← Startup Stories - Mixergy
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- [00:00]
A
Hey there, freedom fighters. My name is Andrew Warner. I'm the founder of Mixergy, where I interview entrepreneurs about how they built their businesses for an audience of ambitious entrepreneurs like me, like you, like today's guest. So get this. Imagine you're running a company and people tell you use the data, right? The data is the answer for everything. But frickin a going and getting the data is often harder than it seems. And the bigger the company is, the harder the data seems to be. And so most people will say, okay, we'll run the data once a month or once a quarter or whenever we can. But to get it on a daily basis or even, frankly, instantaneously would be dramatically different for a business. And so that's what today's guest set out to do. He created a company that does that. I met him last Friday and I said, oh, these stories that you're telling me are killer. We gotta do an interview about how you built it. His name is Pavel. Oh, wait, let me make sure I'm pronouncing right. Dolejal. Dolejal.
- [00:51]
B
Perfect. Pavel.
- [00:52]
A
Dolejal company is Kabula. They use AI to make data more accessible for more use cases. Pavel, you were telling me about that stationary story. That's a client of yours. Tell the audience.
- [01:03]
B
Yeah, yeah. Hi, Andrew, by the way, thanks. Thanks for inviting me. You know, Mixer G is OG of podcast, you know, like, so, thanks. Well, the stationary company is actually called Mechpen. And the guy who actually runs him, he was working for the large, you know, enterprise, and they kind of like were shutting down the business and he bought, I think one or two shops out of them, and then he started to grow them. And then he knew that he needs to borrow money to grow them and then he needs data to run them. And he saw that in the enterprise, right? They had a huge team of dozens and dozens of data people to run the data. He didn't want that. He didn't want huge team of data people. And his vision was that he's going to teach every clerk, you know, every seller on the floor. And this is stationary business that they're gonna, they're selling bands, you know, rubber gums, you know, back to school, you know, items. And that every single person is gonna be a data analyst and it's gonna contribute to actually, you know, making business better. Kind of like when you read the stories of Walmart, some Walter, they would meet every Saturday and they would benchmark who is selling what.
- [02:16]
C
And.
- [02:16]
B
And he was like, no, everybody's going to do that daily. So he reached out to us, we built a project for him. We use Kabwa to integrate the data, we clean up the data, we set up automation so it would be always fresh. And then we set up dashboards in the software called GOO Data. And then it was kind of like one o' clock in the morning. I remember that very vividly because like we gave access to every single employee to dashboards. And he calls me like 20 minutes later. He's like, you sorry, you like crazy. This is not how you do it. Immediately turn it off. And I'm like, no, no, you wanted data democratization. He says, yes, but this is anarchy. If you want to democratize something, this is not how you do it. You need to have a change management plan. So he actually forced us to lock down the system. And for the first three months we worked only with him. After he started to understand it, get hands on, you know, then we expanded to his leadership. After his whole leadership started to understand it, we expanded to everybody in the company. Long story short, the company grew from five, you know, like five locations to over 60 in like three years. And he didn't, you know, have any VC capital, anything. He borrowed money from bank, he paid it back. They went through Corona, they went through Russian invasion of, of, of Ukraine and the hike in prices. Everything profitable. And what they do. This is like, sorry, this is like, you know, today we have a lot of AI clients. You know, everybody's crazy about AI, but a lot of cool businesses. But this is kind of like the most old school business in the world. They sell, you know, pens and papers and every day, you know, it's mostly older ladies, like 60 plus who actually sell there. They come to the office, they turn on their computer, which is their bus system as well. They log into the good data and they see the data that they sold yesterday and they benchmark with everybody else, right? So have a leaderboard and what the questions he told them, questions they should ask. So they're selling something, what would be a good upsell? So like when I, you know, when, when, when back to school is coming, is the, is it the best to actually upsell the rubber gum or is it best to upsell, you know, candy or whatever? And they compete with each other and.
- [04:47]
A
This is just, and that's what, that's what this up to. The minute data that's accessible to everyone can do. It means not only that, they get to see how these ladies stores are comparing to others and how they're doing as salespeople compared to others. They're also getting insights into at this point in the year today, if somebody buys this one thing, they're more likely to buy the other. So when you're selling the one thing, make sure to sell the other. The example you given me was back to school. When someone comes in to buy a pencil, ask them if they want to buy an eraser. And it's an overly simplistic example, but at scale, you're talking about real impact to their business. That's what you're making available.
