<![CDATA[The adoption of large language models (LLMs) in customer relationship management (CRM) has increased rapidly, but their use in micro, small, and medium enterprises (MSMEs) remains limited due to computational and cost constraints. This study explores the use of small language models (SLMs) as an efficient alternative for customer service chatbots, using parameter-efficient fine-tuning (PEFT) with Low-Rank Adaptation (LoRA) and comparing it with retrieval-augmented generation (RAG). This research follows the Design Science Research (DSR) approach with a case study on a local garment production business in Pontianak. The Qwen2-1.5B-Instruct model is adapted using LoRA and deployed on a 6GB GPU. Evaluation is conducted through quantitative and qualitative methods. Results show that the base model performs poorly without adaptation. The LoRA approach achieves the most stable performance, with intent accuracy up to 95%–100% and 0% hallucination rate, while RAG improves contextual understanding but lacks output consistency. The study concludes that domain-specific efficient fine-tuning is crucial to enabling SLM-based CRM solutions for SMEs.]]>
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