CVector Raises $5M to Build AI Nervous System for Industry

CVector Energy, a New York-based startup, has raised $5 million to grow its software platform that works like a brain and nervous system for factories, power plants, and chemical sites. The company, founded by engineers Richard Zhang and Tyler Ruggles, aims to help industrial operators make quicker decisions by pulling together data from machines, weather, energy prices, and more. This funding comes after an earlier round and builds on early use by energy firms and manufacturers.

Background

CVector started in late 2024 when Zhang and Ruggles, both with backgrounds at Shell and CERN, saw a problem in heavy industry. Factories and power grids often run on old software that cannot handle today's fast changes, like shifting energy costs or weather impacts. Their platform fixes this by gathering high-quality data from equipment in real time and mixing it with outside information.

The company has offices in Providence, Rhode Island, New York, and Frankfurt, Germany. It has a small team of about eight people, but plans to hire more who care about physical systems like power grids and plants. Early customers include gas utilities across the country and a chemical maker in California. These groups use CVector to watch their operations closely and spot issues before they grow.

CVector got its first funding of $1.5 million in August 2025, led by Schematic Ventures. That money helped build the core product and get security certifications, including ISO 27001. Now with $5 million more, the focus shifts to bigger rollouts in tough settings like refineries and manufacturing lines.

The founders stress they have no plans to sell the company soon. Customers in critical areas, like national energy systems, ask about this early in talks. Zhang says they give firm answers to build trust, which helps win deals.

Key Details

CVector's software takes data from sensors on machines—things like temperature, pressure, and vibration—and adds live feeds on energy prices, weather forecasts, and market shifts. This creates a full picture for workers to act on right away.

How the Platform Works

Operators plug in AI tools that run on the data. The system learns from how people work at each site, spotting patterns over time. For example, in power plants, it works over old systems written in languages like FORTRAN. CVector adds new layers that give clear views without slowing things down.

In chemicals and automotive plants, it helps find root problems fast and plan fixes ahead. The platform supports self-repair features, where AI suggests actions based on past fixes.

"CVector is building the brain and nervous system for industrial assets," said Richard Zhang, co-founder and CEO. "We're giving operators and engineers a foundation to plug in AI, understand system-wide behavior, and act with speed and precision."

Dr. Tyler Ruggles, the CTO, points out that the software does more than collect data. It runs models at the site edge to check costs and economics in real time.

"We go far beyond data collection," said Dr. Tyler Ruggles, co-founder and CTO. "With CVector, operators can run complex techno-economic modeling at the edge. It's not just about insight—it's about automated action and real operational lift."

Schematic Ventures' Julian Counihan sees it as key infrastructure for factories of the future.

The company earned strong security marks early, with ISO 27001 in June 2025 and SOC 2 Type II underway. This matters for sites handling sensitive operations.

CVector draws ideas from finance tech for fast data handling, oil and gas for tough conditions, and even racing teams for real-time signals. This mix lets it handle complex spots like research labs and old plants alike.

What This Means

This funding lets CVector hire more staff, improve the product, and reach bigger customers in energy, chemicals, and manufacturing. Operators stand to save money by running plants smoother, cutting waste, and avoiding breakdowns.

For power companies, better grid control could mean handling demand spikes without blackouts. Chemical firms might speed up production while meeting safety rules. Across industry, the shift to AI-driven systems could cut costs and boost output as markets change faster.

The platform sets up factories to add more AI agents over time, moving toward hands-off operations. Early wins show it works on real sites, not just tests. As energy rules tighten and costs rise, tools like this could become standard.

CVector plans to grow in critical infrastructure, where old tech limits quick responses. By staying independent, it promises long-term support for users who need reliable partners.

Industry watchers note that many AI tools fail without strong data links. CVector fills that gap, connecting the 'brain' of analytics to the 'body' of machines. This could speed up how factories adopt AI overall.