Telmo SampaioOliveira, Pedro FilipeMatos, Paulo2026-05-202026-05-202025Sampaio, T.; Oliveira, P. F.; Matos, P. (2025). 3D-Chatbot: A Intelligent Conversational Agent Integrating RAG, NLP, And VR For Commercial Stores. In 11th International Conference on Engineering and Emerging Technologies, ICEET. Institute of Electrical and Electronics Engineers Inc. ISBN 979-833156755-2.979-833156755-2http://hdl.handle.net/10198/36745This paper presents a comprehensive 3D chatbot system that combines advanced AI capabilities with practical commercial applications in immersive virtual reality environments. The system integrates natural language processing, retrieval-augmented generation (RAG) architectures, and real-time speech processing to create seamless customer inquiry experiences. The implementation combines four main components: an intelligent data acquisition pipeline that transforms over 1,000 unstructured web documents into AI-ready knowledge bases; an advanced RAG system utilizing OpenAI's text-embedding-3-small model and Pinecone vector database for semantic search; multimodal speech processing with real-time speech-to-text conversion and lip-sync animation; and a Unity-based VR environment with professional avatar interactions. The results demonstrate exceptional performance with a 94.6% success rate across 65 test interactions, achieving response times under 100 milliseconds for knowledge retrieval and completing full voice response cycles in less than 3 seconds. The system maintains conversation context through a six-step interaction flow while accessing external knowledge sources, with 96.9% contextually appropriate responses and 92.3% conversation continuity in multi-turn dialogues. Performance optimizations ensure responsive operation under variable load conditions, demonstrating the viability of intelligent virtual assistants in three-dimensional commercial spaces.eng3D-chatbotConversational AINLPRAGVirtual reality3D-Chatbot: A Intelligent Conversational Agent Integrating RAG, NLP, And VR For Commercial Storesconference paper10.1109/iceet67911.2025.11424080