The Permit Audit Automation Project aims to streamline the current workflow between safety documentation and excavation permits. At present, engineers face significant friction when analysing multiple document sets — such as Detailed Site Surveys (DSS), Locator Reports, and Dial Before You Dig (DBYD) plans — to identify underground assets and complete permit templates. This process is slow, repetitive, and prone to human error, as it requires cross-referencing unstructured data and manually producing markups and written documentation. The goal of the project is to develop an AI-driven system that consolidates these three datasets into one clear digital markup of underground assets. The AI will be capable of reading and interpreting DSS drawings, Locator reports, and DBYD plans to automatically generate a single visual representation showing asset locations and types. It will then use this information to pre-fill the relevant sections of the Word-based excavation permit template, effectively automating the most time-consuming and error-prone parts of the process. By integrating computer vision, document recognition, and natural language processing into the permit workflow, this project will reduce administrative burden, improve consistency, and enhance safety compliance. Ultimately, the system is designed to allow engineers to focus on higher-value technical analysis and site decision-making, while AI handles the data synthesis, markup generation, and documentation required for safe and efficient excavation approval.