Understanding the impact of orebody mineralogy on process performance is fundamental to the exploration, design, and operation of mineral processing circuits. Beyond mineral distribution in plant feed, the liberation and association of valuable, gangue, and deleterious minerals strongly influence operational outcomes. Access to timely mineralogical data enables process engineers and Advanced Process Control (APC) systems to adjust chemistry and equipment parameters for improved performance. Evolution Mining Northparkes Operations (NPO) has been an industry leader in incorporating mineral liberation analysis into its routine workflows, generating deep insights into how mineralogy affects key process indicators (KPIs). While conventional techniques such as scanning electron microscope (SEM)-based liberation analysis continue to provide valuable information, their high capital and operating costs, combined with slow turnaround times, limit their use for real-time decision-making. MinDet’s AI-based rapid optical mineralogy platform, Emago, seeks to address these challenges by developing a measurement system that focuses on reducing both cost and turnaround time. Emago is established to deliver critical mineralogical information within an hour of sample collection, analysing thousands of particles per minute with minimal preparation. Rather than replacing established techniques, the Emago platform complements them by targeting daily, operation-level responses to feed variability-shifting mineralogical analysis from monthly to near real-time decisionmaking. This paper presents the MinDet Emago pathway and learning at NPO, outlining the challenges encountered and the improvements made to the sample preparation methodology, sensing, data acquisition and the AI algorithm. It also compares the initial results with SEM-EDS (energy dispersive spectroscopy) data, demonstrating the technology’s potential to transform mineralogical analysis into a practical daily operational tool.