had like some catch up to do. But it got there very fast. For example, when we launched in February, the topic was still very, very niche and only a few people were really interested in it, but it was the very smart people. And they also predicted that this would be a very important thing that would happen on the market. And then three months later the ecosystem had changed so much that basically everybody was interested and we were flooded with interest and demo calls and inquiries and stuff.

A (14:31)

So how much did you know about? Well, first of all, what's the term? We talked about this earlier. Is it geo? Is it aeo? What? What's the term that you use?

B (14:41)

So we use Generative Engine Optimization, geo, but we also don't really care. We will call it whatever people call it. We have no take on what it should be called. And to your question as to how much we knew about it, so very little, to be honest. We're not SEO professionals from our history, Daniel and me specifically, Toby a bit more than we are. He worked in sea search engine advertising for a bit. But Daniel and me are just very curious about consumer. We really like consumer stuff. When we walk together through the supermarket, we'll always be like, oh my God, this could be a new cool pasta brand. Or how do people like this trend and this trend? So we were all very interested in consumer and of course I'm very interested in AI. So it was pretty natural for us to look into what's happening with AI and consumer. And this was one of the things we looked into.

A (15:28)

So let's break it down a little bit. So for a founder who doesn't understand much about GEO other than, you know, they, they're starting to think about it, that I need to get discovered more in ChatGPT or Perplexity or wherever, just break it down for us. Like what makes an LLM pick one product or brand over another, right?

B (15:56)

So basically it's a combination of different things and it also depends, kind of like how the LLM treats the question that's being asked. So it can, one can do one of two things. It can look at the question, decide that it can answer that question from its foundational training data and then give an answer. Or it can go, ha, this is actually a very difficult question. Let me do some web search, then pull all the results I can find on the Internet, kind of go over them, read them and then decide what my answer is going to be. If we purely talk about the foundational training data response, then of course, getting in the training data is very, very important. And if we talk about the web search response, then kind of being in that step where the LLM goes out and discovers content and then also being represented well on those sources that the LLM pulls in, that gets you in the answer, essentially.

A (16:43)

And so what should a founder be thinking about doing? What's the free solution? If I wasn't using Peak or some other product, what are some of the fundamentals that I should be ensuring that I'm doing with my product, my marketing side, anything else?

B (17:02)

I think the number one thing is actually understanding is this even relevant for you? Right. So you definitely shouldn't be spending time on stuff that is not relevant for you as a founder. So what I would do is to think about where are my customers spending time? Are they spending time on ChatGPT? I think for like B2B SaaS software, this is very common. A lot of B2B SaaS buyers spend a ton of time on ChatGPT researching products, and then you should think about what they're asking. So what could a potential customer be asking on ChatGPT? For example, let's say you have like a niche solution for like, I don't know, like pure procurement optimization or something like that. Then you want to definitely be found if a customer asks what is the best solution for procurement optimization in SAP or something like that. And then what you can do is you can try to understand if you're already in those answers by opening an inco neoto tab and just putting it into it a couple times and seeing what the result is. This can be very skewed, which is of course why a product like ours is very much recommended to really get a full sense of how you are performing on a differentiated set of prompts. That's not just one prompt, but maybe like 50 or like 100 or 200. And then once you have that data on if you're appearing or not, you need to start to understand where's the model pulling information from. For B2B SaaS brands, this may be like a couple different sources. This may be like Reddit at the moment. That's quite an important source to be present in. And then if you discover something like Reddit to be an important source, then of course you can start to form a marketing strategy around that and say, hey, maybe I should engage in these communities on Reddit that are like talking about my space and kind of like get my product out there or my name mentioned there. And that can like, be a starting point for your, I would say, like, geo strategy.

A (18:32)

You know, what strikes me is that this is becoming increasingly important. I mean, we've heard people say, you know, Google search is dead. And it's not really dead, it's declining. And the amount of searches that people are, or search traffic that they're getting from LLMs is increasing. But I think even maybe Gartner said that by end of next year it might be like 50, 50 or something like that. So anyway. But there's definitely like, you know, a clear growth here. And the. To me, it strikes me like the product needs to be pretty sophisticated to be able to pick up all of these different signals and intent and everything and show your customers reliable information, to be able to make the right decisions. But how do you go about building a product like this? You don't have experience in the space, right? You're like, just going through different ideas, different domains, all of this stuff. And then I came across, when I was doing research for this interview, it was like, you built your essentially your MVP in like one and a half days. Right? So tell me about that. Like, what did that first version of the product do? And how did you, how did you build it?

