Building an intelligent brain platform for small and medium-sized enterprises using ChatGLM and Multi-Agent Systems.

Overview

1. NongFu Store Development Group Co., Ltd, Beijing, China

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.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

No file of the authors' code could be read here: it is described below, and read at its source.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

• 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
• 0 scripts, each with its path and the digest of its content;
• no match between paragraphs and code yet;
• neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

• github.com/ownthink/knowledgegraphdata, at github.com; found in the text, “Experimental design”

Data Availability

All relevant data are within the paper and its Supporting Information files.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 1 author, 22 references.

Cite

This paper

Yuan, D. (2026). Building an intelligent brain platform for small and medium-sized enterprises using ChatGLM and Multi-Agent Systems. PloS one, 21(3), e0340964. https://doi.org/10.1371/journal.pone.0340964

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

No other paper with a page shares enough with this one yet: tools, categories, datasets, references or authors.

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

<!-- oscr-badge:start -->
[![OSCR](https://openscicode.org/badge/doi/10.1371/journal.pone.0340964.svg)](https://openscicode.org/badge/doi/10.1371/journal.pone.0340964)
<!-- oscr-badge:end -->

.. oscr-badge:start

.. image:: https://openscicode.org/badge/doi/10.1371/journal.pone.0340964.svg
   :target: https://openscicode.org/badge/doi/10.1371/journal.pone.0340964
   :alt: OSCR

.. oscr-badge:end

<!-- oscr-badge:start -->
<a href="https://openscicode.org/badge/doi/10.1371/journal.pone.0340964"><img src="https://openscicode.org/badge/doi/10.1371/journal.pone.0340964.svg" alt="OSCR"></a>
<!-- oscr-badge:end -->

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion: code errors and mismatches between the code and the paper about this paper and its code, from readers and its authors, beside the paper and its tracing map.

Report a mismatch between the code and the paper · Report a code error (signed in)

Reproductions: reports from readers who ran the authors' code and did not get the paper's result, what they ran, with which environment, commit and data.

Report a reproduction failure (signed in)

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.