- [05:24]
B
And this, this is so huge impact, you know, because, like, you can. You can literally double the profit margin just by these simple things. I always, you know, compared, remember? Kind of like it's like digging for gold, right? Either you go and you find a big, big nuggets, there's just a couple of them, or you have a river and everybody sits alongside the river and they just, you know, like do this. And everybody finds one small gold flake a day. But if you have thousand people and they do it every day, it's more than a big nugget a day, Right. And it's kind of like what you can compare the company to. You know, the river is your processes. People are everybody who works there and they're using data to get insights. And if they find one flake every day.
- [06:10]
A
All right, let's go back a little bit. You ran a huge portal back in the days when Portals actually meant something. Portal was like, supposed to be the place where a user would get onto the Internet and then from there figure out where to go. It's a portal called Atlas. You had a problem there that led you to launch this company. Atlas was what? I. I don't know Atlas. I've never been on the site before.
- [06:32]
B
We were. You would not be. Yeah, well, of course it was a clone of Yahoo, if you would say so it was. But yeah, of course.
- [06:40]
A
How big and famous did you get from it?
- [06:43]
B
Oh, it was. It was for Eastern Europe. So we were, you know, like, like very large in Czech, Slovak, number one in Ukraine. And so it was like 15 million people using it every single day. We actually had, you know, like online maps in 99. You know, like Google Maps was 2004. We had the online maps in 99 and it was awesome. But honestly, we didn't know how big it can get, you know, how big of a business it can be.
- [07:09]
C
And.
- [07:09]
B
And we sold it to Warwick and Pinkus a couple years later and was the investment company.
- [07:16]
C
Yeah.
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A
Did you get rich from that?
- [07:18]
B
Oh, I didn't. I had just A couple of percentage. But my friends who actually started that with me, they got.
- [07:25]
A
Yeah, because you were the chief product manager at the time, not the founder. Yes, got it. Okay.
- [07:31]
B
And then right after that, I joined two weeks late.
- [07:36]
A
Seriously?
- [07:37]
B
Yes, seriously.
- [07:38]
A
Okay. All right. And the problem you had there, and I know you've launched a few companies after that too. The problem, though, that you had there was what?
- [07:46]
B
Well, it was always, you know, I launched a couple of companies and they were all using data and machine learning to actually automate processes. And the problem with Atlas was like, 15 million people are doing some searches like, like in Google today, and I needed to understand, what do they search for?
- [08:02]
C
You know, like.
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B
And it took me like half a year, you know, to get engineers to build this view for me and to get all the data together so I would not, you know, like, bring down the systems.
- [08:12]
C
Right.
- [08:13]
B
And so when I saw this, the same problem, exactly the same problem in three different companies I built, I was like, it's time to change it. And when the cloud started and the whole SaaS industry started, the proliferation of, you know, SaaS data sources, it was just bigger and bigger problem. So I was like, this needs to get changed.
- [08:34]
A
I'm surprised. I get it. You're talking about with Atlas. 1998 is when the company was running. You were there till 2002. Okay. So back then, getting any kind of data was hard. Right. But you'd had other companies since then. We're talking about, what was the company that you had just before this one? You were at netmail to 2019.
- [08:56]
B
Yeah, yeah, yeah, yeah.
- [08:57]
A
But data was still too hard for you to get.
- [09:00]
B
Oh, yeah. It started to be even harder. I thought that when the cloud started, you know, it would be like, okay, but remember 2020-2012, it was the nobody knew snowflake. It didn't exist. And so the de facto standard of data was Hadoop technology. It was built by engineers for engineers. It was originally started off of the white paper from Google. Then, you know, like, Yahoo and Facebook started that and it was Apache project. It was like, so freakishly hard to do anything, so it started to be even harder. And so when I got together with my co founders, we were like, they saw the same problem from the consultancy angle. They had a small consultancy doing IT projects, and then more and more people wanted this data, you know, and so they started to build it together and they saw it. And so we started to actually get together and like, hey, what's going to change in next years? We're like, well, there's going to be, you know, somebody is going to figure out the database problem. It's not going to be hadoop, it's we're going to go back to SQL and it's not going to be one backend, it's going to be multiple backends. So yes, now we have redshifts, no flag, BigQuery, DuckDB, Iceberg, you name it. You know there is, there is like endless platform of backends, right? And then we were like well there's gonna be, we see this SaaS businesses, you know, taking over and there's gonna be, you know like proliferation of SaaS businesses within the enterprise. Guess what? Gartner says that there's up to 300 different SaaS tools in the enterprise business. 300. You know how hard it is to get data from two sources, just like 300.
