Profitability Before Funding: How One Startup Did It

Intro 00:01
Welcome to Startup Success, the podcast for startup founders and investors. Here, you’ll find stories of success from others in the trenches as they work to scale some of the fastest growing startups in the world, stories that will help you in your own journey. Startup Success starts now.

Kate 00:17
Welcome to Startup Success. Today I have a very exciting founder in studio. We have Joseph Rutakangwa, who is the co-founder and CEO of Rwazi, a really exciting AI startup that I can’t wait to get into. Welcome Joseph.

Joseph R 0:38
Thank you so much for having me, Kate.

Kate 00:40
I’m so excited you’re here because what you’re doing is so neat, the way you’re leveraging AI for companies.Give us a brief overview of Rwazi, and then I want to get into your background and what led you to founding it. But I think if we learn, if they learn from you what you’re doing, it sets the stage nicely.

Joseph R 01:00
Yep, Yep, I agree. Thank you so much for having me today. So as you said, I’m the co-founder and CEO of Rwazi. We’re a decision AI platform helping enterprise teams drive growth. And you know, the problem that we’re solving for these teams is in three parts. The first one is helping these teams capture, retain and grow market share. The second one is helping them decrease the cost of acquiring customers. And then the third one is helping them increase the lifetime value per customer. So a lot of the companies that we work with are large companies, so Fortune 500, Fortune 100 companies. And yes, you can apply the same technology across different company sizes, but we started with the largest companies and are going backwards. And these companies, you know, the established companies that have been around for a long time, they have 100,000s employees. They have products and services across so many domains. And the challenge there is where you’re having these new entrants, whether it’s startups or mid-market companies, coming up and chipping away at market share, like, slowly. The legacy analytics tools and software tools and even, like, there are people still using PDF reports and Excel sheets, right? Even if you have data, like your data, internal data, like transaction data, customer feedback data and so on, social media data and such, it’s very fragmented. Everyone has their own dashboards. Everyone has their own view. Everyone is like struggling in their own bubble. And on top of that, it’s very difficult to actually have data that’s coming directly from consumers on top of all the data that they have. So data isn’t the problem. The problem is, then, do you have decision systems internally as your large organization that are helping guide executives to make the correct calls? And for us, that’s the problem we set out to solve, which I can get into a whole story of how that happened. And when we set out to solve for that, we ended up finding out that there’s like four layers that helped our evolution into getting to this decision AI platform. And that was the first layer was, what’s happening in the market. I have the data, I have transaction data, I have customer feedback data, but still, you get hit by all these competitors and all this. And when you had the data, but the problem is, the data wasn’t plugged into your design system, system that was just designed for decisions. So what’s happening in the market was, like the first layer of the problem that we solve for. And the second layer was then, well, now that I know what’s happening in the market, you know, what does it mean for my business? So this is a combination of both ingestion and zero party data. So ingested data is like data that you already have as a company, and then zero parties data we capture ourselves from your direct consumer activity, and that gives you a picture of like, what’s happening and what it means for your business, for your services and the products in relation to what’s happening in the market. And then the third layer was that we started getting from a lot of customers as well. Now that I know what it means for my business, how do I, you know, what should I actually do? Right? Should I go back and pay these consultants half a million dollars, a million dollars to kind of give me PDFs and PowerPoint? The human approach to solving that problem isn’t scaling, right. It doesn’t account for the, you know, massive variation of context across different people within the organization, across all consumer types, right? So we built our AI, which is called Sena, based on the dual context: the context of the consumer, and the context of the consumer to be able to tell the executives the correct next moves to make, this is what’s happening in the market and what it means for a business, and taking into account their jobs, their roles, their functions, their budgets, their constraints, and then generating simulated outcomes based on that. And then, of course, naturally, the follow up question was, Well, how do we actually go about executing this, next moves, the recommended next moves, And that’s where our copilot helps with execution. So we’ve been around for four years, and right now we are doing very well. We have a lot of customers, a lot of so it’s a lot of time crunching and absorbing this level of scale that we’re in. And I’m very proud of our team and that has helped us get to this point.

