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In the wave of smart manufacturing, how can SMEs transform AI from concept to real productivity? KING's Solution, a professional injection molding machine manufacturer, has set an industry example. This year, KING's successfully developed and implemented its internal AI Agent system—“AI-powered Chief Engineer Agent” The project not only passed the final review of the “SME Transformation and Innovation Challenge” program promoted by the Small and Medium Enterprise and Startup Administration, MOEA, but also became a leading case of deeply integrating AI Agents with enterprise knowledge systems.
Pain-Point Driven: From Training to Practical Implementation
This project was jointly supported by the National Innovation and Entrepreneurship Association (NIEA) and the Taipei Computer Association, in technical collaboration with No. 7 Algorithm Integration Co. Ltd. The concept originated from CEO Charles Hsu's personal participation in the 16-week “AI University” training program in February 2025. Through internal pain-point assessment, the team identified two key challenges faced by the design department: ‘low machine model selection efficiency' and ‘difficulty in design knowledge transfer.' These challenges became the driving force for launching the AI Agent implementation project, turning theory into real AI application in the field.
By facilitating cross-department collaboration, KING's successfully combined its traditional injection molding know-how with advanced AI models, achieving digitalization and intelligent utilization of internal knowledge assets.
Smart Decision-Making: Enhancing Design and Knowledge Transfer
The “AI-powered Chief Engineer Agent” is an intelligent decision-making tool that combines generative AI with the company's internal knowledge base. Its strength lies in integrating scattered enterprise data assets, including machine specifications, design drawings, ERP data, and historical project records.
The system offers two core functions: First, it speeds up design decisions by analyzing customer samples or specifications to provide machine model recommendations, assess design requirements, and deliver calculation logic explanations, significantly reducing preparation time. Second, it shortens the onboarding time for new engineers through a local GPT-based interface that allows natural language queries for design knowledge and parameter logic, thus reducing reliance on senior engineers.
Impressive Results: Efficiency Boost and Error Reduction
The AI implementation delivered significant improvements. Average design decision time was reduced by over 25%, greatly enhancing R&D efficiency. Design errors were noticeably reduced, improving product quality. Additionally, smoother data flow enhanced responsiveness to customer inquiries and quotations.
Expert Recognition: Laying the Groundwork for Knowledge Inheritance
The project received unanimous praise from the review committee, stating “KING's determination and speed in AI implementation sets an example for SMEs.” Executive Director Sheila also commented that the system not only improves operational efficiency but also lays a strong foundation for long-term enterprise knowledge inheritance.
CEO Charles added that KING's will continue to optimize the AI system and plans to expand its use to sales and after-sales service, building a comprehensive smart service chain. The company sees this as a shareable industry asset and is committed to promoting the experience across the supply chain and machinery sector to accelerate overall industry digitization.
Adhering to the brand philosophy of “Green Solution, Machine Soulmate,” KING's views AI adoption as a key step toward smart manufacturing, and will continue advancing smart services and sustainable transformation built on knowledge and experience.
