AI Agent+Manufacturing | SIE Manufacturing Intelligent Agent Consolidates Full Stack Innovation Power, Breaking through AI Applications
When you analyze code with DeepSeek
AI is parsing billions of rows of industrial data
When Midjourney generates art masterpieces
Algorithms are also designing precision mold drawings
When Tesla FSD learns human driving
The machine vision of smart factories has achieved 0.01mm level detection
……
The beacon of global AI competition
It is burning from the consumer Internet to the main industrial battlefield
SIE is making use of the whole stack of information
Drive the evolution of intelligent agents in the manufacturing industry
The combination of AI and manufacturing is becoming the core battlefield of a new round of productivity revolution. China's manufacturing industry has been ranked first in the world for fifteen consecutive years, accounting for over 30% of global production capacity. Its demand for transformation and upgrading is more urgent than any other industry. However, in the journey towards AI, the manufacturing industry is facing many difficulties such as limited computing power by humans, model adaptation to local conditions, and fragmented toolchains.
A dilemma
Four major challenges in AI driven manufacturing
Computing power dependenceHigh end GPU chips are monopolized overseas, with high costs and the risk of supply interruption. The domestic GPU market share is less than 10%, and there are obvious performance and ecological shortcomings, making it difficult to meet the training needs of large models. The cost of computing power and the risk of supply interruption have become a heavy burden for the AI driven manufacturing industry.
The adaptation of general large models to manufacturing scenarios is difficult, facing challenges in understanding professional terminology, adapting process logic, and integrating long chain scenarios in manufacturing. The nature of large models based on "probabilistic reasoning" leads to unstable output in complex industrial scenarios, making it difficult to accurately meet production needs.
Model training requires a large amount of computing power resources, while private deployment has huge costs and insufficient open source resources. In addition, data organization in the manufacturing industry takes more than 3 months, and small and medium-sized enterprises rely solely on free models to try simple functions, making it difficult to delve into core business scenarios. The time-consuming data processing and lack of development resources severely restrict progress.
The technological ecosystem is fragmented, with toolchains and business systems fighting independently, forming new "data islands". The implementation of AI in manufacturing requires the support of experts in specific fields, and the implementation process is essentially a "replacement revolution" for traditional processes. Ecological fragmentation and implementation complexity hinder the deep integration of AI and manufacturing.
What China's manufacturing industry needs is not only AI technology, but also a set of fully controllable, deeply adapted, and ready to use full stack solutions. SIE, which has been deeply engaged in the digitalization of manufacturing industry for 20 years, is providing a reproducible and landing AI upgrading path for Chinese enterprises with its full stack of information and innovation capabilities of "Chinese computing power+Chinese model+Chinese AI tool chain+Chinese made AI building block application".
Broken
Practice of implementing full stack information and innovation capabilities
Chinese computing power:
Building a solid foundation of autonomy and controllability
SIE actively embraces domestic computing power. Its self-developed Shanmou GPT platform has been perfectly adapted to Huawei Shengteng 910B chip, realizing the adaptation of domestic computing power and supporting domestic computing power. With the help of domestic computing power, the cost of enterprise model training has been significantly reduced, ensuring the secure storage and processing of data in China, avoiding the risk of data leakage, and building a secure foundation for enterprise digital transformation, allowing enterprises to confidently apply AI technology.
Chinese model:
Rooted in business scenarios, addressing industry pain points
Through the independently developed Shanmou GPT platform, we have taken the lead in launching a vertical industry model in the PCB industry - the "PCB Industry Model Based on Shanmou GPT". In the practical process, the original 4-6 hour work was shortened to 4-6 minutes, with an accuracy rate of over 95%, significantly improving the production efficiency and competitiveness of the enterprise.
In the future, SIE Information will also use its experience in PCB and other advantageous industries to expand horizontally to photovoltaic, new energy and other fields, integrate resources, optimize model performance and computing power distribution, and build a more powerful model platform.
Chinese AI toolchain:
Improve development efficiency and accelerate scene construction
It takes 5-6 months for traditional enterprises to build AI scenarios. Through a set of AI tool chain, SIE Information can quickly couple various application systems and build an application AI network of multi-agent cooperation, reducing the time to half.
Application of Chinese made AI building blocks:
Accelerate the transition from "single point intelligence" to "global evolution"
Based on AI toolchain, the challenges of construction engineering can be modularized and application scenarios can be simplified, enabling manufacturing customers to choose and combine these modules at a lower cost and faster speed. Enterprises can quickly build personalized application systems based on their own business processes and needs, like building blocks, thereby accelerating business speed, increasing revenue and profits.
In the manufacturing industry, AI has changed from a selectable topic to a mandatory topic. Only by organically combining AI with business scenarios can it unleash true transformative power and activate the potential of the industry. SIE is actively engaged in AI capacity building, continues to deepen product layout, and continues to increase product R&D investment in the direction of DeepSeek cost reduction+AI tool chain application cost reduction.
effect
AI driven innovation and upgrading in manufacturing industry
The value of AI lies not in showcasing its skills, but in solving the challenges faced by humans. SIE has actively engaged in AI capacity building, and has achieved initial results in addressing the AI challenges in the manufacturing industry.
PCB industry:
PCB industry big model based on Shanmou GPT
SIE's "PCB industry model based on Shanmou GPT" has completed the compatibility test of Huawei AI framework MindSpot, and has obtained the right to use Huawei's Ascension Technology Certification and Ascend Compatible logo. It has also accessed general models such as DeepSeek to enhance multimodal capabilities, further expanding the application boundary, and currently empowering 10+customers to improve quality and efficiency.
Photovoltaic industry:
Optimization Model for Battery Screen Printing Process
SIE has built a battery screen printing process optimization model for a photovoltaic enterprise. In response to the pain points of high cost proportion of silver paste, difficulty in controlling paste viscosity, and dependence on experience for manual parameter adjustment, we use big model technology to mine and train automatic reverse control processes based on multidimensional historical data. It is expected to save millions of costs for enterprises every year, while speeding up process debugging and ensuring product quality.
Non motor vehicle manufacturing industry:
Intelligent Operation and Maintenance - Automated Order Processing and Approval
SIE builds an automated single processing system through AGENT+SIE ITSM operation and maintenance platform, integrating intelligent customer service and enterprise knowledge base. Through GPT customer service access, instant messaging conversations, intelligent queries, interactive Q&A, and other functions can be achieved. Leveraging AIGC middleware management knowledge, it connects human resources, finance, IT, and other processes to efficiently process work orders and improve enterprise operation and maintenance efficiency
At the beginning of 2025, SIE Information also announced that it had signed 48 million orders for individual AI industrial applications, focusing on AI application scenarios such as intelligent networking development and product design, covering end-to-end capabilities such as data processing, large model training and fine-tuning, and AI application development based on AI tool chain, and has the ability to access DeepSeek's large model. This signing is a major achievement of SIE in high-value AI business scenarios such as intelligent networking and product design, and also a high-quality milestone in the process of technological breakthrough and application implementation of the big model, which strongly verifies the commercialization feasibility of the application of the big model of AI and injects new vitality into the development of the industry.
In the future, SIE Information will continue to deeply cultivate the manufacturing industry, with the full stack information and innovation capability of "Chinese computing power+Chinese model+Chinese AI tool chain+Chinese made AI building block application", the dual wheel drive of "AGENT+pan ERP" and "AI+intelligent manufacturing", to help Chinese manufacturing industry go deep and solid, and stand out in the global competition.
