INTERVIEW | Make Money by Winning AI Search Without Venture Capital with Jon Mest

0:00You're listening to the Travis Makes Money podcast. What's going on, everybody? Welcome back to the Travis Makes Money podcast. Words are mission to help you make more money on this episode of the show. I have a new friend, John Mest. John is a data science by training a salesperson by accident and a customer obsessed entrepreneur. He's worked as a Wall Street analyst, built data products for future or for Fortune 50 brands. Excuse me and helped found multiple companies. Today, he's the CEO of Just Reach Out, a digital PR platform that helps businesses earn media coverage and chat rank, which helps brands understand and improve the visibility in AI tools like ChatchukT and Google AI. Together, his work focuses on helping companies build their brands for the AI-driven future. John, what's up, man? Welcome to the show. Thank you so much for having me. Let's go back in time. First of all, man, tell me the first time you ever made a dollar that excited or shocked you. Where you were like, I can't believe somebody paid me money for this. This is crazy. It's my first job. All my friends decided they were going to either a bag groceries or move shopping carts at the local supermarket.

1:00Now, I was like, I was a kid that was obsessed with baseball. I played it. I loved it. I couldn't get around the game enough. And so what I did is as a 13-year-old, as a freshman in high school, I reached out to the local Little League organization and said, I want to umpire. When they were like, okay, how much you know about baseball? They were like, I start talking to the guys like, well, you know more than just what everybody in this town that does umpire of these games. And so they put me in umpire training school, which was a couple lessons. It was me and a bunch of 45-year-old dudes that I was literally like a freshman in high school. Nice. And they got me, I got me fitted for an outfit, and I started umpiring baseball games. They paid me $30 a game, which at the time was insane. My friends were making $5.50 an hour. But I was like, to me, I couldn't have had more fun. I was doing three games a day on Saturday. It was just the best time. But to me, it was like, and I'd say a different penny, but it was just to me like that. That was my first real job. And it was like, honestly, I couldn't believe they're paying me to do it. How long did you do that for? I did that all four years of high school. So that was like my high school job was, so I did, I progressed slowly too. So I mean, I was making $30 doing, you know,

2:0210-year-old games. By the time I was a senior in high school, I was doing like, you know, pretty intense high school level games, making $85.90 a game. So, you know, for me at the time, again, I was about a per 17 years old. That was pretty good. That was about a $45.50 an hour rate. So... Yeah, no kidding. Plus, you get to do it around something that you really love. I actually love that idea of doing that. Like, if you get started and feel like that, that young, you can make good money doing like real college games. And then obviously professionals make real money and get flown around and get to hang out with, you know, baseball players all day. It's a pretty cool gig. Yeah, it was actually kind of funny because I've got, I ended up going to college, was like, you know, studying engineering, applied math, like data science type work. I got really technical stuff. And one of the guys a year above me at my, in my program, he played on the baseball team, really, really smart analytical guy, his life streams actually had become a MLB umpire. And he did it. He actually did make it. So, I mean, it took him about 10 years of cruising through it, but he, he's named it Dan Brazel. He's an, he's an engineering umpire now. And he does, you know, he's on in the circuit.

3:03But his whole life's goal was to do this. I was so jealous because I was like, I got it, you know, after graduating to Wall Street, you know, got a real job. I mean, in New York, and it was like a very different experience. He was just like, you know what? I screw it. I'm going to go do this and he started making, you know, like, probably, I don't know, the dirt money doing minor league baseball games, but you know, worked his way up and now his name will be umpire. Wow. That's awesome, man. Yeah, I see that as like a really good potential for people who are listening, but like want to start a side hustle or something. Like if you're just a big sports fan and you love a particular sport, I can be a really good way to get a rep. My, my, my uncle refs in the pack 12. Yeah. For football, yeah. And it just gets flown around and, you know, he, he just loves doing it. He just describes it as like the coolest thing because he loves football and he gets to go watch the game. The field with all the players, you know, yeah, it's pretty cool. Anyway, not to make this entire episode about becoming a ref. So, so this was a high school job for you. You end up going to school. What did you go to school for? So I studied applied math and engineering at Johns Hopkins.

