Founder of Reprex, a company dedicated to building trustworthy data and AI.

Daniel has been writing code since childhood, learned the early foundations of AI in the last century, and today I advise C-level executives on navigating modern AI challenges. His focus: transforming messy, incomplete, or biased data into a reliable foundation—because without clean data, even the best algorithms fail.

Across Europe, whether on stage at AI conferences, in boardrooms, or at hackathons, he shares practical insights on how organizations can create real value with AI.

👉 If your algorithm doesn’t have clean data, Reprex can fix it—or find the right data for you.

He is also a research affiliate at the Centre for Competition Policy and at the Institute for Information Law of the University of Amsterdam.