B (19:53)

Right, so we are basically in December, two months after starting Antler, and Toby was still working, like, our third co founder was still working with this other team. So we didn't really have like someone super technical on the team. And you know, I worked as a software developer before, but at that point it's been like four years since I wrote like the last line of code. So I just took V0. I was like, hey, we just got to get it done. I Vibe coded the first prototype. We used that prototype to raise the first like 100k from Antler, which on the AC, they committed to us. And we, I think we closed like eight or nine, like Lois with customers based on their prototype to be like, hey, if we build this, are you guys actually going to buy this? And then they said, yeah, sure. And so we made them. Made them send an LOI for that. And then we convinced hubby to join afterwards. And then he of course took over kind of like the technical implementation of the actual product. And he's just amazing. He's super strong technically. And then he built the product in six weeks, which was also crazy, crazy fast. And then we started selling basically right away.

A (20:53)

You used V0 for that MVP. What did it actually do?

B (20:59)

So basically what it would do, it would allow you to put in a set of prompts, so you could enter 50 prompts, for example, it would then go to the LLMs APIs and then it would put in this prompt, it would extract the response and it would show you which brands are showing up and how they're being talked about. So it would rate kind of like the sentiment about the brand between 1 and 100 and it would show you which brands are being mentioned. And that's basically all the product did at that point. But that is still like a core functionality of the product today, right? Like, of course, like you need to know which brands are being mentioned and how they're being mentioned. So I would say the, the product roadmap for our space is actually pretty clear, similar to, I think, how, if you think about Neobanks, for example, the product roadmap for neobanks is also very clear. When Trade Republic started and N26 and also Robinhood and stuff to a degree. I think if you ask all those founders, hey, what is the product? For the next five years, it was very obvious someone's going to do a foreign exchange product, a credit card product, an insurance product, all these different product lines. And similar to us, we and our competitors, they had all the same ideas. And then it just became about how do we execute this the best and who can build the company the fast, who can hire other people, who can raise the money and who can build the best product and convince the customers to join.

A (22:13)

The MVP gets built very quickly, the prototype. And what I want to try to figure out is where did those first 10 customers come from and who were they? What type of companies or buyers, you know, were those initial customers?

B (22:34)

So we went the path of least resistance by trying to find customers that were then at that point most willing to buy. So we just looked about, like, looked at like, who's talking about this problem the most on social. So when someone would post something about our space on social media, like on LinkedIn or on X, we would just like try to approach them with the solution at that point. Because we already validated, had this base calling point and now was about selling the product. And those were like very warm leads, right? Because they actively talk about this problem and that we found, I think our first in customers by someone that's been like, hey, AI search is going to be important, blah, blah, blah. And they were like, oh, that's very cool. We actually worked on this and now we have this product like, do you want to try it out for free? And then at some point, you know, seven days later we asked them like, hey, okay, we can all pay for it. So I think that's the route we went.

A (23:17)

What was the pitch to those initial customers that signed the letters of intent? The Lois, they weren't looking for GEO tools. I know it was very early in the space. I'm not even sure they really understood that there was a problem or a solution, you know, that they needed to focus on. So I'm just curious, what was the pitch that you, you gave?

B (23:44)

Pitch that we gave was basically, hey, this might be a channel you can make revenue through. So wouldn't it be nice if you could tell your kind of like board or CMO how you're performing in that new channel? And that was kind of the pitch we delivered. And some people took it. Not everybody, of course, was very hard to pitch it at that point because, I mean, there was basically no revenue being generated at that time. So they had to also believe that this would become important. And that's kind of like how we needed to position it back then.

A (24:10)

And then once they responded, you would go and show them the prototype that you'd built in one and a half days and then get them to a point where they would sign a letter of intent.

B (24:23)

Yeah, and of course also like letters of intent in general are pretty worthless, but they're like legally non binding. But at least it sets the stage as, hey, they are so interested that they would at least like go into DocuSign, they would read through the LOI, they would make sure it's actually non legally binding and they take the risk and they, you know, click sign. And I think it's at least like some level of commitment that you're trying to get there. But I do want to emphasize, like nothing beats someone paying. Like that is definitely the only real validation that you can get from, from a, from a potentially interesting customer or lead is by them actually, you know, taking out the credit card, putting in the credit card details and then purchasing the product. That's only the real thing.

A (24:59)

Did you guys think about charging them upfront and maybe just saying, hey, if you pay for this now in advance, we'll give you a big discount rather than asking them to sign an LOI

B (25:08)

at the point we didn't. I think if you can get away with it, that's a better way. So I think if you can do that, you should do that. And I think in an enterprise setting that is also more common where, you know, there's more integration work being done, you can kind of get away with doing something like that.

A (25:21)

And how long did it take typically on those first 10 to go from them replying to your email and saying they're interested to getting that DocuSign notification that they just signed the LOI.

