Community Spotlight: Lennard Kooy

Building Lleverage, AI automation, and the search for product-market fit

Every month, we spotlight a member of our community. At KickstartAI, we believe that meaningful progress starts with individuals — engineers, researchers, founders, and practitioners — who are applying AI in bold, practical ways. Through our Community Spotlight series, we celebrate the people shaping the future of AI in the Netherlands: their work, their challenges, and the lessons they’re learning along the way.

This month, we’re featuring Lennard Kooy, co-founder and CEO of Lleverage — a startup making AI-powered automation accessible to everyone. From building with natural language to navigating the search for product-market fit, this is his story.

It’s been less than a year since Lennard Kooy co-founded Lleverage — an end-to-end AI platform that automates complex business processes without the need for developers. A serial founder, Lennard has built four SaaS startups to date, but Lleverage is the first one that has a 100% AI DNA. 

“In all honesty, I think that if you're starting a SaaS company now and it’s not an AI company, there has to be a very good reason for it,” Lennard says. 

He believes that, in five years time, 80% to 90% of what developers currently write in code will be created using natural language — making AI a fundamental technology for future SaaS startups. 

The birth of Lleverage 

The idea behind Lleverage came from firsthand experience. At his previous SaaS company in marketing technology, Storyteq, Lennard was already experimenting with generative AI. It felt like a natural fit: GenAI excels at language and visuals — the backbone of ads. 

But when it came to actually integrating this technology into the product, Lennard and Badr Eddial (at the time, Storyteq’s co-founder and CTO) ran into a problem. Their team of developers struggled with the non-deterministic nature of AI and new concepts like RAG pipelines and vector databases. 

“When we left Storyteq, we thought this was quite an interesting problem,” Lennard says. “So we started out with making a platform to bridge the gap between traditional developer skills and the power of AI.” 

While Llevarge was born to simplify AI workloads for developers, the focus quickly expanded to a business audience, such as product owners and business experts. 

The pivot followed early conversations with customers. Lennard and his team realized that, by automating business processes using just natural language, Leverage could directly cater to non-technical teams in need of technical solutions. The traction in their deal pipeline was significantly quicker with COOs and CEOs compared to CTOs, supporting this change in direction. 

Under the hood 

The first thing that Lleverage built was a workflow engine to facilitate data flow in automations. The team decided from the beginning to differentiate the company from chat interfaces like ChatGPT or Gemini and focus on the data processing pipelines rather than the end-user interface. 

Lennard explains that the startup’s core technology is the workflow engine — primarily built with JavaScript and powered by Natural Language Processing (NLP).  Underneath this engine, there are multiple building blocks, from various AI models to vector databases like Weviate. 

In essence, Lleverage offers “describe-and-build” AI workflow solutions. This is what it calls “vibe automation.” 

Such an architecture doesn’t come without its challenges, according to Lennard. The complexity and volume of data processing in user-defined pipelines create significant technical demands. Behind the scenes, Lleverage has to ensure data integrity, handle retries for external API failures, and manage the non-deterministic nature of AI outputs — all while keeping the experience smooth for the user. 

But the most complex problem Lleverage has faced so far isn’t a technical one. 

The greatest early-stage challenge: finding product-market fit

“The biggest challenge for a company in our stage is finding the right product-market fit,” Lennard says. “It’s an iffy concept.” 

“When do you feel that your product metrics are giving you the signals that your positioning is right for you? Or when do you think that there’s not enough real traction and you need to keep changing and do another iteration on the product or the business proposition?” 

“It’s a constant struggle,” he adds. 

Lennard also stresses that the search for product-market fit influences every part of the business: from the business model and the pricing strategy to the type of talent needed and the overall go-to-market motion. 

“When transitioning to a sales-led enterprise play, for example, you need to think of a different organizational structure, with a focus on customer success, solutions engineers, and dedicated account executives,” he says. 

He compares this to a product-led growth strategy, where the focus shifts towards growth marketing and community management. 

Lleverage itself is now in-between the sales-led and the product-led growth motion. 

“We’re consciously not making a decision right now,” Lennard explains. “But we’re going to make one in the following months. We can’t maintain this position for a long time, because it defocuses a small company like us.”

Besides market fit, Lennard mentions another challenge for early-stage companies: funding. The startup raised €3mn earlier this month, bringing its total investment to €5mn in less than a year. It has managed to avoid the pitfalls of the funding rollercoaster thanks to its seasoned founders who have already built and scaled successful companies. 

But for first-time founders, attracting capital — especially in the AI space — is no easy task, Lennard says. “You have to convince VCs about the defensibility of your idea and that you can actually outgrow your competition.” 

Building a startup is no bed of roses 

When asked about the biggest lesson he’s learned on his journey with Lleverage, Lennard doesn’t hesitate: “I had forgotten how painful it is to build a startup.” 

He cautions prospective founders against romanticizing the concept of a startup. “Make sure you actually want it and talk to people who have done it before or are doing it now.” 

At the same time, he advises against inaction. “If you really want it, just go for it, because sitting on the fence for eternity is also something you shouldn’t do.” 

Another important lesson for Lennard is figuring out how to handle competition — especially in a space as crowded and fast-moving as AI. 

“I don’t open my LinkedIn every day anymore, because every time I do, I see another competitor,” he jokes. He explains that being confronted with competition every day is not only draining, but also distracting. 

“You have to find the right balance between listening to those noises and just believe in what you’re building. There’s a lot of value in perseverance.” 

Growing the Dutch AI ecosystem needs more work

Lennard contrasts the Netherlands with France, where active government support, investor focus, and ambitious infrastructure — like the soon-to-launch world’s largest startup campus — are transforming the country into Europe’s AI powerhouse.

“I feel in the last six months we’re making steps, but before that, there wasn’t a lot of noise around the ecosystem,” he says. “I think we can still do more.”

Lennard says that initiatives like KickstartAI have a role to play. He joined the community when he attended one of our events earlier this year. “I think that communities can be a big boost to the ecosystem and they’re a great way for people to feel that they are part of something.” 

💡 Want to learn more about the KickstartAI community and our mission to accelerate AI adoption in the Netherlands? Check out our website, listen to our podcast, or follow us on LinkedIn!