VideoCutterAI leverages computer vision and AI-driven scene detection algorithms to analyze video frames and identify natural transition points. Users can specify sensitivity thresholds, select output formats, and process multiple videos in batch mode. After cloning the GitHub repository and installing dependencies, a single command triggers automatic scene analysis, ffmpeg-based cutting, and export. The tool outputs neatly named clip files, preserving original quality. Developers can customize the detection model and integrate the cutter into existing pipelines. Suitable for quickly generating highlights, breaking down lengthy recordings, or preparing footage for further editing, VideoCutterAI streamlines the video post-production workflow from input to clip generation with minimal manual effort.