End-to-end food ordering chatbot using natural language processing

Abstract

Chatbots have become increasingly valuable for enhancing user engagement and delivering efficient customer service across digital platforms. By utilizing Natural Language Processing (NLP), these intelligent systems can interpret and respond to user inputs in real time, providing tailored support and simplifying interactions. The surge in popularity of online food delivery has driven the adoption of AI-driven chatbots to automate order management and improve user satisfaction. This paper introduces a food ordering chatbot developed using NLP techniques and Google Dialogflow. The system is designed to manage customer inquiries, facilitate order placement, and connect with a backend ordering infrastructure. Key components such as system architecture, intent detection, entity recognition, and API integration are explored. Evaluation results demonstrate that the chatbot enhances response speed, minimizes manual intervention, and boosts overall user experience.