What inspired you to cofound Olympia Tech, and how does Olympia Tech differentiate itself from other software consulting firms in the industry?

After leaving BCG, I was invited to join a startup in my hometown, Rotterdam. Even though I had worked across the Netherlands and internationally, I had never had the chance to work in my home city. Starting something in Rotterdam had always been in the back of my mind, so this opportunity felt like the right one. I joined the startup as CTO, where we were building a cloud-based platform for corporate lending—admittedly not the most glamorous space, but an important one.

We were developing software for banks, insurers, and legal advisors to facilitate large-scale corporate loans—loans of €100 million or more—that typically involving contracts that were hundreds of pages long. Our goal was to automate and streamline that entire process.

What excited me about this experience was the contrast to my previous roles at BCG and another major consulting firm. There, I had worked with large corporate clients and big budgets. But this startup was scrappy, fast-paced, and full of unknowns. We had no rigid processes, no defined roles—just a small, determined team trying to build something ambitious.

In that role, I worked closely with the team on product architecture and strategy—figuring out how to design and build a tool that made the lending process easier for clients. Through that experience, I connected with the startup’s investors, many of whom had multiple portfolio companies. As I got to know them better, I started noticing a common challenge across their startups: they were struggling to find the right engineering talent.

These companies were still working to find product-market fit. They had tight budgets and couldn’t hire large in-house teams like corporates could. I remember, at the time, I had posted 10 job openings to grow our engineering team in the Netherlands—but no one applied. Startups can seem risky—they don’t offer competitive salaries, and there’s always the fear that the company might not be around in six months.

We started looking beyond borders for talent, particularly in Eastern Europe. I partnered with a longtime friend from Bosnia who also ran a company there. We discovered that not only was it easier to find skilled engineers in that region, but the cost structure also made much more sense for startups.

It started small and grew organically. We began matching Eastern European engineering talent with startups in the Netherlands and Belgium, building dedicated engineering teams for them. That was the beginning of Olympia Tech.

What makes Olympia Tech different is that we’re deeply embedded in the startup ecosystem. We work with companies that are building innovative, often disruptive products. We don’t just provide engineers; we act as thought partners. We help shape product strategies, define architectures, and guide scaling decisions. For example, one portfolio company approached us for help expanding beyond the Netherlands, and I was able to draw from my corporate and consulting background to advise them on how larger players operate and scale.

What I find most fulfilling is bridging the gap between business and tech. I love working with engineers, but I also understand the business context—how to serve clients, what the product needs to do, and how to structure it for success. I often act as the “translation layer” between the two worlds.

Today, Olympia Tech is part of a larger venture group called UNETI (pronounced “Unity”). It’s a venture capital firm that invests in early-stage startups. Olympia Tech serves as the flexible engineering arm for these companies. And my own role has expanded: I now also assess new startups from a technical perspective when our group considers investing.

So while Olympia Tech started as a solution to a talent and execution gap in the startup world, it has grown into something more: a bridge between product vision, engineering execution, and strategic growth. That’s what drives me every day.

Out of curiosity, why did you choose “Olympia” for the name?

It’s actually rooted in history. When we were brainstorming what to call our own company, “Olympia” stood out. The name originates from Mount Olympus—the home of the gods in Greek mythology—and it carries a sense of strength, resilience, and aspiration. For us, Olympia symbolizes the kind of team we want to build: flexible, agile, and high-performing. One week, we’re working on a project in one industry, and the next we’re tackling something entirely different. That ability to shift gears quickly, almost like athletes adapting to a new challenge, is central to who we are. In that way, the name “Olympia” really captures the spirit of what we do.

How does Olympia Tech stay ahead in emerging technologies such as GenAI while helping its clients in their digital excellence journey? What challenges has the company faced and overcome in this space?

In my view, there’s a major shift happening in how we build and use technology—especially when comparing what companies have been doing historically with what’s emerging now. Traditionally, businesses, including many startups I’ve worked with, were focused on building SaaS products—essentially tools to make people more productive. These tools were designed to support users and streamline tasks.

But with the advent of AI—and more specifically, generative AI—we’re seeing a massive evolution. Just two or three years ago, tools such as ChatGPT, Claude, and Gemini started entering the mainstream. And for the first time, many of us really believed in AI. Not because we were told it was powerful, but because we could finally see it in action. It moved from something theoretical to something tangible and impactful.