4:05So really focused in on like data science, like world optimization theory, probability, these really kind of intense math focused things to like, you know, for me, I was, I just loved solving problems to me. I was not about, you know, I didn't want to do mechanical engineering or civil engineering. It was too specific. Like I loved the idea of just an open-ended curriculum of just how do you like solve a problem. I actually took a class called math modeling where it was a graduate level class. And the entire semester, there was only one project. Your target grade was one project and it was ask a question and answer it. And it was just using data and obviously using the models when things we learned in school. But I mean, you had to really go dig deep and figure out how to solve a problem that was completely open-ended. You could do whatever you want with it. This was back in 2011. So at the time, I mean, data is prevalent now at the time. There was basically, it was stocks, whether or baseball. Those are the three data sets that existed for eForCollege kids. So as you can probably guess, I chose baseball. And I did basically started inventing, you know, saber-metrics statistics. I started inventing a, was called like a luck factor in baseball. But that kind of stuff to me was super interesting to just figure out

5:05how to just, I don't know, completely open-ended. And I love those kind of professors that kind of gave me that opportunity to do that. What kind of jobs do you get after graduating with a degree like that? Yeah, for me, it was kind of the natural step was to go to Wall Street and go make a financial analyst at a bank. And so it was not my life's calling. I made it about 18 months. It was not my true passion. But it was the, I mean, a way to get me to New York, get a head of pay to good salad. It was a way to kind of get started on my journey. But yes, that's a lot of people end up being actuaries. Actually, they go work for insurance companies and you know, do data analysis. That was too boring for me. I couldn't go that route. But I actually did have a lot of fun working with my analyst. There's a hierarchy of boringness. Yeah. My sister actually studied actual science. She was like, I love just being incredibly boring and working at AIG. And she's been doing it for 15 years. So good job. Maybe she's the smart one, bro. You know what I mean? Yeah. Great job because she likes to. Yeah, she's doing well. Okay, so you lasted for 18 months. What made you be like, all right, yeah, this is not for me. To me, it was the, I like the fast-paced environment.

6:07Like the entrepreneurial mindset of let's go build something. And so in my while being as a banker, like you're doing the XM&A dealers, you're researching companies all the time. I actually found a company in New York that was doing really interesting things at the time, called 1010 data. And they were really focused on. At the time, again, this are going to sound funny today because it's 2026. But like in at the time, it was all about, you know, big data analytics and, you know, cloud-based computing and these things that were really kind of interesting and unique in 2013. And so I joined them and kind of like, felt figured out like when you click on something and you have a technology that nobody else has and you're starting to go out and fly around to, you know, that are like Walmart target, major retailers, major banks, major companies and say, we have this opportunity, this capability to do this. It was just like eye-opening to me to try to realize what it could look like. So I joined the company there about 50 people or so. That's how I left for like 250. We had a massive exit for half a billion dollars to a family office. It was like, it was an amazing opportunity. But really what from you was interesting was just, again, like building, exploring.

7:08I go sit down at the PNG office in Cincinnati and having no idea really what PNG was. I was a kid. I was just like, oh, cool. Like you guys make tide. And then like realizing, no, no, this is really serious stuff. Like we need to figure out like the shave category. We bought your lead. It's just going to work for us. What's going on with this Harry's Razors or like Dollar Shave Club? Like are they real competitors? We're trying to like turn this data for them and figure it out. To me, that was just fascinating. I just loved going in there, problem solving, figuring out what they needed and going to build it for. I was with the call sales engineer. So I was like a technical person on the sales team. So I would go in, listen to their problems and live code in front of them in the meeting, like a prototype or like what this could be. And they would say, wow, that's amazing. And then like obviously we would then productionize and try to sell them a bigger package. But to me, it was just literally in the room, being able to hear them ask a question and then take their data in and just it and actually share back answers to them in real time. Again, this is about like this over 10 years ago. But at the time it was really, really cool. And to me taught me so much about sales, how to work a room, how like the politics of these kinds of things can work.

8:09Watching a company like shoot itself in the foot knowing full well that they should be buying this, but they just don't want to admit that they did something wrong. And these kind of things were like lessons from me as I grew as a salesperson every time. Yeah, with the data side, is this like their own customer data that you were pulling insights from? Or is this like market analysis data? What type of you know sets did you have? It was both. But a lot of it was their internal data and then merging it with external data. So we worked a lot of like grocery like anybody had a lot of data like think about every single scan of the grocery store. Every time you hear that deep, that's a line of data in their database to understand what people are buying, what kind of products are well with each other, how you can assort your products differently, all these kind of things all play into it. So they're really, really interested in kind of learning more about that. But it leads to more than just, you know, what should I sell? Like how many lines of how many like different catchups should I put on the shelf? So also like, well, I mean, we did like shrink analysis. They say like, are people stealing from you? And what are they stealing? That kind of stuff also was like a, there's a lot of things you can find in that data that you're not thinking about all the time. And usually are more reactive too, but if you can be proactive about it, it can be really interesting.

