Lucend

here's my pick for the 20 hottest Dutch startups hiring right now 🇳🇱 some just raised hundreds of millions. some are rebuilding entire industries. some are absolute sleepers that deserve 10x more attention. 🦄 Unicorn & late-stage → Wonderful – enterprise AI platform automating voice, chat + email at scale. newest Dutch unicorn, $150M Series B (Mar 2026, Insight Partners + Index Ventures). lots open → Framer – the no-code website builder that actually feels like proper design software. €1.7B valuation, €85.6M Series D (Aug 2025, Meritech + Atomico). hiring across product + growth → Mews – cloud PMS powering some of the world's best hotels. $300M Series D (Jan 2026, EQT). 700+ team and still scaling hard → Picnic – the grocery delivery that actually cracked the unit economics. $473M raised in Nov 2025 → Mollie – the payments backbone of European e-commerce. $940M raised, Series C. 10 open roles at Amsterdam HQ right now 🚀 Series B & C → Axelera AI – building AI chips for edge computing. one of the most exciting deep tech bets in Europe. $250M Series C (Feb 2026) → Finom – all-in-one SME banking: accounts, invoicing, accounting, expense management. €115M Series C (Jun 2025, General Catalyst + Northzone). hiring → RIFT – generating clean energy by burning iron as fuel. sounds impossible, totally real. $73M Series B (Mar 2026) → Vitestro – autonomous robotic blood drawing. yes, really. $70M Series B (Mar 2026). most unique medtech bet in the country → WorkFlex – automated compliance for cross-border remote workers. €37M Series B (Mar 2026, Spectrum Equity). hiring → Silverflow – rebuilding card payment processing infrastructure from scratch. $40M Series B (Mar 2026, Picus Capital) → Cradle – engineering better proteins with AI. serious biotech, Series B, Amsterdam 📈 Series A → Monumental – robots that lay bricks autonomously on real construction sites. Series A, Amsterdam. shipping in the actual world → Polars – the DataFrames library developers are switching to en masse. Series A. open source darling going commercial → Ditto – AI records your doctor consultation and sends you a plain-language summary. €7.6M Series A (actually seed sry) (May 2026). hiring 🌱 Seed & early → Reson8 – speech recognition for European languages that actually works. €5M Pre-Seed (Mar 2026, Balderton). 🌶️ → SOUS – AI growth platform for independent restaurants + F&B businesses. €4M Seed (Mar 2026) → Lemni – AI agents for customer interactions, built for the conversational-first era. Pre-Seed, Amsterdam → Orange Quantum Systems – quantum hardware, genuinely from the Netherlands. Seed, Apr 2026 → Lucend – AI for data center cooling + ops. $3.3M Seed (Jan 2026). solving real infrastructure problems as AI scales → MarvelX AI - Domain-specific AI that automates high-volume, operational tasks for Insurance and beyond. $6.4M pre-seed what did i miss? pls @ your/their company page if they're hiring! 👇

Every day, data centers generate millions of operational signals—from temperature fluctuations and fan speeds to power draw and humidity. The challenge isn't collecting the data—it's turning it into action. "Hidden in these signals are major optimization opportunities that could save a facility millions of dollars." In this Partner Spotlight, InterGlobix Magazine features Lucend, whose AI-powered platform helps operators uncover performance improvements using the data they already have—without adding operational risk or requiring new capital investments. As AI workloads continue to drive demand for greater efficiency, solutions that optimize existing infrastructure are becoming a strategic advantage for data center operators. Read more on the website about how AI is helping facilities unlock hidden value while improving operational resilience. Jasper de Vries | René Gompel | Lucend #DataCenters #AI #DataCenterOperations #DigitalInfrastructure #OperationalEfficiency #AIOps #FacilityManagement #EnergyEfficiency #DataCenterInnovation #InterGlobixMagazine

Transparency must be the baseline for every AI system in critical infrastructure. Not a feature. Not a differentiator. The baseline. If an AI system touches uptime-critical infrastructure and can't explain its reasoning — what data triggered a recommendation, what outcome was expected, how to verify it — it shouldn't be deployed. That's true for power grids. It's true for hospitals. And it’s true for data centers. That receipt is what separates grounded recommendation from a trust fall. The standard we want to see: Every recommendation comes with the data that triggered it, the expected outcome, and a way for the operator to verify it. All readable by the person running the facility. No black box. No 'trust the algorithm.' Just the work, shown clearly with a traceable path. That matters because operators own the accountability for whatever follows. And they deserve to see the reasoning. That's not too much to ask.