Now, when it comes to SaaS platforms, I believe we’re in the middle of a paradigm shift. We need to ask ourselves, “Why are we still building tools just to make people a bit more productive?” If I give you ChatGPT, sure, you might become 10%–25% more efficient by offloading tasks such as content reviews or quick evaluations. That’s great. But we’re already entering the next phase—where AI agents can take over entire workflows.

This raises a fundamental question: Why build tools for people when you can build agents that do the work—while people simply orchestrate the process? For instance, imagine building a website. Instead of manually coding and adjusting colors, you could just tell the agent, “I want this, this, and that,” and it generates a draft. You say, “Make it green instead of red,” and it updates. No coding. No dragging and clicking. This shift is significant, and it will disrupt many white-collar professions—from coding and marketing to finance, law, and accounting.

We’re still in a transition period, and tools have a role to play. But the near future is about moving from incremental productivity gains (like 15%) to exponential ones—10x, even 20x productivity. To get there, companies will need to fundamentally rethink how they operate. How do you empower your people to become orchestrators while letting AI handle the grunt work?

From my experience—including working with BCG and various corporate organizations—I’ve seen that simply giving people powerful tools isn’t enough. Many employees aren’t from tech backgrounds, and when they try tools such as ChatGPT, they’re often skeptical. “It doesn’t work for my use case,” they say—and that’s a fair observation.

But I think this comes down to a lack of readiness. I like to use a metaphor here: imagine a man digging trenches with a shovel. He’s got rhythm, technique—he’s comfortable. Then his boss brings him a shiny new excavator to speed things up. At first, the man’s excited. But once he starts using it, he’s overwhelmed—too many buttons and levers. He digs too deep, damages a fence, and wants his shovel back. The machine wasn’t the problem—the lack of training was. Once he learned how to use it, the excavator made him 10x more productive. And now, one person could do the work of 10 or 20.

That’s exactly what we’re seeing with GenAI. The tool is powerful—but people need to be trained, even certified, to use it effectively. And that changes the whole dynamic of organizations. If 1 person with GenAI can do the work of 10, how do you redeploy and upskill the other 9?

Right now, the focus should be on how we put GenAI into people’s hands—effectively. How do we make it intuitive? What are the best use cases? What tools are out there? Over time, I believe companies will shift toward building their own agents, because that’s where the true acceleration lies.

My role is to help clients see this future—and take the first steps. I encourage them to start small: look at what you’re currently doing and identify use cases where GenAI can add value. Don’t get bogged down in complex engineering or messy integrations right away. Start with use cases that AI is naturally good at—such as language processing.

For example, a customer sends an email about a damaged product and requests a refund. Traditionally, a human would read and interpret that email, decide that the customer is right, and initiate a refund. That’s a perfect job for an AI agent—it can interpret, assess, and act.

This reminds me of the early days of Google. My parents tried using it, but they were frustrated because they asked full questions. At the time, Google only understood keywords, not natural language. So they gave up. We’re in a similar moment with GenAI: users need to understand basic prompt engineering to get value. But that will change. User interfaces will evolve, and the experience will become more natural—no need for detailed instructions or manual context setting.

Still, we’re not quite there yet. Right now, it’s similar to where we were with Google 10 years ago. So a bit of training is still necessary—to truly unlock the potential of these tools.

Having worked in both consulting and entrepreneurship, how do you navigate the intersection of both industries at Olympia Tech? Are there lessons from your time at BCG you have carried through your career that influence the way you lead your team today?

I learned a great deal during that short but intense period at BCG. It was one of the most formative phases of my career. I had the opportunity to work on a wide variety of challenges, and I look back on that time as incredibly valuable. That’s why I always enjoy connecting with fellow alumni or current BCGers—it’s a shared experience that stays with you.

One of the key takeaways from BCG was how to approach complex problems. You’re often dealing with issues that don’t have immediate or obvious answers, so learning how to structure your thinking and work through ambiguity is critical. Another big one was change management. It’s not enough to just give people a new tool—you also have to address behavior, processes, governance, and mindset. Without those elements, real change doesn’t happen.

Of course, working at BCG means you’re surrounded by incredibly sharp, driven people. Everyone is highly capable and motivated, which can spoil you a bit. When I transitioned into the startup world, the challenge was figuring out how to translate those consulting lessons into a very different environment. I had to learn how to communicate those ideas in a way that resonated with engineers and other team members. It wasn’t just about the frameworks—it was about inspiring them, helping them see the bigger picture of what they were building and why it mattered.