9:10So was that sort of like a gold rush of data? I assume like most companies have access to that type of data these days, is that correct? Yeah, they do. At the time and still today, it's like, does that data make sense? Is it organized in a way that you can use and realistically, you know, take advantage of? And that was still a problem like, dirty and messy data is still like an issue even today. But realistically, like actually being able to turn through it and figure out insights from it, AI has made that a lot simpler. Doesn't make it any better. Like honestly, if you judge your data, it's no good. But that was the time really kind of eye-opening to need to figure out. I mean, there are companies that just didn't even understand their own businesses. And they were obviously doing super well. But it's just like at the time, you kind of are like, wow, there's just real opportunity here. What kind of insight did that give you into your own like market research capabilities for when you decided to start your own thing? Yeah, I think to me, the number one kind of learning I got from that was just when you're in sales and again, I was like the technical salesperson. So I didn't have to like, you know, do the annoying like sign here type of work. I was like more, I was more like kind of getting them excited and getting them excited to go do that, that thing.

10:11But for me, what I would have realized and learned over that time is like, how people buy and why they buy is very, very, you know, different than what you kind of might expect coming into it completely not having been in this role before. And so you might think, oh, I have the best thing. Like they must have, they would have to buy it. It's like, no, no, like are you solving a real pain of theirs? Are you giving them something they don't have the actually need? But I realized a lot of times was like, I'll call it out. We worked with Trader Joe's at the time. Really cool company and we were basically showing them, hey, listen, like there's, you've had some kind of issues with some of your employees or some of like, you know, some of the, you're the things that the people that are working there might have been, you know, not so honest with some of the things that we're doing at the register. And they were like, that's not a corporate culture. We don't want to be spying on our employees. We're shutting this down just like turned it off. We were showing, we gave them an analysis of the show that they're going to make millions of dollars back and they were like, listen, we don't want to be like doing that kind of thing. And so for me, I was like, well, I showed you're going to make a million to dollars. But the internal struggle they'd have gotten from having the exposing that was not worth it. They wanted to keep it quieter and not go pay for a kind of a fancy toy to show them

11:12that. I was like, okay, that taught me a lot about how when you're selling something, you have to be realistic and like thoughtful about what is the other person the side of the room on the other side of the room thinking in that moment and how are you going to make their life better or like, you know, make them a champion, make them look really good. That's so much more important than the features or kind of widgets in your product. And that to me was like a huge eye-opening experience, especially talking to big enterprises. So how long between your like, let's say graduating from college and starting your first company? Yeah. So I took the approach of I wanted to soak up and learn as much as I could. Somebody at the time when I was like really young, kind of right out of college was like, your 20s are for learning, your 30s are for like making money. Like, you know, they kind of just soak up as much as you can. And so I spent the majority of my 20s essentially learning from incredible mentors and great companies that I work for to kind of go to all that. And then basically once I hit that stride and I basically turned 30 until I started my first business. So to me, it was like a listen, learn, observe, get as much knowledge as you can, this first call it eight or so years and then kind of go for it. And what was that first business that stole the business

12:13you run today? No, so it was a failed effort we had with a friend of mine too. It was back in the NFT run. So we were looking to kind of try to figure out ways to make a better fantasy sports experience. And so we built this thing. We were launching it. We were actually getting some good traction. And then a company that we had heard of was like kind of like hanging out around there that was not super big raised like $100 million to do exactly what we were going to do. And we were like two kids like had no funding. And we're like, yeah, shut it down. That was that was our first attempt at their first realization of, you know, we bootstraped every business since we have not raised ventures. So I really appreciate like the bootstrap mindset. I want to talk about that today. It is to me the best way to own your business own your lifestyle own what you want to do and actually make the most money truly. But at the time, it was also realization that if you you're a direct competitor to raise a hundred million dollars and you have none, like that's that's a hard that's a problem out of the client. Yeah, I more or less agree with you for the most part, but it does it does sort of take you out of the game of some of the existing potential businesses

13:16that you can play at, right? Like if you're going to pursue something like that and you're going to take the bootstrap approach, just recognize that somebody that does have a hundred million dollars in funding is probably going to beat you to market is probably going to beat you to a better product is probably going to beat you to more sales is probably going to have more, you know, market recognition. But there are the good news is that there are, you know, 80 plus percent of the businesses you could start do not need to compete in that area and can still be wildly profitable. What about that made that decision for you? Like was there an experience? Was there a story? What what made you make that decision for yourself that you were going to bootstrap everything? Yeah, so 1010 data that company was talking about the data company they bootstraped everything from day one. They never raised any venture and so when they had a five hundred million dollar exit watching like it took them 15 years to get there. But that half billion dollar exit when it doesn't go to and immediately 90 percent of it doesn't go to your BC and goes in the pockets of you and your employees. That's like a that's a really fun party. Like that's a really like kind of fun experience. Yeah, I mean, I learned in that moment again, it was New York's there's a little

