Overview
Bert founded Timeseer.AI in Flanders, building AI tooling for time-series data. The platform helps industrial and operational teams detect anomalies and extract insights from sensor and time-stamped data.
As founder, Bert drives the technical and commercial direction of Timeseer.AI, focused on making time-series AI accessible to operational teams.
Career history
In the news
- The first Data Science 4 Industry Belgium edition @ Microsoft Zaventem. Join me on September 22. Great initiative .Jan De Lombaert
- AI won't kill software! It'll kill weak software. ⚡ Every day, another "built it in a weekend on Claude" post hits the feed. Half of that excitement is right. Half is about to get expensive. Is a capable model now enough? My honest answer, and how to decide use case by use case 👇 ⚡ Generic logic compresses to zero. ⚡ Proprietary data, context, and trust do the opposite. ⚡ The middle of the stack, data readiness for AI, did not become free. It moved underneath your cool demo or MVP. ⚡ Schneider Electric / AVEVA just paid $3.1B for
- The EU AI Act raises the stakes for operators of critical infrastructure. Non-compliance can carry significant fines. But governance alone doesn't make meter and sensor data trustworthy. In this article, I use Ofwat's AI adoption plan to explain why data validation is the missing foundation for trusted AI.
- Joachim Vleminckx and Maarten Van de Vijver are looking for a chief of Staff. Interested in joining us at Enersee?
- Interesting post from my co-founder Thomas on a topic I believe will become increasingly important in the coming years: Data readiness for AI. Many organizations are investing heavily in AI, agents, digital twins, semantic layers, and modern data platforms. But there is a foundational question that often gets overlooked: Can we trust the data feeding them? My view is that the winners in Industrial AI will not necessarily be the companies with the most data, but the companies with the most trusted data. Data readiness precedes AI
- Andreessen Horowitz says “Everything, Everywhere is Compliance.” Thanks to James da Costa and Angela Strange for a sharp piece. I couldn’t agree more. In industrial, swap one word: everything, everywhere is validation. A spot on punchline : "90% correct reading is still 100% wrong"... In metering, mostly-right has already left the building as a wrong invoice, a failed audit, a mispriced tonne. And you cannot just throw data at AI and trust the output. Models never promise 100% accuracy. That gap is exactly where the moat is. a16z
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