// what drives me
Does the AI advance the WHY? Is the founder committed to keep executing?
In deeptech, and especially in AI, I am not here to fund people doing the same work a little faster. I want
teams whose mission genuinely makes the planet better. The technology still has to earn that: models, data,
and systems that hold up in production, not only in a deck. Building, investing, and auditing are the same
test from different seats: where the engineering matches the claims, and the stack can scale technically over
time.
On the founder side, that question weighs more: I look for real commitment to the mission, not only a polished
deck. That is the bigger half of the test by far. Vision sets the WHY, but execution matters more to me.
Execution and operating
are what determine whether the company actually runs, and whether it keeps running with stability, from
first ship to production that holds up when reality piles on. That bar applies to startups and scale-ups,
and to funds as well. I back teams who close the loop between purpose, product, and what stays running in
the wild, and who keep that honest over time.
// building
The hardest part was never the AI model.
We started INIT8 in 2003 among the first analytics companies in Dutch healthcare and sold it in 2011. That
exit funded what came next and drilled in that execution beats ideas every time.
At VODW, later EY, we grew a data science practice inside large legacy organisations. The work was mostly
politics, integration, and patience, not the model. That path led to co-founding Enjins in 2018: one focus on
getting AI into production for companies that actually use it.
Around the same time, at Quin, we built AI-driven clinical decision support, where a bad prediction hits
real patients. Human-in-the-loop became concrete: named people, clear accountability, and the power to step in
when the system fails.
Enjins and Quin together pointed to Deeploy in 2020: runtime AI governance after deployment so you can
monitor, measure, and keep the right humans in real control. Enjins and Deeploy are both still running, and
both still get serious time from me.
INIT8 ↗ · EY VODW ↗ · Quin ↗ · Enjins ↗ · Deeploy ↗
// investing
Investing without building is pattern matching: decks, narratives, borrowed conviction, not judgment from
the work.
The INIT8 exit planted the bug. By 2014 I was investing seriously. It has always been my capital: first on
my own, then with a wide set of partners (family offices, VCs, and other tech entrepreneurs), often under
the Why Commit Capital label.
Today I am Operating Partner at Volve Capital and Venture Partner at Aenu, with links to a few more funds.
Same focus: founders with real technical depth, pre-seed to Series A, especially where AI genuinely supports
the company's purpose.
I have been inside enough of these builds to recognise what strong delivery looks like, and what it takes
for teams to earn it over time.
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 jointly with partners — 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 ↗
