Open Access

REVIEW

From Documents to Decisions: Enterprise-Grade LLM Systems for Zero-Hallucination, Attributed Generation, and Regulatory Alignment

Yenjou Wang¹, Chihtan Cheng², Jia-Wei Chang^3,*

1 Department of Information, Artificial Intelligence and Data Science, Daiichi Institute of Technology, Taito, Tokyo, Japan
2 Ph.D. Program in Intelligent Engineering, National Taichung University of Science and Technology, Taichung City, Taiwan
3 Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, Taichung City, Taiwan

* Corresponding Author: Jia-Wei Chang. Email:

Computer Modeling in Engineering & Sciences 2026, 147(2), 8 https://doi.org/10.32604/cmes.2026.080888

Received 17 February 2026; Accepted 20 April 2026; Issue published 27 May 2026

Abstract

As large language models (LLMs) become increasingly integrated into enterprise decision-making processes, structural pressures such as version drift, cross-source evidence integration, and regulatory accountability have shifted the primary challenge from isolated generative performance to system-level consistency, traceability, and governability. This paper systematically reviews key technological developments relevant to enterprise requirements, including document perception, retrieval-augmented generation (RAG), hybrid RAG-KG architectures, fine-grained attribution evaluation, and multi-agent coordination. The analysis demonstrates that the main obstacle to enterprise LLM adoption is not model capability, but rather the structural gap between fragmented technical modules and the need for high-reliability decision-making. In response, a risk-controlled data flywheel architecture is proposed that integrates perception, reasoning, verification, and governance layers. By converting reasoning outputs into observable risk signals and feeding them back into retrieval and structural components, this architecture establishes a continuous improvement loop. This approach provides a systematic deployment blueprint for enterprise-grade LLM systems, emphasizing traceability, accountability, and sustainable optimization in high-risk and long-term operational contexts.

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Keywords

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