That’s probably been one of the biggest shifts: adapting those lessons to a startup setting, where dynamics are totally different. Not everything from consulting applies directly. In a startup, you’re working with more diverse teams in terms of backgrounds and working styles. Especially in the Netherlands, where there’s a strong preference for smaller, more dynamic teams, people often enjoy the freedom and risks that come with startups over larger corporations. That creates a unique culture you need to understand and navigate.

What’s stayed constant is my focus on vision and clarity. Whether I’m leading a team or working with clients, I try to break things down into clear steps, themes, and a coherent path forward. Back at BCG, we often worked with senior leadership teams who didn’t need—or want—to get into the technical weeds. It was more important to present the big picture in a way that was easy to grasp and act on. That’s a skill I still rely on heavily today.

People sometimes joke about the infamous “slide culture” at consulting firms, but honestly, there’s value in it. Learning to distill complex information into a single, clear message per slide really teaches you to simplify and clarify your thinking. It’s not about the slide itself—it’s about how you craft the story, how you convey importance, and how you guide decision-making. That’s a skill I use constantly, both in how I mentor my teams and how I collaborate with others. It’s one of the most impactful things I carried forward from my consulting days into what I do now.

Reflecting on your time at BCG, do you have a favorite memory or tale from the road that captures the essence of your experience?

I remember a time when we were asked to do a case for a ministry in the UK—Brexit-related. A full program had been set up for this aspect of the transition, involving several major companies too—there were a lot of people involved. The pressure was intense. The deadline was looming, and there was real anxiety: are we going to make it? That urgency, that intensity—it was so typical of many of the cases I worked on. There was always a huge time crunch. You were expected to move mountains in a very short amount of time. Often, we were brought in after other top firms had already tried and failed. Then BCG was called: Can you parachute in a team of consultants and just make it happen? Fix it all?

I remember flying in for this project—it wasn’t in central London, but a small city south of Manchester. We booked accommodation quite late and ended up staying in a themed hotel—something out of a historical novel. The rooms were strange and quirky. Mine had an antique bed and creepy vintage portraits on the wall. It was bizarre. But that’s part of the lifestyle. You get dropped into a random place, often at the last minute, and you have to figure everything out—where to stay, how to get around, and most important, how to quickly build rapport with the client team.

That’s the first challenge: connect with people fast, understand what’s going on, and figure out why the situation is the way it is. Then, once the client team heads home to their families in the evening, our real workday begins. You take everything you’ve gathered during the day, make sense of it, and turn it into something actionable—usually working late into the night. That rhythm—day and night, day and night—is what defined many of the cases I worked on at BCG.

There was always so much uncertainty. So many people had already tried to solve the problem. The stakes were high. The timeline was tight. It was complex. But I loved it. It was an adventure—not just professionally, but personally. It required a lot of resilience, adaptability, and fast thinking. And it came with a deep sense of responsibility. If things went badly, it wasn’t just reputational damage—it could genuinely impact people’s lives. That weight was real, and many of us at BCG carried it willingly. We thrived on it.

And there were always these surreal little moments. For example, the client’s office would close at 7 PM, so we had to find somewhere else to work. One evening, we ended up in a wedding function room at the hotel. There were booklets lying around with wedding decor ideas while we were making slides, cracking jokes, and trying to get everything ready for the next day. It was ridiculous and hilarious—but also completely typical.

That’s why I say that flexibility is everything. Without it, you wouldn’t survive.

In addition to your core role, you also advise other firms—primarily venture capital firms investing in greentech, medtech, and fintech. Are there insights or learnings from that VC work that you find transferable to your work at Olympia, and vice versa? More broadly, do you find value in cross-pollinating ideas across industries?

Absolutely! Over time, I’ve built up a kind of toolbox of technologies through my exposure to a wide range of industries. Having worked across so many sectors, I’ve developed a broad understanding of various tools and technologies and how they can be applied.

Whenever I take on a new case, I make it a priority to speak with domain experts. I want to understand how things really work—whether that’s the process of developing a new drug or what it takes to build a medical device. In medtech, for instance, safety is paramount. Products must undergo rigorous certification processes and clinical trials before they can enter the market at all.

My approach is to deeply understand the domain first, then look into my tech toolbox to identify solutions that might accelerate progress in that specific context. It’s about building a bridge between technology and domain expertise. I don’t need to be a specialist in every field, but I do invest significant time and effort to understand each industry well enough to make informed, effective decisions. It’s an approach that has proven to be very impactful.