Kate 05:37
Incredible. I mean, so many things stood out to me that you said, for someone who’s in Growth, the ultimate problems you’re trying to solve are huge for a company, but then the fact that you and you nailed this so perfectly, there’s so much data out there. And you’re right, like in these big organizations, everybody’s looking at their own dashboard, their own spreadsheet, you know, they’re working off different things. And getting to the actionable insights is a huge leap. It’s really difficult, so the fact that you’ve solved that is incredible. I can see where that would be hugely useful in these organizations. So tell us about your background and what led you to this, because this is no easy feat.

Joseph R 06:24
Yes, yes, yes. Well, I am originally from Tanzania. So I was born in Tanzania, but I grew up in many countries. I call myself like a third culture kid. It’s a thing. So I was born in Tanzania, and when I was a kid went to boarding school in Uganda, and, you know, went back to Tanzania, but was home schooled, then went back into the school system. And then when I was 17, the US State Department used to run a program called the US experience study program that was taking high school students from many countries around the world to the US to go through high school in different states. I ended up being in that program because they’re taking like top top students, like number one, number two, students at like, the best performing schools. So when I was finishing high school, you know, my aim was to get a full scholarship to go to university and become a software engineer, and, you know, get a great job and so on. But that didn’t happen, you know, with life, so that didn’t happen. I got, like, presidential scholarships and all that stuff, but none of that could, you know, like US private investors say, $60,000, $30,000 scholarship of $40,000. How are you going to cover the 30k? And my mom was a primary school teacher at the time, so, you know, she couldn’t afford that gap. I ended up going back to Tanzania, and I found myself in this loop of, you don’t have a job, and people can’t hire you because you don’t have experience, but you can’t get experience because you don’t have a job. You know that chicken and egg situation? (Yes) The way I broke it was by volunteering. I was like, Well, the only thing I have to do is deliver value right to someone else. So I started doing what we used to call zero budget community development projects. And there’s a long story there, but long story short, end up joining the Youth of the United Nations Association. With now the UN I was able to rally and get a lot of – my angle was not a lot of young people finish school. They don’t get jobs, but there’s resource there’s a massive resource base, which is land, right? So parents to donate their land, and then I can get all mobilize all these young people to start working the land, who can raise money, have water pumps. Do irrigation? Do like, short cycle agriculture, sell and then have a flywheel. So that worked. Did that for three years, very, very successful. And, you know, with the State Department programs, ended up going to Indonesia to, you know, become like a professional trainer for people who come from US Department exchange programs, and they go back to their home countries to be able to run diploma projects. So I did that. And in between, like in the middle of all of that, there’s a opportunity at Lehigh University for a summer program in this is in, like, Bethlehem, Pennsylvania, and I wasn’t qualified. This was, like, for people who is of the undergrad, they’ve worked for like five years, like mid career people and so on. But at the time, you know, the State Department guys were like, look, you’ve done absolutely excellent things with very limited resources. So you know, we’ll give you a full scholarship if you get admitted. So I applied, got it, was one of the youngest. (Congratulations) Thank you. Thank you. So end up finding myself at Lehigh. Now at Lehigh in that program that was my first exposure into the world of business. And up to that point, I had already been in this world of like development programs. I had already seen that, you know this, this flywheel isn’t effective because it’s highly dependent on donations and aid, and you can’t actually build like true economic development through these means, right? So my thinking was, once a Lehigh, my thinking was, well, if I work and consult for these large companies and help them grow their businesses, grow their supply chain, their global supply chain, and consulting so on. That will actually help smaller businesses, mid market companies, so forth, expand, hire more people and so on, and that kind of truly catalyzes the global economy. So I started doing consulting after Lehigh with multinational companies, US companies, European companies. And my job was, I was like, I became very, very good at this specific niche, which was, we’re an international company, let’s say US-based, and we have these international markets. This is our current product and service lineup. Which of the markets that we have should we invest more in, with what product in our existing service lineup or product lineup, at what price point targeting, what consumer base, through which sales channels to deliver your targeted ROI. And as you can imagine, if you’re doing that kind of work, the first thing you need and the thing you need the most is data.

