โก Quick Summary
This study introduces an innovative Enterprise Intelligent Brain platform designed specifically for small and medium-sized enterprises (SMEs), utilizing ChatGLM and a multi-agent system. The platform significantly enhances semantic adaptability and intelligent responsiveness, achieving a remarkable task completion rate of 99.904%.
๐ Key Details
- ๐ Datasets Used: Baidu DuReader-Enterprise, E-commerce Dialogue Dataset, Enterprise Knowledge Graph-Based Q&A Dataset
- โ๏ธ Core Technology: Chat Global Language Model (ChatGLM)
- ๐งฉ System Architecture: Semantic parsing, task scheduling, knowledge support
- ๐ Performance Metrics: Task completion rate 99.904%, average response time 0.858 seconds, context retention score 0.953, user satisfaction rating 4.767
๐ Key Takeaways
- ๐ก Tailored Solutions: The platform addresses specific needs of SMEs, such as policy consultation and customer service.
- ๐ค Multi-Agent Coordination: Enhances task allocation and improves operational efficiency.
- ๐ Domain-Specific Fine-Tuning: ChatGLM is fine-tuned for better relevance and precision in SME contexts.
- ๐ Knowledge Graph Integration: Utilizes knowledge graphs for improved contextual accuracy.
- ๐ Robust Performance: Demonstrated strong capabilities in knowledge invocation coverage and error recovery.
- ๐ Empirical Validation: Evaluated against three public datasets, confirming superior performance over baseline models.
- ๐ High User Satisfaction: Achieved a user satisfaction rating of 4.767, indicating positive user experiences.
- ๐ Practical Framework: Provides a scalable solution for deploying LLMs in SME environments.
๐ Background
The integration of large language models (LLMs) into business operations has shown promise, yet SMEs face unique challenges. These include issues like semantic overgeneralization and a lack of alignment with specific enterprise knowledge. Addressing these challenges is crucial for enhancing operational efficiency and decision-making in SMEs.
๐๏ธ Study
This research aimed to develop a platform that meets the semantic demands of typical SME operations. By constructing a triadic system architecture that integrates semantic parsing, task scheduling, and knowledge support, the study sought to improve the semantic adaptability and intelligent responsiveness of the ChatGLM model in real-world scenarios.
๐ Results
The proposed system outperformed baseline models across multiple metrics, achieving a task completion rate of 99.904% and an average response time of just 0.858 seconds. Additionally, it maintained a context retention score of 0.953 and a user satisfaction rating of 4.767, showcasing its effectiveness in complex SME environments.
๐ Impact and Implications
The findings from this study have significant implications for SMEs looking to leverage AI technologies. By providing a practical and scalable framework for deploying LLMs, the platform can enhance intelligent decision-making and automate services, ultimately leading to improved operational efficiency and customer satisfaction.
๐ฎ Conclusion
This research highlights the transformative potential of integrating AI and multi-agent systems into SME operations. The development of the Enterprise Intelligent Brain platform not only addresses existing challenges but also paves the way for future advancements in semantic intelligence within the business sector. Continued exploration in this field is essential for unlocking further innovations.
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Building an intelligent brain platform for small and medium-sized enterprises using ChatGLM and Multi-Agent Systems.
Abstract
Large language models (LLMs) have demonstrated strong capabilities in semantic understanding and text generation. However, their direct application in the segmented and specialized domains of small and medium-sized enterprises (SMEs) presents several challenges. These include semantic overgeneralization, poor alignment with enterprise-specific knowledge, and insufficient domain expertise. To address these limitations, this study proposes an “Enterprise Intelligent Brain” platform tailored to the business needs of SMEs. The platform is built upon Chat Global Language Model (ChatGLM) and is enhanced through a multi-agent coordination mechanism and structured support from enterprise knowledge graphs. The study centers on improving the platform’s semantic adaptability and intelligent responsiveness in real-world enterprise scenarios. It begins by identifying the core semantic demands of typical SME operations-such as policy consultation, customer service, and business process execution-and constructs a triadic system architecture that integrates three key components: semantic parsing, task scheduling, and knowledge support. Methodologically, the platform applies domain-specific fine-tuning to the ChatGLM model to enhance relevance and precision. It also incorporates a multi-agent task allocation framework and utilizes knowledge graph reasoning to improve contextual accuracy and domain knowledge integration. The effectiveness of the proposed system is evaluated using three public datasets: Baidu DuReader-Enterprise, the E-commerce Dialogue Dataset, and the Enterprise Knowledge Graph-Based Q&A Dataset. Experimental results confirmed that the optimized system significantly outperformed the baseline model across multiple metrics. Notably, it achieved a task completion rate of up to 99.904%, an average response time as low as 0.858 seconds, a context retention score of up to 0.953, and a user satisfaction rating of up to 4.767. Additionally, the system demonstrated strong performance in knowledge invocation coverage and error recovery, indicating its robustness in complex and dynamic SME environments. Therefore, this study provides a practical and scalable framework for deploying LLMs in domain-specific SME contexts. It offers both a technical solution and theoretical insights for developing enterprise-grade semantic intelligence platforms capable of supporting intelligent decision-making and service automation.
Author: [‘Yuan D’]
Journal: PLoS One
Citation: Yuan D. Building an intelligent brain platform for small and medium-sized enterprises using ChatGLM and Multi-Agent Systems. Building an intelligent brain platform for small and medium-sized enterprises using ChatGLM and Multi-Agent Systems. 2026; 21:e0340964. doi: 10.1371/journal.pone.0340964