At the CMIF 2026 Lean Digital Innovation Conference held in Tianjin from September 5-7, Yuhe Technology showcased its enterprise-grade Agent training platform, LangHub. During the event, the company's Founder and COO Hu Rui, Co-founder Liao Can, and General Manager of the Northern Region Cai Zhenjie delivered three keynote presentations that directly addressed why manufacturing AI often gets stuck after reaching the "usable" stage. Their conclusion is that AI implementation must first deliver verifiable business results, then integrate into cross-departmental collaboration, and ultimately transform expert knowledge into reusable enterprise assets. For its proactive technical exploration and practical implementation efforts, Yuhe Technology was recognized as an "Outstanding Strategic Partner of CMIF 2026."

Answer One: Deliver Auditable Business Outcomes First

In his presentation titled "Full-Stack AI Driving Smart Manufacturing Value," Hu Rui emphasized that manufacturing enterprises evaluate AI with a very simple logic: they look at business results, not model capabilities. A qualified AI task must feature clear inputs, traceable processes, verifiable outputs, and defined accountability boundaries. The LangHub solution employs a human-machine collaboration mechanism where Agents handle heavy lifting such as retrieval, complex parsing, rule invocation, and draft generation, while leaving key decision points like pricing, anomaly judgment, and business commitments for expert confirmation. This approach makes enterprises confident enough to use and trust the system. One large display materials company connected over 20 business systems through LangHub, achieving production anomaly Q&A accuracy above 90% and tripling response efficiency. A leading automotive parts manufacturer compressed production plan creation from four hours to 20 minutes per instance, improving customer demand responsiveness by 20%. Another major shipbuilding enterprise shortened complex quotation drafting from four-to-five days to under 30 minutes, freeing professionals to focus on strategy and pricing exceptions.

Answer Two: Employee AI Proficiency Does Not Equal Organizational AI Capability

In the AI and digital transformation session, Liao Can shifted the perspective from single-point applications to building overall enterprise capability. Individual Chat tools and departmental knowledge bases have indeed improved local efficiency, but if context and experience remain scattered across personal accounts, enterprises cannot establish stable, replicable digital productivity. The LangHub approach centers on building an enterprise-level persistent memory system: Workspace maintains long-term context for customers, projects, and business lines. The persistent memory system enables Agents to understand corporate standards and historical decisions across tasks through global profiles, project contexts, and topic snapshots. The Main Agent scheduling mechanism automatically identifies objectives, assembles context, and dispatches Skills to convert open-ended requests into automated workflows. What enterprises gain is no longer a one-time generated answer, but a manageable, shareable, and iteratively improvable Agent collaboration capability.

Answer Three: Expert Judgment Methods Are the True Asset Worth Replicating

During the exhibition presentation, Cai Zhenjie provided an in-depth analysis of the underlying value of "expert digital employees." The biggest drawback of traditional knowledge bases is that they preserve large volumes of historical documents while losing the logic and experience experts use to handle exceptional cases. The LangHub solution packages expert logic as callable Skills: verified execution steps, applicable rules, and judgment criteria are standardized into reusable Skills. After version validation, these are imported into the capability library for future task reuse. A leading chip company built intelligent specification parsing and retrieval capabilities, tripling specification iteration efficiency and reducing technical support response time by 80%. In one intelligent equipment simulation scenario, the Agent automatically handled requirement analysis, product selection, and quotation drafting, compressing what was originally three-to-four days of work into just 20 minutes. The cycle runs from solving an immediate problem with a single task, to incorporating new rules through revision, to dynamically building organizational capability through reuse.

Exhibition Highlights: Moving from Feasibility to Implementation

Throughout the event, Yuhe Technology's booth A-048 attracted key conference guests for in-depth discussions. The team presented LangHub's product positioning, enterprise-grade Agent implementation paths, and manufacturing benchmark cases, focusing on how AI integrates into production, quotation, and pre-sales operations while continuously converting expert experience into organizational capability. Booth conversations delved into specific implementation details: how to improve parsing accuracy for complex multi-page business documents while reducing model hallucinations; how to maintain context continuity for long-cycle tasks spanning multiple weeks; how expert judgment methods can be validated and refined into Agent Skills; and how Agents can securely connect to ERP, MES, and other enterprise systems to generate official documents that flow directly into business processes. The transition from general model capabilities to concrete business tasks shows that on-site discussions no longer focus on whether AI can do something, but whether it can understand corporate rules, integrate into existing processes, and deliver trustworthy, auditable business results. These questions reflect that manufacturing enterprises are shifting their AI focus from capability demonstration to value verification.

Manufacturing AI's Next Step: Starting with One Specific Work Unit

Although CMIF 2026 has concluded, the digital transformation of manufacturing continues to accelerate. Enterprise AI transformation does not require massive system overhauls from day one. The most effective approach is to identify a specific scenario that is high-frequency, time-intensive, judgment-heavy, and result-verifiable, then let an Agent produce initial drafts, have experts review and refine, and finally consolidate the experience into reusable Skills. Through this iterative cycle, enterprises naturally build AI core capabilities that remain within the organization. This is the direction Yuhe Technology has consistently pursued: moving AI from answering questions to completing work, and transforming expert knowledge from individual minds into organizational assets.