14:19different kind of mindset, but it was very much we exist like today like we just tomorrow could be sold a deal today. Like you have to everybody's selling everybody's thinking about how did like grow get better improve and sometimes with BC you can kind of sit back a little bit like oh like my paycheck's good. The money's in the bank we're like set we'll just go raise more money. So it kind of it can do the I can by the way I'm not anti BC like it can absolutely like grow and like strengthen what you're trying to do and give you that boost to really go for it. There's a lot of ways I think that's amazing, but I've also seen they have the opposite effect. So after 1010 when I worked today, you know, that was a boot shop business. I then after my Delta 10 pre-worked it, I worked a BC back company and I watched just how that can go really wrong. Yeah. And how you know, the wrong mind thick and come come there. And so for me, having seen both sides of it to me, I was like, all right, my next business is going to be bootstrapped. And again, with chat rank right now, a lot of our competitors have raised like a ton of money. So like we we have boot shopped this from ground up. We have just truly done this ourselves. We have competitors that have raised $200 million. I can say that our product is like just I mean as good if I can say better

15:23than a lot of ways than their product is. That does a testament to how much more you can be doing with you know, agentecoding and building things today. Software and the features aren't really the differentiator now. It's the knowledge. It's the customer relationships. It's the ability to sell and go build things. That to me is a real difference. And so are do they have more revenue than us? Of course, they have an army of salespeople going out selling this thing. I wish them all might all of our well-funded competitors the best of luck. They're going to make us look good. Yeah. So to me, they're also going to expand the total market, right? Exactly. They're spending add dollars to increase awareness that this is some of the people who should be spending money on. Then those customers will have more awareness of your thing when you finally reach out to them and get you know, and if your product is better, then you'll probably be the one who wins. It's the winner-take-all market. Don't like you kind of have to go like you have to be the best. You have to maybe like go go be a little more aggressive. But in the case of chat rank like it does not have winner-take-all market. It's a massive market. There's plenty of room for plenty of companies here. And so to us, we feel like we found our niche. We found a really good spot in that. That's great. And we're not really competing with them again. I'm rooting for all those

16:24guys because they do well. That makes us look better too. Absolutely. Yeah. And it'll help price the market for a potential exit as well. If there's some money being exchanged in that world with exits, then it'll be like, oh, well, they priced it at 700 million. And so ours is objectively worth this much. From the perspective of the product itself, is this like an agency? Like what does chat rank actually do? What is the product? Yes. So we are in not an agency by definition because we're doing it with software. But the idea of an agent, like an agent, like a AI and software kind of doing the same as an agency is where we're focusing our time and effort on. So when traditionally, if you wanted to go do what we're like traditionally SEO or like, you know, like trying to win in search, you go hire an SEO agency who would do the things that you need to do. Frankly, because it was kind of a little esoteric, they knew what they were doing. You did it. They had the relationships and connections. They were hacking the Google algorithm. It all just like was always just paid agency and they do the work for you and they kind of done well with that. In the new world of AI discovery and AI search, the fundamentally how AI makes

17:25decisions and provides answers, their answer engines, not search engines is just different than what you know, Google's algorithm has traditionally done. The point that even Google's Gemini product is very different than like Google search and what they recommend at search. And so for us, we've realized is the software actually can figure out what you need to be focusing on and going after and being very, very specific about what your brand does uniquely well. The AI tools cannot be cheated. You can't really like trick them. What you can do is you can be really, really authentic to who you are and make sure they are very clear about what you do exceptionally well and you can go win that area. So we like to say that AI search is much more meritocratic in that way and that you can actually like, they'll give you the win, the answer to something that even if you're on page seven of Google, they don't care if you're the best answer to that question for that user, they'll do that. And so chat right helps identify and find those gaps, figures out where you're missing, but where you should be doing way better given the company that you are and then filling those in for you all the, you know, our AI agents and the software. Now, we also offer a dump for you plan because a lot of our customers work a lot with medical, e-commerce brand. They're just, they're not really focused on marketing. They just wanted like, you know, diving and going to run their businesses.