Kate 11:26
Yeah, exactly. You can’t make that decision, yeah, you need some data to back up those decisions.

Joseph R 11:32
Exactly, exactly. So the data. Now, these are large companies, like multi billion dollar companies, they do have data, but the problem that I found was, well, companies have plenty of first party data, but it’s heavily fragmented, and first party data has massive gaps in environments where things like online transactions and such aren’t synchronized. People then are struggling with all this first party data, the struggle becomes, well, I need to know intention. I need to track intent so I can figure out what are the drivers of value perception and increasing spend and increasing frequency. But you can’t figure that out if you don’t have consumption. You can’t map consumption in consumers’ default activity, you know, routines. And then where do you turn to? Well, you turn to third party data sources. So I did that for like eight years. I was the guy who would go on and hire these research firms and consulting and pay, like, $5 million. And what fascinated me in that world, and I started when I was 21, and what fascinated me was like, so did I just pay half a million dollars for these guys to make phone call interviews, yes or no questions.

Kate 12:55
Right!

Joseph R 12:57
To just tell me, Oh therefore, this is what’s happening in China. And I’m like what, like, did you really let me get this right, is this what’s happening. Is this how people are making revenue? Like, you’re making revenue, you just have, like, how people do it in movies, you have a focus group, and they show movies. What do you think? I’m like, is that it? Because, like, how do you verify? How are you validating? If you ask someone when’s the last time you had a Coca Cola or Chobani Yogurt. Do you use Xfinity? Like, for instance, this mobile service. Like, no, no, no. I’ve never heard of it, I only use Verizon. But how do you know, how do you validate that stuff? So for me, it was fascinating, and like, fascinating how loose and the heavy reliance that people have on that calls. You’d get all these reports you paid like $5 million, you get a massive report from a consulting firm or research firm, and then none of the executives who were making decisions, would actually reference the reports to make decisions. (Oh, really?) Yes, because the report is actually saying nothing, right?

Kate 14:01
Yeah, good, yeah, that’s true.

Joseph R 14:04
Now the executives, they would use experience. Look, I’ve been running, you know, the European market for, you know, 25 years, and you make some phone calls, and, you know, network, and then just make a call.

Kate 14:17
Just their gut instinct, right? Okay.

Joseph R 14:21
And I was like, Okay, I need to build models that would have a higher level of accuracy in stimulating which moves would be more profitable, right? And I started doing that internally, so that was my thing towards the last leg of my eight years of doing that. And I found it doesn’t work, because every time you’re building a model, it’s confined within the data that the company has, that bubble of that company. Along the way, one of my, you know, companies that I worked with sponsored me through undergrad. So paid for my entire undergraduate experience. For undergrad I didn’t pay a cent. (Incredible) Yeah, thank you. My college, you know, corporate sponsorship dream, you know, happened. And in undergrad, that’s where I met. Eric, who is my current co-founder. And Eric was doing capital markets in finance, in the finance world. And he was having the same thing, because his job was, like, more, more of analyze companies, and say, which companies to buy, and so on. And it was the same problem, which is, if I want to buy a company, these guys are financing acquisitions and so on, how do I truly assess if this company that I’m acquiring is going to be a best fit among the other competitors? This is the one you should buy, and this is the one that will drive your revenue to X. Where’s the data? How do you put it all together to actually have a correct next move and a good, more accurate simulated outcome. So we’re like, okay, we need to build this, but we need to build it from the ground up, starting with the actual first ingredient, which is for the decision system we build it needs to heavily reference the most accurate data. It needs to be trained on the most accurate data. So the route we took – this is all the way back in 2021 – was we want our AI to purely be trained on zero party data. So zero party data is data voluntarily shared, in our case, voluntarily shared by the consumers and captured within the consumer’s default routine. It’s not nudged, it’s not pushed. And the data itself, you’re not asking the consumer anything. Consumers are just capturing images, video, audio, and you have a text interaction with our AI in their default routine. Now that data, the data is highly validated and verified where you have, you know, timestamps, geostamps. We built our own proprietary, extremely tight you pay pipeline there of validation, verification, because we work with we had to account for context, right? Which is, are you training your AI system just based on US consumers? That’s a terrible move, because how is it going to, then analyze your Chinese consumers and Brazilian consumers and European customers and so on. So for us, we had to go with, like, massive range of contexts, from the get-go. So trained on, like, millions of consumers, 190 plus countries, 6000 languages, 40,000 devices, just from the get-go, and you have to, you have to verify and validate across all of those nuances. Now, once, as we built up our data pool, this is 2021, we, you know, we perfected our model, like, Okay, this, I will run the model and tested it, and, okay, this is it. So now let’s go to market. We made a mistake of thinking like, you know, the usual California thinking of, Oh, I’ll start. I’ll, you know, have this pitch deck and raise pre seed, build the product, raise. You know it doesn’t know, it didn’t work for us. We didn’t raise pre seeds, right? So we didn’t, we didn’t succeed pre-seed. We used our own savings from consulting, went dry, so went broke. And okay, so now that we’re broke, do we go ahead and launch the company? And how would you do it if you don’t have external funding? Oh, well, the answer is very simple. This, before it’s a tech platform, before it’s AI, before you have all of that stuff, what is it? It’s a business. For business to work, you need a customer. That’s it. It’s very simple, right?

