Content-Adaptive Encoding (CAE) has transformed video compression by enabling encoding settings to be dynamically tailored to the unique characteristics of each video or scene. Originally pioneered by Netflix, CAE began with per-title encoding in 2015 and evolved into per-shot and chunk-based optimization approaches in the years that followed. However, Netflix’s implementations typically required multi-pass encoding and complex analysis pipelines, which are computationally intensive and challenging to scale cost-effectively in production environments. To overcome these challenges, VisualOn introduces an AI-powered, one-pass framework—Universal CAE VisualOn Optimizer—that delivers comparable or even superior results with significantly reduced complexity. This tutorial presents the architecture and methodology behind the Optimizer, which uses a machine learning classifier trained on spatial and temporal texture features extracted from video segments.