- [10:50]
A
And third, look, the problem you're saying just to catch up on what you're saying right now, you're saying, look, the problem is that there's, that first of all the people who are stored, the software that's holding onto data is not meant to be user accessible. So most people don't have access to it. And that's something that's always bothered you. Everybody should have access to data, number one. And the second issue that you said is there's also now in addition to these data warehousing tools, there's also Hundreds of other SaaS apps that people are using and the data is stored within there. So yes, it should be getting easier but it's actually harder because of all those. And then you had a third reason. What's the third one?
- [11:30]
B
Yeah, the third reason was exactly. It should not be a magic, you should not be a part of the voodoo clan that knows how to SQL or Python. You know, like this is a business problem. So you as a person, tech savvy, tech savvy person in the business should be able to handle all of your questions, you know, and automations yourself, right? So that was our North Star. So that's why we built Kabula as an API first, you know, so we can abstract from the technologies and change them, you know, under the hood as they evolve. And B, we were hoping for something like LLMs to come out very early on. It took us seven plus years. So we were ready for LLMs back in 2016. And we're like there's going to be something that's going to help us to write these SQL Python queries so the user can just work in natural language. Yes, that happened Seven years later.
- [12:27]
A
Okay, by the way, you said not just get data accessible, but also do something. One of the discoveries that I've had over the last week when I've talked to different AI companies is they're much less excited about the agent thing than they are about the make data more accessible. So every time I talk to them, I want them to tell me about how AI will magically do a thing for me. And they say, no, no, Andrew. Like this company. Hi.
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C
O.
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A
He goes, real estate brokers have data in all these different places. They can't access it. So what they do is they text someone on the team and they go, can you tell me? And that's a waste of time and it takes forever to get the answer. So he goes, what we created was a text that you can. That, you know, like almost like a person on text. You text this number, you ask your question, you get an answer instantly, and we pull the data faster than anyone else. He goes, that's the exciting part. It's not what we could do with the data, it's can we actually make it accessible? And you're smiling the same way.
- [13:22]
B
Well, because that's the first problem. If you can, like, you know, for us, this has been known problem for years. So it's a hurdle. You know, like, you see all these nice demos with agents and everybody, you know, but like, if you look under the hood, what they are actually doing, they're, you know, using Excel or Google Spreadsheet and maybe Google Calendar, right? It's easy. But you know, in the reality is these guys, you know, we're telling you most of the interesting data is locked in the proprietary systems or, you know, specific SaaS systems, or they might have Salesforce with a different implementation, right? So that's hard and that's not sexy. You know, it's kind of like plumbing. But like, once you actually unlock it and like we do, you know, automated data pipelines, they would run for eight years, 10 years. Like, you know, there is a company, DXC, right? Like, like $16 billion a year company, you know, the original IT company. And they use us, you know, to run all of their sales and marketing data pipelines early around the globe. For last eight years, it just runs, right? So you automatically get the data accessible. Once you have that, you can start building those interesting use cases and you can then start automated processes with it. But unless you have, you know, data accessible, no magic AI.
- [14:47]
A
The other thing that's interesting is first of all that you're saying, first we make data accessible Then we act on that data. There's like an earlier step that I'm noticing come up over and over which is consulting. That as a consultant, not a software vendor, not a SaaS maker, those things are sexier. But as a consultant you get to go in, you have deep understanding of what the customer needs, you do it a lot by hand and then eventually you create software that systemizes it. You're smiling. Take me through the early part of the business when Kabula did that.
- [15:18]
B
Well, this is how we started. So my co founders actually had the consulting business.
- [15:24]
C
Right.
- [15:26]
B
They will be deploying their engineers. Before we all knew forward deploy engineering was a thing. And just like consulting with clients, building the data accessibility automations insights for them. And so I came from the second angle. I was the owner of the business, I had the issue. And so together we started to go around different clients and we did implementations ourselves. And by that we actually learned what are the hard problems.
- [15:57]
C
Right.
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B
And that's how we started to build Kibble. And that's the principle we keep, you know, up to today. We have you know, a couple of clients where we actually do implementations ourselves and we actually learn and we co design with them. One of the examples is one of the fastest growing unicorns in Europe which is called Rohli Group. They do grocery deliveries within one hour. Kind of like imagine Amazon Fresh actually working. That's Rohlik in Europe. Everybody's so used to it and so we are designing things together because it makes sense.