Kate 18:32
True, true, yes, yes.

Joseph R 18:36
So we incorporated a company on June 16, 2021. Went to our prospective customers, and we’re like, Guys, this is what we’re building, a decision AI system. But we’re not there yet. We don’t have the resources. Now, what do you guys need as the absolute bare minimum for you guys to start subscribing? Like, great. Now we don’t care about, you know, the bells and whistles and a nice platform and with logins and multi factor authentication. What we actually want is if you guys can tell us what’s actually happening in the market, but with actually verified and validated data points directly from the consumers. That’s all. You can deliver it by Excel. You can deliver – it doesn’t really matter. This is how low the buy is. There’s so much demand that the buy isn’t how beautiful your platform is and your signup process. The buy is just like, can you possibly get this data right? Right? We got our first subscriber in August of that year, and we started making revenue. By the end of the year, we’re profitable.

Kate 19:45
Wait, that’s incredible, by the way.

Joseph R 19:48
Yeah, yeah. We had profitably fast. Now we were paying ourselves like nothing to get to profitability, but we got to profitability. And then the problem became Okay, now we have all these customers, and the question is how do you increase your customer volume, if you don’t have external funding to hire a sales team, right, and processes? We looked for hacks, and we need to join accelerator programs, but we need to find an accelerator that would have a network of corporate customers, such that if we tap into those, we’re able to acquire them at low cost, you know, with a very low cost of acquisition, and then scale. Looked at all the accelerators in the US, and Techstars was like, the winner, right? Techstars is the biggest corporate network, and it’s like heavy B2B. We looked at which Techstars program. There are so many, so which one? And now I have lived in Iowa, I’ve lived in Pennsylvania. I do not like the snow.

Kate 20:49
That’s understandable.

Joseph R 20:51
I need nice weather at least. Also, we looked at the Managing Directors of each Techstars accelerator program. (Oh, smart) to see which managing director has a background that would help us. And the LA program, Matt Kozlov who was the main… (oh yeah) You know Matt? (Yeah) So Matt’s background is very, very extensive experiences, he was a consultant at Bing. And I was like, Oh, this guy will get us exactly.

Kate 21:25
He’ll get it. Yeah, that was very smart.

