The industrial sector consumes more than a quarter of U.S. electricity, accounting for 7% of U.S. emissions. Manufacturing drives more than three-quarters of U.S. industrial load, and is critical for producing decarbonization technologies, from batteries (discrete manufacturing) to green molecules (process manufacturing). There is a substantial opportunity for manufacturers to improve efficiency and reduce energy costs, but doing so will require more sophisticated data and analytics than are available today. 

Industrial companies collect massive volumes of data, but much of their legacy data stack is decades old and poorly suited for modern AI-driven analytics or intuitive, effective UI/UX.  As a result, time to value and return on investment (ROI) are constrained across a range of use cases, from predictive maintenance and anomaly detection to economic optimization. Moreover, most legacy industrial data platforms fail to effectively integrate market data or robust techno-economic analysis (TEA), which limits manufacturers’ ability to make financially-informed, real-time decisions that optimize energy costs.

CVector is building an end-to-end industrial data and analytics platform that can scalably ingest high resolution supply chain, control system, and market data and deliver tailored analytics and agentic workflows to end users. By augmenting process data with integrated market data and robust TEA models, CVector enables customers to increase operational efficiency, identify and prioritize economic optimization opportunities, and reduce both energy costs and emissions. 

Process manufacturers are exposed to electricity prices, and load growth is driving prices higher. 

• For electricity-intensive manufacturing processes, optimizing electricity consumption is a competitive advantage. For instance, electricity accounts for approximately 40% of aluminum smelting production costs, so even small efficiency improvements can have a meaningful financial impact. 

• In the United States, we have entered an era of significant load growth, driven by manufacturing onshoring, data center expansion, and electrification. Given the already constrained energy supply on the grid, electricity prices are expected to continue rising. The EIA forecasts that wholesale electricity prices will increase to $51/MWh in 2026, representing an additional 8.5% rise on top of the more than 20% increase between 2024 and 2025. Beyond higher prices, accelerating load growth also threatens grid reliability and risks increasing carbon emissions. 

• These dynamics underscore the urgency for electricity-exposed manufacturers to find near-term solutions. Leveraging data, analytics, and demand-side management is one of the few tools manufacturers have at their disposal.

Industrial data is fragmented, inaccessible, and incomplete. 

• The legacy industrial data stack typically consists of five distinct architectural layers, generally served by different technology providers: edge ingestion, the historian, data management, analytics, and UI/UX. 

• While process manufacturers have collected time-series data for decades, the data captured at the historian layer often lacks critical context, like machine type. As a result, substantial manual effort is required at the data management layer to map, cleanse, and normalize the data before it can be used for analytics and visualization. 

• In addition, legacy stacks rarely integrate additional data layers that allow for optimization with respect to factors outside of the production process, such as the cost of inputs and outputs. 

• Recent tech advances, including open protocols for Programmable Logic Controllers (PLC) and Supervisory Control and Data Acquisition (SCADA) systems alongside cloud-native deployments, create an opportunity for an AI-native platform to unlock scalable data ingestion and analytics that finally deliver meaningful economic value from existing, rich data sources.

CVector’s end-to-end industrial data and analytics platform accelerates time to value and drives ROI across predictive maintenance, anomaly detection, and economic optimization. 

• CVector is building an end-to-end industrial AI solution that spans ingestion through analytics and visualization. 

• The company utilizes state-of-the-art AI tooling paired with an edge device to scalably ingest time-series and contextual data directly from SCADA and other plant systems, which enables deployment in weeks rather than months. 

• CVector stores the data in a cloud platform and integrates third-party data, such as market signals and weather. The company then layers on TEA models to tie operational and market data to process economics. 

• Finally, CVector offers an AI analytics layer and software development kit (SDK) that enables personalized dashboards and agentic workflows so that end users, like equipment operators, can take actions in real time. As operators use the platform, it also captures on-the-job, experiential knowledge that is not typically recorded in other enterprise systems. 

• CVector will initially target smaller process manufacturers with newer equipment that supports open protocols for data ingestion, focusing on segments with high exposure to electricity prices. Over time, the company plans to expand to serve larger process manufacturers and potentially move into discrete manufacturing. The company will enter the market with analytics that improve decision-making and economic outcomes, and over time has a meaningful opportunity to replace large portions of the legacy technology stack. CVector’s near-term target verticals, which  include  metals, chemicals, and power plants, represent a SAM of approximately $10B.

Powerhouse Ventures is proud to lead CVector’s $5M seed round, with additional participation from Fusion Fund, Hitachi Ventures, and Myriad Ventures. We look forward to working with Co-Founder & CEO Richard Zhang, Co-Founder & CTO Tyler Ruggles, and the entire CVector team to unleash the power of industrial data.

Special thanks to deal lead and article author, Gabriel VanLoozen.