AI Research Team Visits Nuoyake Electric, Sees 45% Efficiency Gain and 50% Faster R&D

On August 5, 2026, the research team visited Shanghai Nuoyake Electric Co. , Ltd. in Songjiang, Shanghai, to examine on-site the application of AI technology in high-end electrical manufacturing. The team toured the company's smart exhibition hall and intelligent production workshops, gaining a comprehensive understanding of the company's development strategy and smart manufacturing achievements.

Nuoyake Electric is a wholly-owned premium sub-brand of Chint Group, specializing in smart electrical equipment, new energy, and data center supporting products, with business covering more than 40 countries and regions worldwide. The company's R&D team exceeds 300 people, with 32% holding master's or doctoral degrees. Leveraging three global R&D centers, the company has built an integrated system spanning R&D, production, and service.

Since launching its smart upgrade initiative, Nuoyake has undergone more than a decade of iterations, completing the transition from manual production to automated and intelligent production lines. In 2021, it built an integrated smart factory and was recognized as a national-level smart demonstration factory and green factory. Using digital simulation technology, the company achieved virtual commissioning of production lines, integrating machine vision, the Internet of Things, and AI algorithms to establish an intelligent online inspection system. After the smart transformation, production efficiency improved by more than 45% and R&D cycles were shortened by 50%.

Chint Group began its AI industrialization push at the end of 2024 and completed a unified AI computing infrastructure in 2025, building a computing system based on open-source models and cloud service resources. Nuoyake has developed multiple AI application scenarios across R&D, production, supply chain, and new energy, achieving quality improvement and cost reduction in production operations.

During the visit, the company highlighted industry pain points for AI deployment in high-end manufacturing: a pronounced supply-demand imbalance in computing power, with high costs for self-built computing infrastructure and security risks to sensitive industrial data on public cloud platforms; a weak industrial data foundation, with uneven digitalization levels across factories and missing historical data; and highly vertical and fragmented manufacturing scenarios, where general-purpose large models lack sufficient generalization capability, requiring customized small models and scarce compound talent.

The two sides reached a cooperation consensus on building the industrial ecosystem together. Going forward, they will open up vertical AI application scenarios, engage technology companies in related fields, and tackle the core challenges of AI deployment.