Question 1: How does your company specifically help manufacturing customers improve efficiency and reduce costs?

Reply: The company is committed to providing an easy-to-use AI toolchain, engineering AI scenarios, simplifying the construction process, and enabling manufacturing customers to integrate their own processes with AI like building blocks, thereby improving business efficiency, increasing revenue and profits. At the same time, various application systems are quickly coupled through AI toolchains to form a collaborative AI network, reducing the cost structure of AI applications and making AI affordable and effective for every enterprise.

Question 2: How is the application of AI toolchains in the manufacturing industry?

Reply: Currently, the manufacturing industry has a strong demand for this, and the company's business departments have received a large number of end-to-end business scenarios such as data processing, large model training and fine-tuning based on AI toolchains, as well as AI application development. This reflects the explosive growth in AI application implementation in the software field of manufacturing enterprises this year.

Question 3: How does the company use AI internally to improve efficiency?

Reply: The company improves internal efficiency through AI technology, especially in the development and testing direction, striving to achieve a cost reduction of 20% to 30%. At the same time, in terms of research and development, using large models such as DeepSeek to accelerate the development process, such as using AI agents to independently complete data preparation and annotation, and using AI to develop AI within the research and development department.

Question 4: What is the outlook for the ERP sector business?

Reply: The "large and comprehensive" architecture of traditional ERP systems is no longer able to adapt to the rapidly changing business needs of enterprises, especially in the context of intelligent manufacturing and global operations. ERP decoupling is an inevitable trend. By decomposing ERP into modular services such as procurement, inventory, production planning, etc., enterprises can flexibly combine functions as needed. In this process, the company relies on the experience accumulated from decoupling and restructuring ERP projects with super large clients to promote the methodology of enterprise application modernization and job modernization: retaining the core financial module during the restructuring process, gradually decoupling peripheral business systems, and using microservice architecture and low code platforms to achieve agile iteration. At the same time, decoupled modules are easier to integrate with AI technology (such as intelligent scheduling and dynamic pricing), improving system response efficiency. Currently, decoupling services have covered top customers in industries such as electronics and automotive, verifying the feasibility of taking small steps and running fast. In the future, they will further expand to the fields of new energy and high-end equipment.

Question 5: Apart from intelligent production, what other fields can apply AI technology?

Reply: AI technology is not only generative AI, but can also be widely applied in the fields of vision and logistics, such as multiple intermodal transportation, warehouse location selection, supply chain network distribution optimization, as well as equipment predictive maintenance, AI quality inspection and other scenarios.

Question 6: Is it possible to replicate the successful case of the recently signed AI big order in the future?

Reply: With the accumulation of AI capabilities and customer practical experience in the company, there will be more follow-up orders, and the size of each order may be different. However, the input of this experience and capability will enrich the company's practice and achievements in the entire AI field.

Question 7: How does the company view the calculation of the accuracy and efficiency improvement space of vertical models in application scenarios? What is the company's horizontal expansion capability in other manufacturing industries beyond its advantageous industries such as PCB?

Reply: The company is closely tracking the testing status of the new version of the long thought chain reasoning model internally to evaluate its accuracy and potential for improvement in application scenarios, but further quantitative analysis is needed. The company is actively expanding horizontally, especially based on breakthroughs in the PCB industry, by applying technology and experience to other fields such as PCBA and photovoltaic solar panels. By building a powerful model platform, it is possible to distill models applicable to different fields more efficiently, achieving rapid construction and application of intelligent agents.

Question 8: What are the strategic considerations for the future in robotics and related fields? How to integrate the existing advanced manufacturing R&D capabilities and resources into this rapidly developing market?

Reply: The company attaches great importance to the direction of intelligent integration of software and hardware, and has been investing in the upstream and downstream industrial chain of industrial robots since 2021. For example, it invested in Saigan Intelligence in East China, which focuses on the research and development of intelligent sensors such as 3D LiDAR; We also invested in Shanghai's Xiangong Intelligence, engaged in the research and production of robot controllers, mobile robots, and robot control software; And invested in Xinguang Intelligence in Guangdong, focusing on the overall delivery of production station robots for sheet metal and photovoltaic module frames. These layouts highly overlap with the company's customer base in the software direction and have wide applications in industries such as semiconductors, 3C electronics, lithium batteries, photovoltaics, and automotive components. In the future, we will further focus on software and AI applications, collaborate with robot hardware companies to create integrated solutions, and create more value for customers.

Question 9: Regarding the application of big model generative AI capabilities in the manufacturing industry, from the perspective of budget preparation and investment pace, what is the current state of customers? How to predict the pace of subsequent construction?

Reply: Currently, there is a strong demand in the manufacturing industry for the use of large model generative AI capabilities. For budget preparation, it is necessary to analyze the current situation of the enterprise, which may involve cutting out a portion of the existing budget for the exploration and application of new technologies, or adding a special budget. Sai Yi Information will assist clients in developing reasonable construction schedules and budget plans based on their specific needs, budget arrangements, and implementation cases.

Question 10: What progress has the company made in developing its overseas business?

Reply: The company established SIE International last year, marking the launch of its overseas business and setting up a subsidiary in Japan, further consolidating its market position in Asia. The overseas strategy has entered the implementation stage since the preparation stage last year, and it is expected to build the second growth curve of the company's business development in the next one or two years.