Industrial AI startup CVector has built the brain and nervous system for large-scale industry. Now founders Richard Zhang and Tyler Ruggles have an even bigger challenge ahead of them. That means showing customers and investors how this AI-driven software layer translates into real cost savings at industrial scale.
The New York-based startup has seen some success since its pre-seed funding round last July. The system is currently in operation with real-world customers, including utilities, advanced manufacturing facilities, and chemical producers. The pair got more concrete examples of what problems they could solve and save money for large customers in the industry.
“One of the key things we’re seeing is a real lack of tools for customers to translate small tasks like turning a valve on and off. Does this save them money?,” he said.
As a homeowner with bills to pay, it’s a bit unsettling to think of one unexplained valve making a big difference to the bottom line for your company and your customers. But it’s examples like this that have helped CVector reach new milestones. Zhang and Ruggles told TechCrunch they have now closed a $5 million seed round.
The funding was led by Powerhouse Ventures and included a mix of venture and strategic backing, with participation from early-stage funds such as Fusion Fund and Myriad Venture Partners, and Hitachi’s corporate venture arm.
As the funding round closes, CVector tells us a little more about some of its first customers and how different they are.
“The joy of the last six to eight months has been moving to industrial heartlands, to places in the middle of nowhere but where there are large-scale production plants that are reinventing themselves or really changing the way we make decisions,” Zhang said in an interview.
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One such customer is an Iowa-based metalworking company called ATEK Metal Technologies, which makes aluminum castings for Harley-Davidson motorcycles. CVector identifies potential problems that could lead to equipment downtime, monitors the energy efficiency of the entire plant, keeps an eye on commodity prices that impact raw material costs, and more.
“This is a great example of where we need a really skilled workforce. They’re going to need all the help they can get for us, on the software side, on the technology side. It’s about having this group of people to help them transform and take the business to the next level so that it can continue to grow,” Zhang said.
For companies like CVector, finding optimization in old factories may seem like the most obvious route. But it has also picked up startups as customers, including Ammobia, a San Francisco-based materials science startup working to lower the cost of ammonia manufacturing. But what CVector is doing for Ammobia is surprisingly similar to what it’s doing for ATEK, Zhang said.
CVector is also growing. The company has up to 12 employees and has closed its first physical office in Manhattan’s financial district. Zhang said it is attracting talent from fintech and finance, especially hedge funds. Those in the hedge fund industry are already quite focused on using data to gain financial advantage, so the latter is ripe for hiring, he said.
“This is the core of our sales strategy, and we call it ‘operational economics,’” Zhang said. “We position ourselves between running the plant and real economics, the margins of making a profit.”
But Zhang still sees public utilities as a good place to apply CVector’s technology. (That’s where the valve example comes from.) And he’s found that even these types of customers talk much more fluently about the kinds of things CVector does.
“Tyler and I were talking about how when we first started the company almost exactly a year ago, talking about AI in general still felt like a taboo. There was a 50/50 chance that your customers would either embrace AI or disparage you, right?” he said “But now, especially in the past six months, everyone is demanding more AI-based solutions, even when ROI calculations are not clear. This adoption frenzy is real.”
Ruggles said this is a big part of what CVector does because it ultimately comes down to one thing: money. And with so much uncertainty in the world, managing costs is becoming increasingly difficult.
“We’re at a point where companies are really deeply concerned about their supply chain and the costs and volatility there, and they can layer AI on top of the economic model of the facility. That’s resonating with a lot of our customers, whether it’s an old, industrial place in the heartland or a new energy producer trying to do something new and novel,” he said.
