Omen AI has closed a $31 million Series A to bring real-time spectrometer-based fluid monitoring to liquid-cooled data center racks, addressing bacterial contamination risks that can force multi-hour, multi-million-dollar system shutdowns.
Omen AI, a startup founded in 2024, has raised a $31 million Series A round to deploy miniature spectrometers inside data centers, giving operators real-time visibility into the health of the cooling fluids that run through liquid-cooled GPU racks — a problem the company says most of the industry is currently navigating without adequate instrumentation.
The round was led by Nava Ventures and included participation from CRV, Vanderbilt University, Mann+Hummel, Starhill Holdings, and Hard Launch Capital, as well as personal investments from executives at Bridgestone, GM, Johnson Controls, and Tensorwave. The new funding brings Omen's total raised to $40 million since its founding.
The company's core product is a small spectrometer that continuously samples the fluid circulating through liquid-cooling systems, detecting bacterial contamination, pump wear, and seal degradation before those issues require costly intervention.
The problem it addresses is rooted in a tradeoff data center operators face as they push chips to run hotter. The cooling liquid used in those systems is a mixture of water and a bacteriostatic agent. Increasing the water concentration improves heat absorption but accelerates bacterial growth, which can clog fluid pathways. When contamination reaches critical levels, operators must flush the system — a process that can idle a rack for five to six hours at a potential cost of millions of dollars.
"You're not risking huge amounts of downtime because you have no insight into what's going on chemically," said Zach Laberge, Omen's CEO and founder, in an interview.
Laberge, who founded his first company in 2020 at age 14 — raising $3 million to install sensors on construction equipment before dropping out of high school — started Omen with the goal of applying real-time fluid monitoring to heavy machinery. Caterpillar dealerships were among the company's early customers for that vehicles-focused business. Because Caterpillar also supplies gas-powered turbines and generators to data centers, those same dealerships eventually pointed Omen toward a new opportunity.
"A lot of the dealerships were saying, 'Hey, we're starting to put sensors on our turbines, can you guys do anything on the building side of things?'" Laberge told TechCrunch. That inquiry, roughly six months ago, accelerated Omen's pivot toward data center infrastructure.
The company is currently working with a dozen data center customers, including TensorWave, which operates an AI compute cloud built on AMD chips. "The fluid running through these massive systems is a critical variable that most of the industry is flying blind on," said Piotr Tomasik, TensorWave's president, in a statement.
Beyond bacterial contamination, Omen's spectrometer can detect copper or chromium particulates that signal pump wear, and silicon that indicates seal degradation — giving operators an earlier warning system across multiple failure modes that previously required extracting fluid samples and sending them to external laboratories.
Cory Rellas, a partner at Nava Ventures who sits on Omen's board, said the firm's confidence in the company was shaped largely through customer introductions. "It's rare to see such a young founder who has the respect of established, large corporations in a space that moves a bit more slowly," Rellas said. "Much of our diligence came through our introductions with large customers which quickly validated their approach."
Omen is not alone in the space. Pyxis, an established water-monitoring firm, launched a data center coolant monitoring product earlier this month, signaling that the segment is attracting competition as the AI infrastructure buildout intensifies.
Laberge attributed the recent feasibility of on-premises fluid analytics to convergent improvements in optical hardware and signal processing software. "Hardware is just cheap enough that it makes sense to play at scale, and then signal processing lets us make more sense out of the noise," he said.
With data center operators under mounting pressure to maximize uptime as AI workloads grow, the ability to catch cooling system failures before they cascade into expensive shutdowns is likely to draw continued investor and operator attention in the months ahead.
Marc Sabatini is a staff writer at TechEchelon covering enterprise software, cybersecurity, and the regulatory beats that shape both. He focuses on the deal flow and policy decisions that move markets.
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