Joseph R 21:27
Thank you. So I just cold emailed Matt. At that point, we’re making good revenue, so we didn’t have issues on that front. Cold emailed Matt, you know, gave him the stats, where we’re trying to go, why we’re reaching out to him, why Techstars LA? He responded, you know, we got on the first call, and he’s like, Great. He just interviewed us. He asked us a bunch of questions. He saw it, like he saw exactly what is going to be like years to come. And to me, that’s one of the things that I like when I’m speaking to a prospective investor, is like, can you actually see where I’m going without me explaining it? (Yeah, can they see the vision?) Yes, you see the vision. Matt saw it like immediately. So we got into Techstars LA and by Demo Day, our revenue was already, like, $1.2 million of ARR. (Wow.) So just hold it. And the thing we did different when we went to Techstars, we didn’t go there trying to raise. If people wanted to give us, like, to invest in our pre seed or whatever. But we were focused on customer acquisition, just customer acquisition. And the program is very intense, so it’s easy to work on the business and forget to work for the business. So we needed to pause, which was very intense, and we’re like, 1.2 million in ARR, we did the Demo Day video. In the first two minutes they were, like 4.5 million in bids, like seed funding bids. So we run our seed process very efficiently. Eric was driving that, and we ended up doing a seed round in 2022. And our seed round works, like Bonfire Ventures, absolutely the best VCs you can ask for. Now our problem at that point, we’re making so much revenue without solid systems behind the revenue to support it. So we went into firefighting mode, hiring all the function heads to scale the company and put in systems and structures. And at that level, in our like, problem architecture was still solving the problem, what’s happening in the market, and we had a lot of customer requests on X, like, doing write ups, or, like, doing some writeups to tell them what it means for their business. And you’re a SaaS company, you can’t, because that’s like a service based, kind of like request. So that drove us into expanding our products to not just, you know, uncover signals and tell customers what’s happening in the market, but do the next layer thing, which is now tell them. So you do a marriage between zero party, you know, signals you capture from live consumer activities, you marry it with what’s happening in the business, so the data that the business already has. When this is talking to each other, and you have dual context on top of it, then the AI system can tell you what this means for your business. And that was 2023. In 2024, as we had more expansions, now people started, you know, customers started telling us, well, we want to know that you think is what’s happening to our business. And we know, you know, like things are going certain things are going bad and so on. Now, can you tell us what we need to do? And none of us on our monthly or quarterly business reviews can start doing that because then, if you’re analyzing using a human brain, you just, you just filtering and including your biases, prejudice and experience and all that stuff on top of the data. So we evolved Sena, which is our AI, to actually tell the executives exactly what are the low hanging fruit things that you can do? So in Sena put in, you know, my role is, let’s say, VP of, I don’t know, like Commercial, my budget is 10 million, 100 million dollars per year, I have these KPIs. These are my constraints. So now tell me things based on my position. (That is so cool.) Thank you. Thank you. And if you’re a social media person, like manager, you don’t have $10 million. maybe you have, like, I don’t know, half a million dollars or 100k. Say I only have 100k, I only run socials, I’m targeting Gen Z, you know, this is my problem. We have a quota coming up, and I’m 40% of my quota targets. What should I do? And Sena just tells you, right? And gives you, like, simulated outcomes. And you can, you know, keep updating that. And eventually, this year, we expanded to add the copilot, which actually, you know, helps you with, actually, how, how to go about executing that. And now you can connect your other, you know, working apps to be able to have Sena kind of do low hanging fruit executions for you. This is trained and will, you know, we’re wondering, right, how it is Fortune 500 companies coming to Rwazi, and you have all these giant hyperscalers, all these, you know, more established AI companies that have raised way more than us. We just raised our Series A this year. So how are these customers coming to us and spending massive amounts in ACV, where you have all these much larger AI platforms? Well, the difference is, as we learned from our customers, they want the certain, the philosophy of builds and training has to be based on, like, extreme reality, like actual, actual reality. Not scraping the internet, not, you know, using copyrighted data and ending up with lawsuits right, which customers care about a lot. It’s just pure focus on is your system trained on actual, real consumer activity? Is the massive volume of consumer activity data based on, you know, like taking into account all contexts, across all markets, all consumer types and so on. Is it passive formats and so on, right? So that’s the advantage, the advantage we had been building for four years, you know, without knowing, or not knowing much on how it would, it would monetize in the future. So we’ve ended up with this, you know, very, you know, multi stacked, you know, like unique advantage that now is helping us grow. We’re now past like 20 million of ARR and flying high and so forth. So, that’s a short, long story of how we got here.

Kate 27:41
Incredible, incredible. I mean, I love this copilot. What did you call the name of it? Yeah, Sana. That’s, I mean, what an incredible tool for these executives that are using it, right? And to see all the different scenarios. Just the way you described your whole trajectory, from the idea, to Techstars, to the different layers, to customer acquisitions. Incredible. And you really explained so clearly to us the benefit of your solution. Incredible. Can you share with us an example you don’t have to name the company, just like one example that would, I think that would be really helpful.