A customer gave us sensor data for a test. We found something in their facility that stopped them in their tracks. It wasn't a simulation, nor a claim to accept on faith alone. We caught a pattern indicating a chiller issue they had missed… for months. The reaction in the room was not really about the finding itself. It was about the question behind the finding. "How did you know that?" The answer was simple, but it changed the energy in the room. They leaned in, and I told them: "It was right there, in your existing data." Most facilities produce far more operational truth than any person could read manually. Temperature, pressure, flow, valve position, fan behavior, and chilled water ring data do not always announce problems as alarms. Often, their patterns only surface when the whole system is analyzed together. This is why a proof of value can be so powerful. The conversation immediately shifts from "should we trust AI?" to "why could we not see this ourselves?" This is the moment curiosity becomes operational.

Talent pipelines were on the agenda this week, during our time at Datacloud Global Congress. Here's what we kept thinking through those sessions: The shortage was in the room with us... not some future problem. Across four days in Cannes, much of the talk was around megawatts, nuclear, and capital. The industry scaling at a pace the buildings must keep up with. But the people can't. Not in the same way. Facilities are growing faster than experienced teams can comfortably absorb them. New sites are coming online and existing sites are being retrofitted. The complexity of cooling, power, controls, and customer requirements keeps increasing. At the same time, the most experienced people are not infinitely available. This creates a strange pressure. The industry talks constantly about power, land, and capacity, but the people responsible for running the facilities are often treated as if their operations and output will simply scale because the buildings do. It doesn't work that way. A data center is not a static asset after commissioning. It changes. It drifts. It responds to load, weather, customer behavior, maintenance, and thousands of small conditions that only become obvious when someone understands the system deeply. That is why it matters for tools to move beyond monitoring and empower real understanding. Not because it replaces experience, but because facilities evolve continuously, and experience that lives only in people's heads is too fragile to keep up. The talent gap won't be closed by hiring alone. It gets closed by giving the people we already trust a way to see more of the facility at once, with evidence they can validate and trust.

At data center events, the conversation often returns to power and land. That makes sense. Without power, there is no capacity. Without land, there is no build. These are visible constraints, and they deserve attention. But they are not the whole story. Someone eventually has to run the facility. Someone has to inherit the design decisions, the construction compromises, the customer requirements, the maintenance backlog, and the gap between what the documents say and how the building actually behaves. That work is less visible. It is also where many of the hardest problems live. Operations is where speed to market becomes daily responsibility. A facility can be designed well, commissioned successfully, and still become difficult to operate if the people running it do not have enough clarity about the system they are inheriting. This is why I think the data center conversation has to bring operations in earlier. Power and land may get the facility built. Understanding keeps it running.

Operators don’t need another screen to watch. They already have systems showing them pieces of the facility. In many cases, the data is there. The problem is that it has not been turned into understanding. A screen can tell you something is happening. It may even show a trend. But the operator still has to interpret it, connect it to the rest of the system, decide whether it matters, translate it into a maintenance or operational workflow, and explain the decision to everyone else who needs to trust it. That is a lot to ask from a person who is already responsible for uptime, safety, compliance, customer expectations, and cost. What operators need is clearer recommendations, visible reasoning, and a way to carry the evidence into the work that follows. Find our Co-Founder and CEO René Gompel at ITW, this week, to learn more. If you're operating or investing in connectivity infrastructure and want to see what's hiding in your facility data, we'd love to connect. Reach out to René directly to set up 30 minutes. #𝗜𝗧𝗪 #𝗗𝗮𝘁𝗮𝗖𝗲𝗻𝘁𝗲𝗿𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 #DigitalInfrastructure

We're heading to ITW 2026. Monitoring only tells you something changed. Understanding tells you why it matters, so you can decide what to do about it. Most data center operations teams have the first. Lucend delivers the second, turning facility data into clear, traceable recommendations that help operators act with confidence. René Gompel our Co-Founder and CEO, will be at the Gaylord National in National Harbor, May 19-21. If you're operating or investing in connectivity infrastructure and want to see what's hiding in your facility data, we'd love to connect. Reach out to René directly to set up 30 minutes. See you in National Harbor. #ITW #DataCenterOperations #DigitalInfrastructure

$4.3 million saved in one year. 40% energy efficiency gain. Not from a new build or a hardware upgrade. From a morning routine. Our co-founder Jasper de Vries shared this story with Molly Wood on her podcast, Everybody in the Pool. One of our users runs data centers in Dublin, including facilities he cannot physically enter. Big tech tenants control some of the rooms. When something changes inside, he cannot walk the floor to check. So every morning, he opens what he calls his "daily newspaper." It tells him what changed overnight, what needs attention, and what to do about it. Every recommendation comes with a receipt, so he can verify it himself. When leadership asks why he made a decision, he shows them. When a vendor claims their equipment is performing to spec, he has the evidence. That is the $4.3 million story. But what Jasper told Molly next is the part that surprised us: how this Dublin operator started using the platform in ways we never designed for, and why that changed how we think about what we are actually building. 🎙️ https://lnkd.in/d9KzRPcy