Not everyone is naturally inclined toward that kind of curiosity—it can be demanding. Many engineers, for example, prefer to focus purely on the technology. They’re brilliant at coding and often wait for someone else to define what needs to be built. But I challenge that mindset within my teams. I tell them, “We’re going to do things differently. We’re going to understand the domain first.” Because when you understand the “why” behind what you’re building, you’re far more likely to get it right the first time. You avoid costly mistakes and create better, more effective solutions—whether it’s software, hardware, or something in between.

In recent years, I’ve spent a lot of time diving into the medtech and greentech landscapes. Take the solar energy situation in the Netherlands, for example. There used to be strong government subsidies that encouraged adoption of solar panels. But due to recent political changes, those subsidies are disappearing. Even though solar panels are more affordable than ever, many people are now hesitant to invest. In some cases, users actually have to pay to feed excess electricity back into the grid—so the incentives have completely flipped.

This shift introduces major market risk—up to 80% of it could be affected. On top of that, we’re dealing with net congestion: an oversupply of energy at certain times, and undersupply at others—like when people get home and start charging their cars or using appliances. It’s a serious imbalance.

At Olympia Tech and UNETI, we’re developing an energy management system to help utilities and energy providers handle these fluctuations. By integrating battery systems and smart controls, we can better manage home devices. For instance, we can temporarily switch off a heat pump during peak hours, knowing it retains heat or cold for hours anyway. By making devices smarter, we help stabilize the grid.

A big part of my role is understanding these industry dynamics and translating them into actionable tools and strategies. I also make it a point to bring domain experts into the conversation with our engineers, so they know not only what to build but why it matters.

I absolutely believe that insights from one industry can be applied to another. I see this all the time. For instance, we’re currently developing several mobile apps, and they tend to require the same basic features: user onboarding, support buttons like WhatsApp and others. These are components we can reuse rather than rebuild from scratch each time.

It’s the same with algorithms—many are transferable across clients and sectors. I’m constantly looking for these kinds of cross-industry connections. Internally, we also document our projects thoroughly and actively share that knowledge. At our campus, we organize knowledge-sharing sessions with portfolio company founders and their teams. These sessions are powerful opportunities to cross-pollinate ideas across industries.

We put a lot of effort into learning from one another and reusing what works. That mindset really multiplies our impact.

What would you share with fellow alumni tech entrepreneurs or those looking to follow a similar career path?

If you're anything like me—someone who enjoys working with diverse clients, on different themes, and with people from varied backgrounds—then my biggest piece of advice is to connect with others in the space. For example, here in the Netherlands, I’ve been speaking with many individuals involved in AI—whether they're building AI companies or integrating AI into their existing businesses. I’m trying to understand where the industry is headed, and I’ve found that engaging in these conversations is incredibly valuable. There’s so much to learn from one another.

Many people tend to focus on competition or worry that certain industries are already too saturated. And it’s true—spaces such as engineering, consulting, and technology are competitive. Olympia Tech, for instance, operates in a highly saturated domain. But I believe it will become even more competitive because we’re at a pivotal moment—one where AI is beginning to drastically expand what’s possible.

So, my thinking is, what will be needed in the near future? What happens when, in two years, many of the tasks we do today are handled by AI agents? How do we adapt to that? How do we make that transition? Because it is inevitable—and I’d rather be among the frontrunners who shape what’s coming than be left catching up.

At Olympia Tech and UNETI, I’m pushing these conversations forward. I know some people are skeptical. Yes, AI is powerful, but it’s not a silver bullet. It can’t do everything. Still, it will have a profound impact. I try not to “convince” people—it’s not about winning an argument. I simply show examples, real-world use cases, and possibilities. If that inspires others to get curious, to explore, and even to help drive this transformation alongside me—then that’s a win.

I’ve got a strong background in AI. I had a professor of natural language processing who used to say, “If I could make computers talk and write like humans, I wouldn’t be here—I’d be out changing the world.” Back then, we talked about how machines needed to understand the world like humans do—building mental models, ontologies, knowing that this is a phone, that is a table, and so on. But what we’ve seen since then is that there are alternative approaches—some of them feel almost like brute force—but they’re working. And honestly, my professor and I couldn’t have imagined this path back then.

To me, this feels like the invention of the internet all over again. That’s how transformative AI is. And that’s why I’m vocal about it—we should be moving in that direction, together.