Today, as the wave of artificial intelligence sweeps across all industries, the manufacturing sector is undergoing an unprecedented intelligent revolution. When industrial AI startup CVector meets with potential clients such as manufacturers and utility providers, the founders are always asked the same question: Will you still be here in six months? What about a year from now?

This seemingly simple question reflects the deep concerns of industrial customers regarding the sustainability of AI startups. In an environment where tech giants are luring top talent with incredible salaries and targeting emerging AI startups through carefully designed acquisition deals, this concern is particularly reasonable.

CVector's co-founders Richard Zhang and Taylor Ruggles always give the same and firm answer: they will not leave. This commitment is crucial for their client base, which includes national gas utility companies and chemical manufacturers in California, who are using CVector's software to manage and improve industrial operations.

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Zhang revealed the common concerns of his clients during an interview: "When we talk to these large enterprises in critical infrastructure sectors, within 10 minutes of our first call, 99% of the time we encounter this question. They need real assurance."

Based on this widespread concern, CVector chose to partner with Schematic Ventures, which recently led a $1.5 million pre-seed round for the startup. Zhang said he wanted to bring in investors with a strong reputation in areas like supply chain, manufacturing, and software infrastructure, which is exactly where Schematic focuses as an early-stage fund.

Schematic partner Julian Kuhnhenn explained to TechCrunch some ways the startup has alleviated customer concerns. In addition to practical solutions such as hosting code or providing free permanent software licenses in case of acquisition, "the key is that the founders are aligned with the company's mission and clearly communicate this long-term commitment to customers."

This commitment seems to be helping CVector achieve early success. Zhang and Ruggles each bring unique skills perfectly suited to the type of work CVector provides to its clients. One of Zhang's earliest jobs was as a software engineer at oil giant Shell, where he often built iPad applications for people who had never used an iPad before.

Ruggles, who holds a PhD in experimental particle physics, worked on the Large Hadron Collider, "processing nanosecond-level data, striving to ensure high uptime, taking responsibility for downtime, and quickly troubleshooting." Ruggles said, "These environments help build that confidence, and this background really helps give people some trust and confidence."

However, CVector's value goes beyond the founders' resumes. Since its founding at the end of 2024, the company has shown smart and resourceful traits. The company built its industrial AI software architecture—which it calls "the brain and nervous system of industrial assets"—by integrating various resources ranging from financial technology solutions to real-time energy pricing data, to open-source software from the McLaren F1 racing team.

They also use different approaches to shape this brain and nervous system in real time with clients. Zhang gave an example involving weather data. Changes in weather conditions can affect high-precision manufacturing equipment on a macro level, but there are also chain effects to consider. If it snows, roads and parking lots around may be salted. If the salt is brought into the factory by workers' boots, it could have a substantial impact on high-precision equipment, something that operators may not have noticed or been able to explain previously.

Ruggles emphasized, "It is extremely valuable to bring these signals into operations and planning. All of this is to help these facilities run more successfully and profitably."

CVector has already deployed its industrial AI agents in industries such as chemicals, automotive, and energy, and is looking toward what Zhang calls "large-scale critical infrastructure." Especially in the case of energy providers, Zhang said a common problem is that their grid scheduling systems are written in old programming languages like Cobra and Fortran, making real-time management difficult. CVector is able to create algorithms that can run on these legacy systems, giving operators better visibility with low latency.

CVector currently has a small team of eight people spread across Providence, Rhode Island, New York City, and Frankfurt, Germany. But with the completion of the pre-seed funding round, they expect to expand. Zhang emphasized that they only recruit "people aligned with the mission," those who "truly want to build a career in physical infrastructure"—which will continue to make it easier for customers to believe that this startup won't disappear.

Although there is a direct connection between Zhang's work at Shell and CVector's current business, for Ruggles it is more of a transformation. However, he said it is a challenge he very much enjoys.

Ruggles said, "I like the fact that instead of trying to write papers, submit them, go through the peer-review process, and hope someone will see them, I am working with customers to solve real problems that we can help them keep their systems running. You can make a difference, build functionality, and develop new products for customers quickly."

The success story of this industrial AI startup demonstrates the huge potential of merging traditional manufacturing with cutting-edge AI technology. At a critical moment in the global digital transformation of manufacturing, AI companies like CVector that focus on industrial infrastructure are providing key technological support for industry transformation, while also proving to investors and customers that AI applications in specialized vertical markets have significant commercial value and development prospects.