Large Language Model (LLM)-based question-answering systems offer significant potential for automating customer support and internal knowledge access in small businesses, yet their practical deployment remains challenging due to infrastructure costs, engineering complexity, and security risks, particularly in retrieval-augmented generation (RAG)-based settings. This paper presents an industry case study of an open-source, multi-tenant platform that enables small businesses to deploy customised LLM-based support chatbots via a no-code workflow. The platform is built on distributed, lightweight k3s clusters spanning heterogeneous, low-cost machines and interconnected through an encrypted overlay network, enabling cost-efficient resource pooling while enforcing container-based isolation and per-tenant data access controls. In addition, the platform integrates practical, platform-level defences against prompt injection attacks in RAG-based chatbots, translating insights from recent prompt injection research into deployable security mechanisms without requiring model retraining or enterprise-scale infrastructure. We evaluate the proposed platform through a real-world e-commerce deployment, demonstrating that secure and efficient LLM-based chatbot services can be achieved under realistic cost, operational, and security constraints faced by small businesses.

翻译：基于大语言模型（LLM）的问答系统为中小企业自动化客户支持与内部知识访问提供了巨大潜力，但其实际部署仍面临基础设施成本、工程复杂性和安全风险等挑战，尤其是在基于检索增强生成（RAG）的场景中。本文介绍了一个开源多租户平台的行业案例研究，该平台使中小企业能够通过无代码工作流部署定制的基于LLM的支持聊天机器人。该平台构建于跨异构低成本机器的分布式轻量级k3s集群之上，并通过加密覆盖网络互联，在实现成本效益资源池化的同时，强制执行基于容器的隔离和按租户数据访问控制。此外，平台集成了针对RAG聊天机器人提示注入攻击的实用平台级防御机制，将近期提示注入研究的前沿见解转化为可部署的安全方案，且无需模型重训练或企业级基础设施。我们通过一个真实电子商务部署场景对所提平台进行评估，结果表明在中小企业面临的现实成本、运营和安全约束下，能够实现安全高效的基于LLM的聊天机器人服务。