// what drives me
Most of what I do comes down to one question: is this AI real, or does it just look like it?
As a builder, investor and auditor — that question shows up every day, in different forms. Building taught
me where AI breaks. Investing taught me where founders underestimate that. And auditing keeps me honest
about both.
AI only gets genuinely better when the right people stay committed to the feedback loop — high quality,
right frequency, over time. That's not just a technical problem. It's a people problem. The founders who
understand that are the ones worth backing.
// building
The hardest part was never the AI model.
We started INIT8 in 2003 — one of the earliest analytics companies in Dutch healthcare. No playbook, no
investors, just building. We sold it in 2011. That exit funded what came after — and taught me that
execution beats ideas every single time.
At VODW — later acquired by EY — we built and grew a data science practice inside large legacy
organisations. Same lesson everywhere: 20% technology, 80% politics, integration and patience. Getting AI
to actually work inside a real organisation is a fundamentally different problem than building a model that
performs well in a notebook. That experience led directly to co-founding Enjins in 2018 — a company with
one focus: getting AI into production for companies that actually use it.
Around the same time, at Quin, we worked on AI-driven clinical decision support. A wrong prediction has
real consequences for real patients. That changed something. Human-in-the-loop stopped being an abstract
concept and became very concrete: specific people, clear responsibilities, and the genuine ability to
intervene when the system goes wrong.
What we learned at Enjins and Quin combined led to co-founding Deeploy in 2020 — runtime AI governance for
what happens after deployment. Once AI is live, you need to monitor it, measure it, and make sure the right
humans stay genuinely in control. Both Enjins and Deeploy are still going. Both still where I invest real
time.
INIT8 ↗ · EY VODW ↗ · Quin ↗ · Enjins ↗ · Deeploy ↗
// investing
Investing without building experience is just pattern matching on slides.
The INIT8 exit planted the bug. By 2014 I was doing it seriously — first with my own capital, later under
the label Why Commit Capital, and increasingly in partnership with other entrepreneurs and early-stage VCs
who shared the same thesis. Over the past decade: 1000+ companies looked at, 100+ real due diligences, 25+
investments across Climate and AI.
Currently Operating Partner at Volve Capital, Venture Partner at Aenu, and affiliated with several other
funds. The focus has always been the same: founders with genuine technology depth, pre-seed to Series A, in
sectors where AI and climate intersect.
The difference from most early-stage investors: I've built the things these founders are building. I know
where they break. And I can tell the difference between a real AI product and a wrapper dressed up as one.
Why Commit Capital ↗ · Volve Capital ↗ · Aenu ↗
// auditing
Most AI claims in pitchdecks don't hold up. Not because founders lie — but because building real AI is
genuinely hard.
Proprietary models, owned training data, production-grade infrastructure, human oversight that actually
functions — most teams aren't there yet, and most decks don't reflect that honestly. After 100+ real tech
due diligences, the patterns became very familiar.
That's why we built TechTruth — a tool that does the first pass systematically. Founder background, AI
depth, architecture reality check. Not to replace human judgment, but to make it faster and sharper. It's
what I wish we'd had ten years ago.
TechTruth ↗