- [16:34]
A
Give me an example of the early days when you were just consulting or your partners were just consulting. Give me an example of a project that was done that then led to software understanding that that led to something that's usable by other clients.
- [16:48]
B
Yeah, yeah. So actually I will use the Rohly Group because like the guy who started that is this is his third company and the first one was a group owned clone for you know, six countries in Europe which was actually very successful and profitable. By the way. Interesting side note, Groupon as an original Groupon is now run by Czechs as well. You know some of our friends and client and is our client actually have bought in as a private equity.
- [17:16]
C
Yeah, yeah.
- [17:17]
B
I have redesigned the whole coupon and data using Kibula. It's a great story, you know and they are, they are awesome team how they are actually executing. But going back to Thomas Chuper is his name when he started his first company it was called Slowman and he like we found him on Twitter honestly one late night, you know he was tweeting, was like again, 2am It's a magical number. You know, like between midnight and 2am Things happen with founders. And he was like, I'm starting this company, I have my database. I have a real problem. It's a MySQL database. And what I need, you know, I need to connect my sales reports. I need to connect this and this. I don't know how to do it. It's like so hard. So we immediately, my co founder reached out to him. Next morning, we were on site and we started to discuss and he helped us to co design, you know, because he's very tech savvy, he's very business kind of magician. This guy's incredible. And you know, he's forward technologically. You know, he likes technologies. So together we designed that. You know, Kepler needs to be API first, right? So that abstraction layer I was talking about, that started with Thomas Chuper, meaning.
- [18:25]
A
He told you just do API, meaning just suck data in from, from other APIs.
- [18:30]
B
He didn't tell us just do API, but he started like, how do you want to use it? Well, have these sources that always change. And then we took it back to our team, right? And we're like, hey, we need to have an abstraction there. That's obvious, right? And then he was like, well, I know how to run the database. I don't want to do it because like, you know, like, it's just too much effort. I want just insights. So we started to run database underneath Kibula.
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C
Right?
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B
And so like these design principles, we literally started with him as a, as a client, and then he used us in three other companies because we designed together.
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C
Right.
- [19:12]
A
All right, I get that. I see it now. Let's talk about some of the challenges. So you built a business, then Corona hits, right? COVID19 what happens to the business as you're building it?
- [19:26]
B
Yeah. So before, before Corona, we were, this is my, I think, third or fourth company. And we wanted to be bootstrapped, you know, in the beginning so we could do what we wanted to do. We had this vision. We knew it's going to take a couple of years and we wanted to. So we started a business. We start to grow business pretty successful. I moved to US in 2019 and then, you know, we start growing in Chicago and Corona hits. So I need to stay in Europe. Honestly, we, we need to, you know, nobody knew what's kind of going. Going on. We need to lay off people in, in us because, like, we didn't have money, you know, to pay them. We need to refocus I don't know if you remember, but like in the beginning of Corona, the. The original or the first, you know, like death in Italy, there was a. There was a mortality rate around 5%. That essentially means like that. That's why there was such a panic in the beginning. That means that it would wipe out the population within a year or whatever.
- [20:28]
C
Right.
- [20:29]
B
And so we're like, well, okay, business is good, but we should help now. Now it's kind of like time how to help use technology to actually, to actually help the governments and people. And so we put out on Twitter again, hey, Czech government, do you want help? And before you know it, you know, six hours later, we are actually with the Prime Minister and the whole cabinet and they're like, we don't know what to do. You know, this is kind of like a technological play and, you know, contact tracing and everything, how to connect the data. And so we are, yeah, we can help you. We can integrate the data. And so we did. But then it was like more and more the governments were like totally not ready for anything. So we actually built a group of enthusiasts, technological enthusiasts, 5,000 people, 5,000 software engineers, and they started to help. Well, that sounds great. The only two issues was that we started to help with one thing, but then we started to see that they need more and more. And then we started to run, you know, with couple of friends, part of that government program for the government. And we couldn't get off because if we would get out, it would just collapse. And so we almost lost the business because like in the times where every tech company was selling and selling and selling, we were focusing on pro bono work. And it was great. If you look at the stats, in the first wave of Corona, Czech Republic was best in the world. But then the government people, they are not really. Yeah, it's strange. I. I would not want to work with government evermore. You know, just like it sucked us in so much. We're trying to change that system. And after almost half year, we had to say stop. You know, like our company is like almost going down and we are helping someone who doesn't want to help. So we went back. We actually, it was, it was, it helped us in a way that we actually started the BLG Motion, which we didn't have before. And since then, over 21,000 companies actually sign up for us. PLG Motion. We grew it. We started to grow it, you know, and now like last 12 months, we grew it three times. So like there's. You can always get something from crisis I think. But yeah, there's so many crisis building the company that you need to make sure that they don't kill you.
