From Menus to Management: An Integrated Platform Leveraging Large Language Models in Automatic Restaurant Ordering Systems

The restaurant industry struggles with managing high volumes of phone-based customer interactions, particularly during peak hours, leading to operational inefficiencies, increased labor costs, and diminished customer satisfaction. Inspired by the rise of large language models (LLMs) in recent years, in this paper, we introduce an AI-driven customer service application leveraging fine-tuned LLMs to address these challenges by integrating speech recognition, text generation, and text-to-speech technologies for seamless real-time interactions. Using domain-specific conversational data from a local restaurant, the system fine-tunes state-of-the-art LLMs, including Llama-3.2-1B and Llama-2-7B, to generate human-like responses tailored to the restaurant industry. The architecture consists of client-side voice-to-text and text-to-voice components alongside a server-side AI backend, ensuring efficient processing and a smooth user experience. Experimental results show that the fine-tuned Llama-2-7B model delivers superior accuracy and robustness, while GPU utilization significantly enhances response latency and overall performance. Through our designed platform, we demonstrate the potential of domain-specific LLM fine-tuning in transforming customer service applications by automating phone-based interactions, reducing operational costs, and improving customer satisfaction. Our demo video can be reached with the link: https://www.youtube.com/watch?v=K_qQaueAmtA.