B (25:36)

I think we were very aware that this is going to be like a massive numbers game that only if you read like 100 dingy messages. I think we had a response rate of like 4% or something like that. So we just completely ran a massive campaign on maxing out every channel. We max out LinkedIn, we max out emails, we called people, we went through our entire network and we went to conferences, we talked to people in conferences. So we just maxed out every channel. And then just by the sheer volume of people we talked to, some people had to do it.

A (26:06)

If there's a founder listening who has maybe who's in that similar stage where they're onto an idea that they believe there's a meaningful problem that they can solve, and let's say maybe they've sent out 100 cold emails and haven't had a great response so far, but they're committed based on the early signals. What advice would you give them? Just to keep trying, send more. For how long?

B (26:40)

I think this is one of the hardest questions we can discuss when not to quit. And I think it's so hard to give a generalistic good answer to this question. It really depends on, have you really tried everything yet? I think just from a numbers perspective, 100 emails, not much, you should probably send a thousand or more. But it also depends on who. If the 100 you sent were extremely well qualified and you really believe, hey, from these 100 people, they're the most qualified in the world, some of these should be buying it and then not buying it. Then there's something wrong with the messaging, how you place the product, how the product looks or feels like so then there would be a mistake somewhere.

A (27:20)

And because it's so difficult at that point because you don't have any customers, you're not sure, is it my messaging that's not right? Is it the target? Am I just talking to the wrong icp? There's somebody in that company, but maybe a different ICP that I should be focusing on and is it my execution? Right? Am I going on the wrong channel? Am I not sending out enough emails or am I not doing more high touch versus the volume game? Right. So there's all of these things that you have to keep testing. I asked you that question. I wasn't expecting, oh, here's the step by step plan to get there. But there are definitely some principles I think that people can apply here, right, as they go through that process?

B (28:06)

Yeah, I think that's right. I think you want to be really truth seeking also just for your own sake. You don't want to spend two years doing something that you know it's not going to work out to the degree that you, that you hope it would. So I think it's a very, very hard line to balance and I just hope that we, if we had to do this again, we'd do it correctly again. But I think it's, it's very tough. Yeah.

A (28:28)

Now you went into a space that had some very well funded competitors who were charging, you know, $500 plus a month for their products and you guys decide to charge what, about $85? Walk me through how you guys arrived at that price.

B (28:52)

So the competitor referring to as the only one that raised more money than us, and they're also bigger than us from an AR perspective right now. They started like roughly a year in advance and they really went out with this pitch of wanting to work with the biggest brands in the world that was kind of like their claim to the market at that time. And, and we looked at that and we looked at the SEO software space and we thought, hey, like the biggest companies in the SEO software space are actually not the biggest enterprise platforms. The biggest companies in the SEO software space are the mid market targeting companies. Because SEO as a topic is so relevant for so many small customers. So we really believe that JIO will develop in quite a similar fashion where it's going to be very, very relevant to 2000-002000-00300,000 clients. So we wanted to capture that segment. That was our market market approach. So, you know, our pricing had to follow that. ICP has to work for some of your audience members for sure, like a founder that can only afford an $89 subscription a month. And so we package the product in a way where that is possible. Right. Where we sell at healthy margins in those small plans, but where we can offer such a price point.

A (29:57)

Okay, so you decided to say, okay, we're going to target smaller customers. There's a bit more of a volume play here. We're not going after the enterprise, the whales. And so the pricing, like was there a point where you would talk to customers and they would compare you to that competitor or ask about why you were different? Because I mean, pricing is one thing, right? You can attract the price sensitive customer who doesn't want to pay $500. But beyond that, what was your, what was the way you differentiated Your product.

B (30:38)

I think as we build more towards our SAP, which is kind of like the mid market, there is different product features that you are launching at that point. Because I would say for an enterprise client you can go pretty far into integration requirements where you can run a three month pilot. They can really put some engineering effort behind it at some point and connect a lot of different things. And for a mid market company or an SMB, they need to get to value very fast. Like they have like a marketing team of like three people or two people or one person. And that person needs to you know, juggle social media. They need to juggle, I don't know, like asset creation for, for graphics. They need to like handle like three different agencies. They need to do SEO, they need to do like all these other things, they need to do their website. So they need to really lock into the product, find something high leverage to do, do that and see the impact. And I think that's very, very different than that. Kind of like an enterprise buyer. For an enterprise buyer also in our space it's a lot about downside protection. And for our mid market clients on our end, I think it's much more about creating growth.