- [23:04]
C
Right.
- [23:04]
B
And you need to have at least plan B and C and everybody says go in, you know, for all. I think that's a great advice if it works out.
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A
Was the government grateful for all though that you'd given up for them?
- [23:18]
B
No, like, it was like we knew that like half year later there would be great articles how we fucked up everything and you know, go back and.
- [23:31]
A
Did you get those articles about how you screwed everything up?
- [23:34]
B
Yeah. They are online somewhere. You can. It's just like, you know, like there was 5,000 people involved, engineers.
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A
So you said that it helped you get to PLG product LED growth. What do you mean? It seems like you were completely in software. I mean it seems like you were completely in the other direction.
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C
Then.
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B
We have two legs, you know, like, like, like a human being two legs. They need to walk in the same rhythm, the same direction. So one, originally we started just like, like we didn't have sales originally.
- [24:12]
C
Oh.
- [24:12]
B
So we started just word of mouth and people recommending us. And you couldn't actually start Kabula online. You had to talk to someone. We had to start for you. And then after Corona we're like, well, it's great that we are getting these bigger clients. How are we gonna work with us? We don't have money now to invest, to go back to us physically. So how are we gonna work with that? So we're like, well, there's this PLG motion that people seem to do well. And so we were lowered. Our principle was lowering down the barriers for people to actually start using Karuro.
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C
Right.
- [24:53]
B
So we started like free account. We now have very, very, very generous freemium where people can actually run their businesses on that. We're like, well, this is our way to, to go back to us because like I don't need to be there for. To target people who are there. Right.
- [25:11]
C
So.
- [25:12]
B
And, and then it started to take off and it's, it's actually, I think it's a. The world has changed. You know, after Corona, you know, I don't think that people are buying now just you know, like by phone only or introductions or reference always. Especially in our industry there will be some one technical will want to try the software. Right. So that's kind of like even when we sell, you know, our. And our tickets are, you know, like mid tickets are 75 to 150k. You know, as a starting ticket we still have. We still have, you know like hundreds and hundreds of companies that pay us, that pay us by credit card, just like $20, $50 and they grow.
- [25:55]
C
Right.
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B
Or that there's thousands of people every, you know, quarantine joining who want to try it and then you can trace them to the companies that we actually do outreach to. I see.
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A
Let's talk about a bigger customer that you got. You, you had this shocking story about how you got a bank to buy from you because you couldn't get them to work with you. Why couldn't you get the bank to work with you?
- [26:20]
B
Well, imagine, right? Banks are one of the most regulated industries, especially in Europe, right. All the GDPR and things, we invented that as Europeans. You know, bureaucracy is our thing.
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C
Right.
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B
And so I don't think there is a hard customer to work with than a bank. But as well, you know, banks are great customers because everybody knows them, right. And they have great use cases, you know, especially retail banks, you know. So right now we are working with the Ersta bank and we are in over 85 departments. There's, there's hundreds of people that use us in 85 departments. But that took us almost 10 years to get there. So when we first started I was like, I want to have this bank as a customer, right. It's a very well known brand in Europe and etc. But like you don't get them as a customer like this, right? So we're like what kind of. They don't typically buy from startups. They are very risk aware. You know, they're, they have so much regulation, so much to lose that their first principle is not to innovate as much as possible. Their first principle is kind of like hey, like do it securely. We are a bank, we actually work with people money. So we need to be secure.
- [27:41]
C
Yeah.
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B
So, but there was a trend of cloud, cloudification. So I knew this bank from the company I invested in NetMEO and I was like, this is a great customer but they will not buy from us for a couple of years. So what we did, we, we actually approached them. We started to do community hackathons because like we wanted to get people excited about data. We want to show our platform and we wanted, we were hoping to get customers out of them. So we approached the bank with you know like the prospect of doing a hackathon. And so we used our data, we anonymized the data and together we did the hackathon for over 500 people. AWS was sponsoring, IBM was there, Google was there. Everybody took like four days, three new companies were started out of that hackathon and it was, it was just amazing. So we established the relationship and then, you know, we worked with them for a couple of more years, two, three years to actually, you know, really work with them, do proof of value to show them, you know. Then we did a lot of, lot of security. And finally in 2019, they signed the first deal. So exactly when Corona hit, we were starting the first implementation there.
