AInnovation Technology Group Co., Ltd. (stock code 2121. HK) was established in February 2018 with the mission of "empowering business value with artificial intelligence". It is a rapidly developing enterprise level AI industry application supplier and leader in "AI+manufacturing" large model intelligent agent applications in China. It is a national specialized, refined, innovative and small giant enterprise. Following the development line of "the first mock examination, one body and two wings", the company has built innovative technology platforms and related applications such as AInnoGC industrial model technology platform for enterprise applications, industrial ontology agent platform, ChatRobot industrial body intelligent robot, ChatCAD generative AI industrial design software, etc.

Qizhi Kongming AInnoGC Industrial Big Model is an intelligent solution tailored specifically for the industrial and intelligent manufacturing fields. Its core functions include human-computer dialogue, text generation, and SQL code generation, which are not only suitable for factory management and intelligent manufacturing training, but also provide an efficient and professional Q&A platform for manufacturing practitioners.

Qizhi Kongming Industrial Big Model deeply cultivates vertical scenarios in the manufacturing industry, with manufacturing scenarios as the core, focusing on the implementation of equipment research and development drawings, production equipment management, energy management, production data analysis, equipment intelligent control, intelligent manufacturing training and other fields. According to IDC data, Qizhi Kongming ranks first in the market share of industrial big model applications in China and has been selected as the leading enterprise in Shandong Province's industrial big model unveiling list.

In the future, Innovation Intelligence will focus on planning two aspects:

Firstly, further integrate the industrial large-scale model into automation construction. The industrial ontology platform has established the relationship between equipment, processes, materials, quality inspection standards, and production indicators in the manufacturing industry. Subsequently, when developers input business requirements, the platform can complete industry context based on ontology knowledge, allowing the generated workflow agents to have manufacturing semantics from the starting point, reducing the cost of repeated manual corrections.

Secondly, explore data flywheel mechanisms applicable to the manufacturing industry. Under the premise of enterprise authorization, data isolation, desensitization, and permission control, the data generated during the trial operation, manual testing, and online feedback of workflow intelligent agents can gradually contribute to the generation, verification, and optimization capabilities. Our goal is to provide the platform with a deeper understanding of manufacturing processes after each use. At the next factory and production line, the starting point for building workflow intelligent agents can also be higher.