When industrial AI startup CVector meets with manufacturers, utility providers, and other prospective customers, the founders are often asked the same question: Will you still be here in six months? A year? It’s a fair concern in an environment where the biggest, richest tech companies are luring top talent with eye-watering salaries and increasingly targeting rising AI startups with elaborate acquihire deals.
The answer that CVector founders Richard Zhang and Tyler Ruggles give every time is also the same: they’re not going anywhere. And that matters to their customers—a list that includes national gas utilities and a chemical manufacturer in California—which use CVector software to manage and improve their industrial operations.
“When we talk to some of these big players in critical infrastructure, the first call, 10 minutes in, like 99% of the time we’re gonna get that question,” Zhang said. “And they want real assurances, right?”
This common concern is one reason why CVector worked with Schematic Ventures, which just led a $1.5 million pre-seed round for the startup. Zhang said he wanted to bring on investors that have a reputation for working on hard problems in supply chain, manufacturing, and software infrastructure—exactly what Schematic focuses on as an early-stage fund.
Julian Counihan, the Schematic partner who made the investment, said there are a few ways startups can try to allay these kinds of concerns from customers. Practical solutions include putting code in escrow or offering a free, perpetual license to the software if an acquisition happens. But sometimes, “it comes down to founders being mission-aligned with the company and clearly communicating that long-term commitment to customers,” he said.
It’s this commitment that seems to be helping CVector find early success. Zhang and Ruggles each bring unique skills that align well with the type of work CVector provides its customers. One of Zhang’s earliest jobs was working as a software engineer for oil giant Shell, where he was often in the field building iPad apps for people who had never used an iPad before. Ruggles, who has a PhD in experimental particle physics, spent time working at the Large Hadron Collider, dealing with nanosecond data, ensuring high uptime, and troubleshooting rapidly.
“Those are places where you get to build up that kind of confidence, and that kind of background really helps give people some trust, some confidence in you,” Ruggles said.
CVector is more than just its founders’ resumes, though. The company has been clever and resourceful since getting off the ground in late 2024. It built its industrial AI software architecture—what it refers to as a “brain and nervous system for industrial assets”—by leveraging everything from fintech solutions to real-time energy pricing data to open-source software from the McLaren F1 racing team.
They’re also taking different approaches to shaping this brain and nervous system in real-time with customers. One example Zhang gave is with weather data. Changing weather conditions can impact high-precision manufacturing equipment on a macro scale, but there are also knock-on effects to consider. If it snows, roads and parking lots may get salted. If that salt is carried into a factory on workers’ boots, it can have a tangible impact on equipment that operators might not have previously noticed or been able to explain.
“Bringing those kinds of signals into your operations and your planning is incredibly valuable,” Ruggles said. “All of this is to help run these facilities more successfully, more profitably.”
CVector has already deployed its industrial AI agents in sectors like chemicals, automotive, and energy, and has its eyes set on what Zhang refers to as “large-scale critical infrastructure.” With energy providers specifically, Zhang said a common problem is that their grid dispatch systems are written in old coding languages like Cobol and FORTRAN, making real-time management challenging. CVector creates algorithms that can sit on top of those old systems, giving operators better visibility with low latency.
CVector is small right now, with just an eight-person team distributed across Providence, Rhode Island, New York City, and Frankfurt, Germany. But they expect to grow now that the pre-seed funding is complete. Zhang stressed they’re recruiting only “mission-aligned people” who “actually want to make a career in physical infrastructure”—which will continue to make it easier to convince customers that the startup isn’t going anywhere.
While there’s a fairly straight line from what Zhang was doing at Shell to what CVector is doing now, it’s a bit more of a departure for Ruggles. But he said it’s been a challenge he relishes.
“I love the fact that instead of trying to write a paper, submit it, get it through the peer review process, and hope somebody looks at it, I’m working with a client on something that’s in the ground and that we could be helping them keep up and running,” he said. “You can make changes, build up features, and build new stuff for your customers—rapidly.”