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A
When you got that many people to participate in a hackathon, how did you get them?
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B
Well, just really being active in the community, so. Well, first being active in the community. So we did a lot of blog posts, Twitter back in the day. And then we started actually to work with other people. Like before Corona, the meetup.com was actually a very active website. Now it's not as much, right. So there would be people with different groups and we found out that they would have one thing, they would have some audience, but they would not have enough content.
- [29:37]
C
Right.
- [29:37]
B
So what we started to do, like, and before Corona, we actually ran London Data Enthusiast Group as well in London with several thousand people there. And like every two weeks I think we would have, we would have, we would have an event, you know, there would be like 150 people. Like looker.com when they launched in UK, the guys actually came to launch at our, you know, like at our meetup. It was awesome. But so that's. We would collaborate corporate with people who would have their small groups, but they would not have content. And we found out that it's very hard for people to get content and to organize. So we're like, okay, everybody has small audience, let's combine together. We will organize, we will do the content, we will prepare the data, we will do use cases, we will moderate. And it turned out pretty well. We've done this several times. The huge hackathons. And then we were like, well, this takes half a year to prepare, right? Great. Data hackathon takes a lot of time. So we were looking for a smaller concept which would be repeatable. We got together with the. Back in the day, 2015, 16, there was a big, was a big women in tech movement. And so we got together with one of the groups, Czech IT girls and we started, hey, you don't do data. They're like, yeah, we would love to do data content. So we designed a specific workshop with them and since then it's been taught to over 30,000 ladies. And it actually evolved into an academy which tried to be sponsored by Google. Three months program.
- [31:17]
C
Yeah.
- [31:17]
B
And stuff like that. It's great to work with community.
- [31:20]
A
I heard in your bootstrapping days these types of community things and hackathons ended up being your salesforce. How did you get other customers?
- [31:28]
B
Definitely how Honestly they would like people would invite other people. So like the participants are our North Star for good. Hackathon was not number of new clients. I remember our CFO back in the day, she was like, I need to understand what is the ROI on, on these activities. And like we don't know, like how can you do them? Like we, we, we have a theory or hypothesis and we are ready to, you know, put all of our effort in it and just like either prove it or disprove it and but we will, it will, it will take us half a year to understand.
- [32:09]
C
Right.
- [32:10]
B
And it's okay with us. And so the guiding principle was to provide the value for people to provide the value for community. People would be inviting people, other people to actually build their use cases at the hackathons. Right. So it was kind of like proof of value done on site. And that's just how people started to invite other people.
- [32:32]
A
So the idea was, I'm going to make a useful event when the people want to come. If they come, they might bring their, their friends and then all those people will get to know we're doing a Kabula. But not necessarily sign up right away.
- [32:45]
B
Yes. And yeah, our also our guiding principle was we're not gonna do prizes because what happens with hackathons when you do prizes, you have a professional hackathon hunters and they go and just swoop in. You know, they take the price there and they go off. That's no fun, right? So like no prizes. Well, actually you would have a price like ham, right? Like Spanish ham, you know, like a big chunk of ham or something like that. And that's still working pretty well.
- [33:24]
A
What's the revenue for the business now?
- [33:26]
B
We are approaching 15 million ARR.
- [33:29]
A
15?
- [33:31]
B
Yeah, 1 5.
- [33:31]
C
Wow.
- [33:33]
A
You went bootstrapping for so long with all these different ideas. Why did you decide to take some money a few years ago for scale?
- [33:42]
B
It's like we saw with Corona that one big, you know, black swan event, what it can do to us, right. And when we saw in 2022 that what, what transformers did with the GPT.
- [34:01]
C
Right.
- [34:01]
B
And how it's kind of like our vision that finally there will be something that can help us with the data and that these two things, we unlock the data potential, make it accessible. And there is the second part which can actually make it Easy to talk to that data, right? We were like, wow, this is, this is now, right? And so like, well, we had a huge discussion. Honestly, it took almost a year internally. So are we gonna lose our freedom? You know, like, you know, or are. Are we. Do we want to, you know, like that as many. Like our mission is actually to automate every single business process with data and AI. That's been for almost.
- [34:49]
A
So it's not even to make it accessible, it's to get to that automation point.