Joseph R 28:27
Yes, yes. So, for instance, for instance, we have, so we have customers across many B2C verticals. But, you know, the one I wanted to use was a mobile service company, right? So you have a lot of wars in like mobile service right now, like, oh, people are fighting on, like, data and how do you bundle? So, in the Telecom world, how do you optimize existing bundles to land more customers, to poach customers from your competitors, and to have your customers increase their spend and frequency over time and so on. So, you know, this mobile service customer came to us, just inbound, and they told us all their, you know, the current challenges and struggles in the markets. They are getting their lunch eaten by one of the competitors, who is, oh, I can’t name, but you can imagine, the new service who is eating people’s lunch, right? So these guys were like, okay, so we want to use your platform to actually tell us exactly what people are using right now, the kind of bundle configurations they are leaning towards, and just track the activity to map the actual drivers and what’s pulling them towards that. This is surprising, even for me. Always I get surprised with these numbers. In six months, they had like 40% growth in their speed of a position, right? So 40% growth in the speed of a position. It was like for us, subscriptions are normal for them, like subscriptions like 100x there, right in six months. I always get shocked with, like, how much, like, how like, how big the effect is. For me, I’m a value oriented person. If you’re subscribing, you know, we’re an enterprise platform, I want to make sure you’re getting, like, 100x plus value. It’s surprising how much value our product generated for that customer. So that’s driven expansion. And one of the investors, so this is a giant Telecom company, one of the investors has invested in multiple Telecom companies, so ended up taking us to the other Telecom company, so we end up getting other customers just from.

Kate 30:47
Wow. Okay, that is the best compliment ever of your work, right? That really is, and the fact that you are surprised by the impact your solution is having at these enterprise companies, that’s incredible.

Joseph R 31:05
So we use ourselves. Like we use Sena.

Kate 31:08
I bet. That is so incredible. I mean, the way you’re leveraging AI is so cool. We’re actually coming up on time. I can’t believe it. You’ve walked us through so much. We always close with one question. Advice for other startup founders listening that you can give them.

Joseph R 31:29
Acquire customers first. Acquire customers first. Center the entire business around the customer. Center the entire technology solution around the customer. Because what I’ve seen now is, yes, you have like AI is extremely powerful, right? You can point you towards physics, you can point you towards material science, can point you towards health. Just apply AI in all the, all so many places right. Now, I am personally, you know, against having a massive AI company, you know, but making $0 right, right? I’m towards any application that you build, is it solving a real problem? How do you identify what a real problem is? You just look at where people are spending. Individuals, where are they spending? That’s the only way you can define a problem. So this is where people are spending. So therefore, these are the problems. End point, invest your know-how, your innovation, creative thinking into building products that are alleviating that massive need for customers. Use that route of there’s a need, x people spending, I like building products and solving problems. I’ll solve this problem and alleviate these needs, and then it’s referring this way. I think that loop, being centered around what the customers are experiencing and helping them solve their problems. You know, in the end, we’re all rendering, you know, service. We’re in service for other people. And I think, you know, centering your entire philosophy around that would be very useful for founders and anyone who wants to do business.

Kate 32:12
I really like how you explain that. I think you’re right. That makes a lot of sense, and you outlined it really clearly. Thank you for that. Thank you for taking us through your whole journey. I mean, for the founders listening, there’s so much you can learn from that. You did so many smart things, really. I mean, I think you guys are on a trajectory that’s going to be supercharged. For those listening that want to learn more about Rwazi, where did they go?

Joseph R 33:40
Rwazi.com. Corporate customers, individual founders, anyone can find us on Rwazi.com.

Kate 33:48
I love it. I can only imagine how busy you are based on everything you’ve shared with us. So thank you so much for your time today. We all learned so much from your story. Thank you.

Joseph R 33:58
Thank you so much for giving me a platform. I really appreciate it. This was wonderful.

Intro 34:05
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