When it comes to revolutionizing mobile development, few can match the vision and grit of Nia Christair, an expert in mobile gaming, app development, and enterprise solutions. Today, we’re diving into the incredible journey behind Minitap, an AI-powered platform that’s raised $4.1 million in seed funding and outpaced giants in the industry. In this conversation, we explore how two young innovators from rural France turned frustrations with sluggish mobile timelines into a tool that accelerates development tenfold, the personal moments that forged their partnership, and the groundbreaking strategies that are reshaping how companies experiment on mobile. From childhood coding lessons to surpassing industry benchmarks in just 40 days, Nia shares the challenges, triumphs, and insights driving this game-changing startup.
Can you walk us through your journey from a small village in France to building a platform that surpassed major industry players on a global benchmark in just 40 days? What was a pivotal challenge during that intense period, and how did you overcome it?
Honestly, coming from a tiny village in Burgundy, the odds felt stacked against us. We had big dreams but limited resources, and those 40 days to hit the top spot on AndroidWorld were a pressure cooker. One defining challenge hit about halfway through when our early AI model kept failing to adapt to real-time device testing—something even the big labs struggled with. I remember sleepless nights, fueled by cheap coffee, staring at error logs in a cramped apartment, feeling like we’d hit a wall. We tackled it by breaking down the problem into smaller chunks, rebuilding our testing framework from scratch, and running hundreds of micro-experiments until we got it right. It was grueling, but that moment taught us persistence and how to iterate fast under fire, which became a core strength for our platform.
How does your platform manage to slash mobile development timelines from six weeks to mere days, and can you share a specific example of this transformation in action?
The secret lies in automating the bottlenecks that plague mobile development—like device testing and cross-configuration debugging—that AI tools for web just can’t handle. Our platform uses intelligent automation to simulate real-world conditions, catch errors early, and streamline iterations, cutting down what used to take six weeks into just a few days. For instance, in a recent project with a mid-sized app team, we helped them roll out a new feature in under a week, compared to their usual month-long cycle. We started by integrating their codebase, ran automated tests across thousands of device scenarios overnight, and flagged issues with fixes before they even woke up. Seeing their disbelief turn into excitement when they shipped ahead of schedule—that’s the kind of impact that keeps us going.
Reflecting on your early days, you’ve mentioned frustration with how long it took to build your first viral mobile product. What specific pain points in mobile development drove you up the wall back then, and how did they shape your mission today?
Oh, those early days were rough. It took us two years to ship our first viral product, and every step felt like wading through mud compared to web development. The biggest pain points were the endless cycles of manual testing on different devices and the lack of tools to quickly iterate when something broke—unlike web, where you refresh a browser and move on. I still remember the frustration of a bug that only appeared on one specific Android version, costing us weeks to track down in a dim basement office, surrounded by a mess of cables and old phones. That grind lit a fire under us to build a solution that obliterates those delays. Our mission now is to make mobile development as fluid and fast as web, so no one else has to feel that same exasperation we did.
With mobile accounting for 60% of internet usage but lagging far behind web in speed, how does your platform empower companies to run more experiments on mobile? Could you paint a picture of a typical use case?
Mobile’s dominance in usage but slowness in development speed is a massive gap, and we’re here to bridge it by enabling rapid experimentation. Our platform lets companies test and deploy features at a pace they’re used to on web, which means they can run 10 times more experiments without the usual headaches. Take a typical use case with a consumer app like a meditation or learning platform: a team wants to test a new onboarding flow. With our tools, they upload their code, we run simulations across thousands of device setups in hours, not weeks, identify friction points with AI-driven insights, and let them tweak and relaunch in a day or two. I’ve seen product managers go from dreading mobile updates to being thrilled at how many ideas they can test in a month. That shift in energy—and results—is what drives us.
Your partnership with your co-founder seems deeply rooted in trust and shared history. Can you share a defining moment from your early ventures, perhaps while growing your first app to 10,000 users, that cemented that bond?
Absolutely, our bond was forged through some intense moments. When we bootstrapped our first app to 10,000 users at just 18, there was this one night that stands out. We hit a server crash right as usage spiked, and we were scrambling to fix it with zero budget and just our laptops in a freezing dorm room. I remember the tension, the way we snapped at each other, but then we just locked in—dividing tasks, debugging together, and laughing deliriously when it finally came back online at 3 a.m. That ordeal showed us we could weather anything as a team, no matter how messy it got. Today, that trust lets us take bold risks with our platform, knowing we’ve got each other’s backs through every high and low.
Drawing from your fascination with AI breakthroughs, how has that passion influenced the technical foundation of your platform, and can you walk us through a specific concept you’ve applied?
My obsession with AI, sparked by seeing what algorithms could achieve in complex systems, really shaped our approach. At university, I dove deep into reinforcement learning models, inspired by how they adapt through trial and error in dynamic environments. We’ve embedded similar principles into our platform to tackle mobile development chaos—think of it as teaching the system to ‘learn’ optimal testing paths across endless device variations. For example, instead of static test scripts, our AI dynamically adjusts based on real-time feedback, much like a game-playing algorithm hones its strategy. I remember the thrill of seeing our early prototype cut testing time by half in a lab setting, surrounded by whiteboards scrawled with equations. That’s the kind of innovation we’re scaling now to redefine mobile speed.
Your experience with large-scale infrastructure projects must have been a game-changer. How did navigating those challenges prepare you to address the complexities of mobile development with your platform?
Working on large-scale systems, like delivery drone infrastructure, was an eye-opener for handling complexity at volume. One major hurdle was optimizing real-time data flows across thousands of units—any lag could cause cascading failures. I recall a tense week troubleshooting a synchronization glitch in a noisy Tokyo office, with maps and data streams everywhere, until we rebuilt the pipeline for near-instant updates. That taught me how to design resilient, scalable systems under pressure, a skill we’ve directly applied to our platform’s ability to manage thousands of simultaneous device tests without breaking a sweat. The chaos of mobile configs doesn’t faze us because we’ve built for worse. It’s like training in a storm—you’re ready for any weather that comes.
Looking ahead, what is your forecast for the future of mobile development, especially with the integration of AI tools like yours?
I see mobile development undergoing a seismic shift over the next five to ten years, largely driven by AI. We’re on the cusp of a world where mobile apps can be built, tested, and iterated in hours, not weeks, making experimentation the norm rather than the exception. I envision small teams and even solo developers competing with giants because tools like ours will level the playing field, stripping away the resource barriers. But there’s also a challenge ahead—ensuring AI doesn’t just speed things up but also maintains quality and security in a hyper-fast environment. We’re committed to leading that balance, and I’m excited to see how this space evolves with more innovators jumping in.