- [34:53]
B
Point, yes, because the accessibility is the prerequisite for it. That's what we identified as the hardest, you know, like thing. And, and that's where people fail. But only coupled with that AI, it can actually, you can make it accessible. People can get insights, right? And by getting insights, they can run their businesses better. But then what they want to do, they want to use the same data. The same data, right? The same systems to actually automate their businesses. And so that's what we saw with, you know, AI, that it can actually have that potential. And we analyze that. We need more capital for that, right? And so, and you know, a year and a half later, that vision is actually, you know, coming together. Because like right now with our systems, you can start with the question, you know, like then, you know, the systems, the LM systems actually help you with actually defining what data you need. Like, oh, I have a, I have my, you know, like, let's say it will be Mac Pen running on Shopify, right? I have my stationary business. I want to see which customers have not bought from me in the last 90 days, right? You input it in, it's says, well, you need the data from your BOS system. Oh, I'm using Shopify. Then Kabula helps you one click to integrate the data. And it says, where do you have your store? You know, like warehouse, you know, in this system, like Shipmonk, where it ca. Helps you in one click to integrate it, but then LLMs help you to join it together. You know, the auto magic, right? And then you can get the answer. But that doesn't end there. Then people are okay, interesting. Now I want to send it, you know, to, to. To something like brace for audience, you know, like, like automation. And then people are like, okay, now I want to run this every day. So that's when you build the agent, right? That's when you build the agent. Not, not before. That's kind of like putting the horse in front of the cart. In front of the horse. Because first you need to understand what are you solving, what is the process and only then you can build the agent. And you need to have data, you need to have quality data, you need to do QA on the data and you need to run the automations.
- [37:13]
A
So that's why most, before you run the automations, you need human beings to do it to teach you what needs to get done. Or is that not necessary?
- [37:25]
B
Yeah, before you run the automations, you know, like with systems like us, you actually like the human beings. Like think about it, how do you get to describing the process? Either you have some advisor or McKinsey guy coming in, analyzing the system and analyzing the process and then saying, this is it, this goes with huge project. The hard thing is business people don't usually understand how their processes work. So what we are trying to do or what we are doing, trying to do, we are flipping that people are very good in asking questions. So like who doesn't buy from me? Why?
- [38:05]
C
Right.
- [38:06]
B
And then, okay, I want to reactivate them. This is the tool I'm using.
- [38:11]
C
Right.
- [38:12]
B
And by the time you've done this, you know, you actually describe the whole process, right? You've done the work of McKinsey guy without actually knowing that. And if you have the system like Kabula, which actually, you know, keep trail of everything you've done, kind of like in that conversation, all the metadata, it's so easy for us to recreate that system, back that process back. And we tell you, this is the data you need here and here it will go here. This is how it's going to transform boom run.
- [38:45]
A
And the boom run is I, I now understand who has bought from me before but hasn't bought from me in six months. I know why they didn't buy, I know what I should do. And then instead of me doing it, I tell the agent to do it. And the agent might write an email that says, We've got this 15% discount on this thing that's very related to what you bought a few months ago. That's where you're going.
- [39:07]
B
I would just say we pair it with different systems. It's not all kibble. We are the underlying automation for the data.
- [39:14]
C
Right?
- [39:15]
B
But then you need, but you know, at the end sometimes you know, like you might even know that you need those systems or you don't even lock, you don't lock into the brace, right? You just send the audience there and other agent, you know, like actually activates that. But I'm just saying agents are not, not the last thing. I, I love agents. I think it's a huge Huge feature. But I mean like the big problem is to get a data accessible and B, describe the process.
- [39:43]
C
Right.
- [39:44]
B
So we turn it around.
- [39:47]
A
You raise your money in June of 2022. How much did you build by then? You had profits by then. You had a clear product by then. Right.
- [39:59]
B
So we raised a seed round in June 22 and by then we had a company. We had, if I'm correct, something 3, 3.5, $4 million. You know, like, like until then we were running the company to be profitable because like, you know, like we were bootstrapping or cash flow positive, you know, depending on the year. Yeah. And that, that's what we had kind of like and we had the basic platform.
- [40:26]
C
Right.
- [40:26]
B
And it was, it was, it was very well. It was very well. It was very well, you know, like tried by, by dozens of, by dozens of customers. And we're like, okay, time to scale.
- [40:36]
A
And it also had LLM in it. Like it was. You were already there?
- [40:39]
B
Not yet.
- [40:40]
A
Not yet.
- [40:40]
B
Yeah, we were. No, back then, in June 22nd. No, we were using. Back that summer we actually wrote the first integrations for GPTs. ChatGPT was I think one something and you could run a query to GPT through our orchestration. So. But you know what? You know what? Nobody wanted to use it. Nobody.
