Overview
Karel co-founded Luzmo with Haroen Vermylen and Thomas De Clerck. He brought over 18 years of BI and data visualization experience from senior consultant and manager roles at CSC and the Belgian Ministry of Economics. The company was originally called Cumul.io before rebranding to Luzmo.
Luzmo's platform enables SaaS companies to embed customer-facing analytics directly into their products without building custom visualization tooling. In 2024, the company launched Luzmo IQ, bringing hyper-personalized AI-driven analytics to embedded use cases. Luzmo has a partnership with Lansweeper (also Belgian) as a reference for the strength of the Flemish B2B SaaS ecosystem.
Career Highlights
- Co-founded Cumul.io/Luzmo, now a leading embedded analytics platform for SaaS companies globally
- 18+ years of BI and data visualization experience before founding Luzmo
- Regular speakerglobal SaaS events including SaaSOpen and SuperNovaAntwerp
Career history
In the news
- Great to see the Luzmo Roadshow land in New York! We brought together a small group of people working in analytics to exchange ideas, share experiences, and talk about where the industry is heading. A lot of the conversation focused on agentic analytics and generative UI, and how these are changing the way people work with data. Thanks to everyone who joined us and shared their perspectives! Next stop: Brussels, tomorrow! If you’re around and want to join the conversation, we’d love to see you there. https://lnkd.in/dT5-RE4e
- We asked product leaders a question that made a lot of them pause: Would your dashboard, your chat and your AI agent give a customer the same answer? Only 12% said yes with confidence. That number stuck with me. We spent a decade making sure the dashboard tells one version of the truth. Now the same data shows up in 3 places, and most teams can't vouch that it matches. It's not the only surprise in this year's Everywhere Index. 63% of teams that shipped AI analytics have already seen it give a real user a wrong answer. On Tuesday
- Are we still embedding analytics, or are we embedding answers? Been thinking about this a lot lately, especially with everything we're building at Luzmo. Put some thoughts into a post. Curious if you see it the same way 👇
- A year ago, “build or buy?” was already a difficult analytics decision. AI coding tools made it more interesting. Building looks easier than it did twelve months ago, but that doesn’t automatically mean teams are more confident owning the whole analytics stack long term. That’s one of the questions behind this year’s Everywhere Index: compared with last year, are product teams more confident building analytics themselves, or buying a platform? If you own analytics inside a product, I’d like your view. It takes around six minutes,
- À SaaSiest Amsterdam, je repars avec plus de questions que de réponses. Et c’est plutôt bon signe. J’y ai retrouvé plus de 100 dirigeants et C-level du SaaS européen pour parler d’un même bouleversement : l’IA accélère le travail, mais elle ne supprime pas les problèmes d’organisation. Trois idées me restent en tête : 1️⃣ Quand le développement va plus vite, le prochain frein apparaît ailleurs : dans les décisions, les revues, la coordination ou la mise sur le marché. 2️⃣ Ajouter l’IA à nos habitudes ne suffit pas. Il faut parfois
- 𝙄 𝙘𝙖𝙣 𝙥𝙧𝙤𝙗𝙖𝙗𝙡𝙮 𝙗𝙪𝙞𝙡𝙙 𝙩𝙝𝙞𝙨 𝙢𝙮𝙨𝙚𝙡𝙛 𝙩𝙤𝙣𝙞𝙜𝙝𝙩.” I've been hearing this a lot lately. And with AI, it's probably true. But building something in a few hours and owning it for the next few years are two very different commitments. I wrote a bit about the new build-vs-buy dilemma, why rebuilding yesterday's software might cost you tomorrow's opportunities, and why knowing what not to build is becoming such an important skill. Curious if others are seeing this too. 👇
- Your customers want to bring their own AI. Not exactly a small change for product teams. Users are getting used to asking questions wherever they already work (in ChatGPT, copilots, internal AI tools, chat interfaces) instead of always going somewhere else to open a dashboard. And that raises a much bigger question: Where should answers from your product data actually live? In a dashboard? Inside your product? In a copilot? In the AI environment your customer already uses? At Luzmo, we’re researching how product and data
- I’d be surprised if most product teams building AI haven’t already shipped at least one wrong answer to a real user. But what happens after the first wrong answer? Not “will it ever be wrong? It will. But… would the system have enough context to recover well? This question is one of the reasons we’re running the Everywhere Index again this year. We want to understand how product and data teams are actually using AI in analytics now, where users are getting answers from product data and what really changed over the last year. Survey
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