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Startups

Omen AI’s plan to optimize data centers is all wet

techtost.comBy techtost.com30 June 202604 Mins Read
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Omen Ai's Plan To Optimize Data Centers Is All Wet
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The demand for AI-driven computing power has data centers wanting to squeeze more out of each rack of GPUs. A consequence? Bacterial outbreaks.

Liquid for liquid-cooled chips is a mixture of water and a substance that inhibits the growth of bacteria. To make the chips run hotter, data center managers can change the mix to include more water, which absorbs heat better but leads to nasty flow-clogging contamination. To solve this, they flush the system, which can mean shutting down a shelf for five or six hours at a potential cost of millions of dollars.

Omen AI has a solution: A tiny spectrometer that can monitor the health of that liquid in real time, spotting bacterial growth before it becomes a huge problem. “You don’t risk massive downtime because you don’t have any insight into what’s going on chemically,” explains CEO and founder Zach Laberge.

Today, Omen AI said it raised a $31 million Series A round led by Nava Ventures and including participation from CRV, Vanderbilt University, Mann+Hummel, Starhill Holdings and Hard Launch Capital, as well as personal investments from executives from Bridgestone, GM, Johnson Controls and TensorWave.

Laberge founded his first company in 2020 when he was 14, raising $3 million to install sensors in construction equipment and eventually dropping out of high school. (His father and mother, a former Ontario education minister, supported his plan to forge his own path.)

After that startup closed, Laberge launched Omen in 2024, with the idea of ​​focusing on fluid systems as the key to enabling construction machinery to be smart enough to know when it needed to be repaired. The idea was to replace the time-consuming process of extracting samples and sending them to a laboratory with real-time awareness. In addition to bacterial growth, the device can detect worn pumps and pumps if it sees copper or chromium, or seals if it sees silicon.

Caterpillar dealerships have been a key customer for Omen’s heavy-duty vehicle business, but Cat is also a major supplier of gas-powered turbines and generators to provide on-site power for data centers. It didn’t take long for Omen to see where the wind was blowing.

“That was kind of the transition,” Laberge told TechCrunch. About six months ago, “a lot of the dealerships were saying, ‘Hey, we’re starting to put sensors on our turbines, can you do something on the building side?’

Omen discovered that these buildings are full of liquid, from the HVAC systems to the chip cooling. Identifying a new, fast-growing group of potential customers, Omen began focusing on data centers.

“It’s rare to see such a young founder who has the respect of established, large companies in a space that’s moving a little slower,” said Cory Rellas, a partner at Nava Ventures who sits on Omen’s board. “Particularly for Omen, much of our due diligence came from our introductions to large clients, who quickly validated their approach.”

Omen, which has raised $40 million since its founding in 2024, is working with a dozen data center customers as they develop their offering, including TensorWave, a company that builds AI cloud computing on AMD chips.

“The fluidity running through these massive systems is a critical variable that most of the industry is turning a blind eye to,” said Piotr Tomasik, president of TensorWave, in a statement. “Omen… see the future of infrastructure just like we do, better monitoring to optimally support computing customers.”

While many organizations rely on sending fluid samples to labs for information, Omen isn’t alone in developing on-site analytics — Pyxis, an established water monitoring company, launched its data center coolant tracking product beginning of this month.

The key technological advances that have unlocked this approach are recent improvements in both optical technologies and signal processing software. “The hardware is cheap enough that it makes sense to play at scale, and then processing the signal allows us to understand more of the noise,” Laberge said.

When you purchase through links in our articles, we may earn a small commission. This does not affect our editorial independence.

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