- [41:02]
A
It was like this was months before ChatGPT came out and like literally months before ChatGPT came out. You guys raised June 2022, November 2022, OpenAI came out with ChatGPT. People started to understand what this could do. They got to play with it and suddenly I'm imagining things took off for you there.
- [41:24]
B
Yeah, that's pretty much, that's pretty much the story. Yeah, that's, that's kind of like it actually it didn't, it didn't go that fast. You know, it took people one more year to start actually running the AI powered automations. So we have clients like Jim Beam, you know, like not Jim, like Jim Beam, like drink, but Jim as a gymnasium, you know, fit company. Okay. In 16, 16 countries. 16. And they went from zero to over 300 million, you know, like revenue bootstrapped. Awesome. Guys. Everything is run on data. They use Kevula as their operating system to get all of the data, everything. And so last year they were one of the first adopters of LLMs in the process automation. What they did, they had a 50, 50, 50 people team with the, the support, user support, like content support. And their biggest problem, like, like people give them reviews in 16 languages everywhere.
- [42:27]
C
Right.
- [42:28]
B
One of their biggest Issues was that people would, you know, actually spam them. So the competitors would spam their reviews. So they use Kabula. They use, you know, chatgpt within their, you know, workflow. Kebla automated that. And, and so we get the data from reviews, you know, we, you know, like normalize it in Kabula. They, you know, read ChatGPT from Kabula. It, you know, analyzed the data and then actually wrote the responses and then again run it through Kabula. And at the end, you know, they would have, they would have a human in the loop interface where people would say, yes, no, yes, no, or rewrite. They would go from 50 people down to three people. That's magic. But it's not magic. Like you need to be part of a secret cult. This is accessibility to everyone. That's my goal.
- [43:20]
A
Okay, let me ask you this. To close it out. If someone were looking to start an AI company today and didn't have your technical know how, I want to know what some of the opportunities are. What do you see? If you're looking out and you're saying, here's the opportunity, go run this. What are some of those ideas?
- [43:37]
B
Honestly, I've helped, you know, like, hate it or love it. I think the vibe coding is like, huge, huge opportunity. I've helped literally dozens of people this year to actually start their own companies. And it always goes like this.
- [43:53]
C
Oh, very well.
- [43:54]
B
Like this guy. I don't. I'm actually working for non profits. You know, like, we don't get too much money, but like, we have this issue, you know, like we don't know what grants are being done when, you know, and the old systems are too clunky. I know exactly what to do. I just don't know how to code it. I teach him, you know, I show him lovable. I show him Kurzar. And like, literally, you know, over a Sunday, he builds an mvp and on Monday he goes and he shows it, you know, like in some, some convention to, to. To other nonprofits. And he literally on the spot, gets 10 people to sign up that they will pay for it. And, and so, you know, like, I think the, if you don't know, if you don't have technical things, what is your, what is your actual secret? Is your vertical knowledge, you know, and that's, that's, that's, that's the thing, you know, coding at least to mvp, not production, but MVP is like now easy. And anybody can go and just like prototype what they want to build and they can sell it. You know, I have literally next to me. Like, I have two friends who built businesses over last year. One of them is now 10 million ARR. 10 million one person vibe coding app. And the second one is. Is doing 3 million, you know, and just like time is now. Honest.
- [45:19]
A
Would you introduce me to them so I could do interviews with them?
- [45:22]
B
Yeah, of course.
- [45:22]
A
Very happy WhatsApp.
- [45:24]
B
Yeah.
- [45:25]
A
All right.
- [45:25]
B
I would. WhatsApp.
- [45:26]
C
Yeah.
- [45:26]
A
Hell yeah. I would love to do a series of interviews with people who were inspired by what you've told them and then ended up building businesses that couldn't have been done before.
- [45:34]
C
Yeah.
- [45:35]
A
All right. Thanks so much for doing this. I'm excited to get to know you better. I love that we're now on what I. Dude, I hated WhatsApp. But now the more I'm talking to people outside of the US and especially in this world of AI and agencies, the more I'm on WhatsApp.
- [45:51]
B
Living there, it's kind of like addictive, right? And then. But then you go WhatsApp telegram, you know, signal, and it's just like. But yeah, I don't know how I would be living without it actually now.
- [46:04]
A
Me neither. All right. Hell yeah. Kabula.com. thank you. Bye, everyone.
- [46:09]
B
Thank you.