Abstract
This paper explores the integration of Large Language Models (LLMs) and secure Gen-AI technologies within engineering design and manufacturing, with a focus on improving inventory management, component selection, and recommendation workflows. The system is intended for deployment and evaluation in a real-world industrial environment. It utilizes vector embeddings, vector databases, and Approximate Nearest Neighbor (ANN) search algorithms to implement Retrieval-Augmented Generation (RAG), enabling context-aware searches for inventory items and addressing the limitations of traditional text-based methods. Built on an LLM framework enhanced by RAG, the system performs similarity-based retrieval and part recommendations while preserving data privacy through selective obfuscation using the ROT13 algorithm. In collaboration with an industry sponsor, real-world testing demonstrated strong results: 88.4% for Answer Relevance, 92.1% for Faithfulness, 80.2% for Context Recall, and 83.1% for Context Precision. These results demonstrate the system’s ability to deliver accurate and relevant responses while retrieving meaningful context and minimizing irrelevant information. Overall, the approach presents a practical and privacy-aware solution for manufacturing, bridging the gap between traditional inventory tools and modern AI capabilities and enabling more intelligent workflows in design and production processes.