Profitability Before Funding: How One Startup Did It
episode
Startup Success: A Podcast for Founders & Investors
33 min
1 speaker
8 chapters
transcribed 15 days ago
Transcript
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Transcript generated automatically by AI and may contain errors.
What problem does Rwazi’s decision‑AI platform aim to solve for enterprise teams?
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.
Welcome to Startup Success. Today I have a very exciting founder in studio. We have Joseph Rudokangwa, who is the co-founder and CEO of Rosie, a really exciting AI startup that I can't wait to get into. Welcome, Joseph.
Thank you so much for having me.
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 Rosie 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.
Yep, yeah, I agree. Thank you so much for having me today. So, as you said, on the Kofana Silvazi, we are a decision AI platform helping enterprise teams drive growth. And, you know, the problem that we we're solving for these teams is in three parts. The first one is helping these teams capture, retain and grow market share. 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 five hundred, Fortune one hundred companies. And yes, you can apply the same technology across different company sizes, but we started with the largest companies are going backwards.
And these companies, you know, they're either established companies that have been around for a long time. They have, you know, a hundred thousand employees, uh, they have press like product and services across so many domains. And the challenge there is where you're having these new entrants, whether it's startups or like mid-market companies coming up and chipping away at market share, like slowly, the legacy analytics tools and and software tools and even like There are people still using like PDF reports and Excel sheets, right? They can't even if you have data, even if you have like, you know, 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 capturing, you know, it's like struggling in their own, you know, 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 a 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, um, 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 are like four layers that helped our evolution into getting to this decision AI platform.
And that was, you know, 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 you get hit by all these competitors and all these. when you had the data, but the problem is the data wasn't plugged into you know 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 was solved 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 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
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Chapters
8 chapters
1
What problem does Rwazi’s decision‑AI platform aim to solve for enterprise teams?
0:01–5:08
2
How did Joseph’s multicultural background shape the founding vision of Rwazi?
5:08–10:04
3
What are the three layers of decision‑making that Rwazi’s AI addresses?
10:04–13:42
4
Why did Rwazi choose profitability before seeking external funding?
13:42–17:54
5
How did Rwazi acquire its first customers and achieve early revenue without a pitch deck?
17:54–20:55
6
What role did the Texas accelerator and corporate networks play in scaling Rwazi?
20:55–24:44
7
How does the Sena copilot turn market insights into actionable execution steps?
24:44–29:04
8
What advice does Joseph give founders about building a business around the customer?
29:04–33:45
Speakers
1 identifiedMore from Startup Success: A Podcast for Founders & Investors
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