Improving Access to Building Licensing Information in Australia: Design and Development of a Graph-Based Retrieval-Augmented Generation (RAG) Artificial Intelligence (AI) System

Digital technologies have been widely adopted to improve efficiency, transparency, and decision making in the construction industry. However, regulatory processes such as building license and registration applications remain complex, fragmented, and difficult for applicants to navigate, particularly for early career practitioners and small businesses. This study presents the design and development of a graph-based retrieval-augmented generation (RAG) artificial intelligence (AI) system that assists users in applying for building licenses and registrations in New South Wales, Australia. The proposed approach integrates eight complementary frameworks of regulatory burden and service design to identify ten categories of licensing-related burden and translate them into concrete system requirements. The developed prototype provides context aware responses, step-by-step guidance, and tailored information based on user queries, thereby reducing regulatory burden for individuals, companies, and industry bodies. Prototype evaluation against general-purpose AI tools indicates that the system can improve information accessibility and reduce application-related friction in representative licensing scenarios. This study sheds light on AI-enabled regulatory support systems and demonstrates how RAG can be applied to improve accessibility and usability of construction related licensing processes. The findings have implications for policymakers, regulators, and researchers seeking to leverage AI to support digital transformation in the construction industry.