18:28So we do offer a dump for you plan, which is like, honestly, just we try to do those and train them along the way so they basically cancel our services and keep our software. That's our ideal scenario. But we're listening to most of our customers around just ourselves, our software. Okay, got it. And what type of, if you can give me any sort of metrics, you know, growth rates or revenue rates or users or anything like that. I'd love to hear anything that you can offer. Yeah. So what most people don't realize is that their brand is probably really good. It probably already has the information needed to win. It's just not optimized in the way these AI tools like to understand or like figure out what brands do. And so this is like the biggest miss, I think, for a lot of our customers. It's just like, you have the assets, you have the information, you have the customer case studies, you have the things, you don't, you don't have the reviews, you just don't have that written in a way in your digital presence. It's an optimized for that. So just by kind of just taking that that leap with us and going to do that, we're seeing, you know, five times more referral into the site, we're seeing e-commerce customers with 40% higher conversion rates for those customers. We've seen across the board, we've never had a single customer where users that are

19:28referred to them by a chat, chvt or an AI tool are worse than like average traffic. They are always converting it higher numbers than average traffic because think about it. When you go to AI, like you're doing your research off page and you land, when you land on the page, you're ready to buy. That's the reality of it. So if you can get more of those customers to your website by being in more answers, that's just going to naturally net it. So we see across the board, across the different case studies in different industries, it's just always true that those customers are more valuable. Yeah, well, it feels more like a referral when you ask AI. People have relationships with their AI. They call their chat, chvt page, a name, a friend of yours. So you know what I mean? Almost as like plays into that trust factor a little bit more when it's a buddy of yours telling you about a company versus just a random Google search or something like that. 100%. John, where do you think the, if the, if the gold in this gold rush is AI, where do you think the shovels are? Who's selling the shovels? Do you view this as like selling the shovel play? Yeah, exactly. So we're, what we are doing is we're helping brands do what they already are doing

20:31and just help them optimize for this group for this new channel of acquisition basically. And so for us, we're not changing how the models think. We're just reacting to how they think. Like we're not actually fundamentally trying to shift anything in the market. We're trying to do is say as a brand, you do this thing really well and you've always optimized your brand for humans. We're just going to help you optimize that brand for how these AI tools like to work. I was like to joke that like Google has been reading websites and is excellent at like reading web pages because they've been doing it for 30 years. Open AI and throttling their web crawlers are historically bad. They're like Google in 1999. They just don't know how to read a website. Don't that understand what you're doing? It's have to be very intentional and very thoughtful about how you feed them information. And so for us, like really it's just leaning into that more. So we're kind of helping these brands acquire the customers. They either were already getting or trying or maybe potentially losing because they're like going to somebody else. And again, like I said, you can't, you can't cheat the system here. If you do figure out like a little cheat code, it'll be going away in a couple days. Like this is not last. What you can do is be really intentional about your brand and try to go make sure that AI just stands who you are and you're going after and make sure that this customer is fine easily. So where did just reach out come from?

21:33Yes. That was for us. That was our fight for it to this was that we partnered with the original founder who was just kind of like really good at the kind of a managed service component of this, like really helping brands with their PR and then really turned that into like more of a software product. So we were able to come like partner with them and transition that into a more of a software brand. So the original founder Demetri, he's still super involved in what we still talked him a lot. We ended up buying him out of the business. So now we do own just reach out. But it's really been a fun experience just to kind of see and understand how again, it all ties with chat rank, but like how this all plays together of these external brand mentions really matter. If you are trying to get that that authority to to both Google search and for AI, if you say like I baked the best cookies in in Las Vegas, but like you just say that to do that people are people back in you up. But like well, three local food blogs all claim that you have the best cookies. That definitely helps you know when you're when you're trying to make that claim and then judge it be to say they have the best cookies because they say they have the best cookies and these three blogs all say it. So that that kind of digital PR and that that brand outreach that also really helps kind of play into this. John, I appreciate taking the time man. I know you're super busy guy.

22:34Working people go to get more from you and everything you're working on. Yeah. Take a look at us at chatrank.ai. That's the main way to kind of take a look at what we're doing. You could also check me out on LinkedIn or on YouTube. But realistically, like what we're doing is just helping brands to succeed and grow in this new channel. So yeah, let's please reach out. I'm always happy to kind of answer questions or be helpful in a way we can. Appreciate you John. Thanks so much for taking the time. Everybody else tuning in. Remember money only solves your money problems, but it's easy to resolve the rest of problems with some money in the bank. So let's start there here on the Travis makes money podcasting student in. Catch you guys next time. Peace.