Chuangsuan Technology: Industrial AI-Native Flexible Intelligent Manufacturing Platform

Chuangsuan Technology (Beijing) Co., Ltd. completed a RMB 2 million angel round financing on October 12, 2026. This round of financing was exclusively invested by individual angel investor Jiang Nan. The funds will be mainly used for core algorithm iteration, the promotion of scenario implementation of the SLM process large model, and the adaptation and optimization of edge computing privatization suites, to support the team in sprinting for the public roadshow in the hard technology track of the 36Kr Creative Project Collection Competition in early November, and accelerate the coverage of core target customer groups such as precision manufacturing and heavy industry.

Chuangsuan Technology focuses on the track of industrial AI-native design platform and vertical large model for metal additive manufacturing (SLM process). Taking the original "Neural-Symbolic-Physical AI" architecture as the core base, it integrates perception generation capability, industrial rule system and physical mechanism constraints to build a full-stack product matrix, covering four core product lines: process AI-native design system, industrial scenario agent InnoAgent, edge computing privatization suite, and industry scenario packages covering metal 3D printing, precision manufacturing and subtractive intelligent manufacturing. By inputting the part model, auditable and machine-processable process plans and manufacturing codes can be directly output, which can realize core functions such as automatic generation of support structures, parameter optimization, manufacturability assessment, defect warning, and production scheduling assistance. It also supports multi-source industrial data fusion collection and edge-side data closed-loop iteration, adapts to the model optimization requirements in small sample scenarios, and all data is deployed locally without leaving the factory. Compared with general large models, it has executability guaranteed by strict physical constraints, and compared with traditional industrial software, it has significant advantages of AI-native driving and lightweight deployment. At present, the Beijing entity has completed full algorithm prototype verification, and the Wuxi entity has carried out multiple rounds of scenario pilots and new business cooperation with key local enterprises in the metal 3D printing service field, and is about to secure a core order worth tens of millions of RMB.

At present, the domestic cluster of "specialized, refined, differential and innovative" manufacturing enterprises is expanding rapidly. Traditional industrial design and production processes generally have pain points such as high first-piece failure rate, long iteration cycle of process parameters, and the schemes generated by general large models that do not conform to industrial physical rules and are difficult to implement. Meanwhile, there is a significant gap in cross-disciplinary talents who have both AI algorithm capabilities and industrial mechanism cognition. The overall industrial AI track is still in the early strategic window period of rapid development, and there is no leading player with absolute monopoly advantage. Relying on the small-sample generative process optimization technology under physical constraints, Chuangsuan Technology has formed clear differentiated competitiveness, filling the market gap between general large models and traditional industrial software. It can directly output implementable process production plans for manufacturing enterprises, greatly lowering the use threshold and trial-and-error cost for industrial users. It has completed preliminary verification in the core scenario of metal 3D printing in East China, and has the foundation to rapidly replicate and expand to scenarios such as precision manufacturing and heavy industry.

This round is the first public financing disclosed since the establishment of Chuangsuan Technology. At present, in the process of promoting business expansion, the team is also facing the common industry challenge of insufficient supply of cross-disciplinary compound industrial AI talents. By building a shared and co-built industrial AI joint innovation platform, the company links industrial partners and university resources to gather ecological forces, collaboratively breaks through the talent bottleneck, and ensures the promotion efficiency of technology iteration and scenario implementation.

Meng Qinghua, Founder, Co-CEO and CTO of Chuangsuan Technology, said: "Industrial AI is an Olympiad math problem, not an extra credit problem. Its development trend is irreversible and the window period is very short. Relying on the long-term accumulation in this track from the circle of master and doctoral classmates from Beijing Institute of Technology, we have gathered senior industrial veterans from various fields to form a golden team. We put on our armor while running, and step into this tough battle without hesitation." As a senior leading figure in China's AI industry, Meng Qinghua has more than 20 years of experience in AI algorithm R&D and product management. He has successively served in core positions such as R&D Head of Microsoft Mobile, Consultant-level Researcher of Lenovo AI Lab, and AI Director of BYD Intelligent Cockpit, holding 3 AI technical secrets. The co-founding team covers core capability sectors including industrial mechanism modeling, government and enterprise market expansion, heavy industry customer ecological operation, and underlying technology R&D. All core members have about 20 years of in-depth experience in their corresponding fields, forming a complete closed-loop of capabilities covering the full chain of technology, delivery and market. Relying on the differentiated technical route of vertical large models and full-scenario implementation capabilities, Chuangsuan Technology will continue to link the cluster of specialized, refined, differential and innovative manufacturing enterprises to output industrial AI empowerment value, and seize the window period to fill the market gap of domestic industrial AI-native design platforms.