A (31:36)

Okay, so you, you, you, you get the allies, you then go and build the product in like 6 weeks and then suddenly 11 months later you guys are like 5.5 million in ARR. Like what happened? What was the inflection point? Where did that growth sudden, that spike suddenly come from? Us.

B (31:55)

It was a lot about social. So I'm actually inherently quite a private person. I don't even have like TikTok or Instagram. But I said, hey, if you want to win in B2 B26, you have to be on social, you have to love content. And I think there's a couple companies that do that really, really well. I think for example Pylon, I don't know if you know them. It's like a customer support success product. The founding team also does it super, super well. So I think we generated a lot of traction on LinkedIn and that really, really helped getting our name out there and then also just really making our customers happy. So we grow quite significantly through word of mouth. It's about like 30% of our revenue comes through that channel. So that's of course like really, really exciting. And if people are happy with the product, they like the product. That's the best revenue channel you can have.

A (32:39)

So the majority of your leads are coming through social.

B (32:41)

Even today I would say it's a pretty wide mix Now A bunch of it is social. AI search, of course is quite a significant channel for us. About 20% of our conversions are coming are coming through AI search. So it's one of our bigger channels of course as well.

A (32:55)

If you want your SaaS product to show up in ChatGPT or Google AI overviews, when people ask for recommendations, Respawna can help. They get your brand cited on high authority sites that AI tools actually pull from. Completely done for you and Pay per placement. SaaS podcast listeners get $1,000 in credit on a subscribe and save plan. Just mention my name after you sign up. Visit respona.com that's R E S P O-N-A.com if you're building an AI agent, a SaaS product, or stuck trying to scale, check out Gearhart. They can act as your fractional CTO and technical team, bringing AI expertise from projects for Meta and Google plus strong Silicon Valley connections with founders and VCs. And since they're a Ukrainian born company, you get senior engineers with an offshore pricing model with offices in San Francisco and London and a distributed team of 40 experts. They've helped build over 70 successful products. Right now, they're offering our listeners the first 20 hours of development for free. Just book a call@GearheartIO. That's GearheartIO. Right now, there's a number hiding in your SaaS metrics. The exact point where your growth will stall. You can change it, but first you have to see it. It's simple math. When your churn catches up to your new revenue, you stop growing. You're running just to stand still. I've built a free calculator that shows you exactly where your ceiling is and the fastest way to break through it. Find your ceiling at SasClub IO calculator. That's SasClub IO calculator. Okay, awesome. All right, look, I'd have to keep talking, but we need to wrap up here, so let's move on to the lightning round. I've got five quick fire questions for you. Ready?

B (34:44)

Ready.

A (34:45)

What's one piece of startup advice that you disagree with?

B (34:50)

Being scrappy. I think being scrappy at the start is super, super important. And we were super scrappy. We ate like two euro canned food every day for six months. But you got to shift into kind of deploying a lot of capital very quickly when you find product market fit. And I grew up pretty poor, so for me it was very hard to get into that mindset.

A (35:06)

What's the last great book that you read?

B (35:08)

I honestly don't read that much anymore. I don't really have the time. I think the last really great book for me was actually an Eckhart Tolle book. It's called the Power of Now. It's quite a philosophical book, but I really, really liked it.

A (35:20)

Good book. What is something that you've had to learn the hard way that you should

B (35:24)

try to be effective rather than trying to be cool when it comes to, like, business fundamentals?

A (35:29)

Yeah. I mean, we didn't even talk about that. But the fact that you guys didn't even have a product manager when you launched. Right. It was like, we're going to figure this all out ourselves. Yeah.

B (35:37)

That was not a good idea.

A (35:39)

What's a tool or habit that saves you most time?

B (35:42)

I, at the moment, I'm obsessed with Claude. I use it so much. It's just incredible right now what you can do with like, Claude and granola and notion. If you deploy it correctly, you can really automate so much stuff.

A (35:53)

I agree. And finally, what do you do for fun when you're not working?

B (35:56)

So I play video games sometimes. Still going out. There's like a local gaming studio which I go to and then I also go partying quite a bit. So I try to, like, get a little dance in when I can on like a Sunday during the day here in Berlin. That's possible. The clubbing scene is even open on a Sunday, so that's always really, really fun.

A (36:13)

Cool. Well, Myers, thank you so much for joining me. It's been a pleasure. If people want to check out Peak, they can go to peak. AI that's P E E C AI and where do you hang out online? Is it LinkedIn?

B (36:24)

It's LinkedIn. Mariusminers.

A (36:26)

Thanks, man. I appreciate you sharing your journey so far. Congratulations on the early traction and excited to see where you guys take this business in the coming year.

B (36:36)

Thank you so much for having me.

A (36:37)

My pleasure. Cheers.