Jumper

Jumper is a machine learning-powered video editing assistant designed to help content creators, editors, and studios locate, navigate, and extract the most valuable segments from their footage without leaving their editing environment. Operating entirely on the user’s local device, Jumper eliminates the need for cloud uploads or internet dependency, ensuring privacy, speed, and offline accessibility. The tool functions as a search engine for video libraries, enabling users to find specific visual elements, scenes, objects, or spoken words across thousands of hours of footage in seconds. It integrates directly with major non-linear editing systems including Final Cut Pro, Adobe Premiere Pro, DaVinci Resolve, and Avid Media Composer, either as a standalone application or through dedicated plugins that embed search capabilities into the editor’s interface. At its core, Jumper uses deep learning models trained on both visual and audio data. Users can type a natural language query—such as “person walking on a beach at sunset” or “phrase about quarterly results”—and the tool will scan the entire local media library, returning ranked clips that match the description. This drastically reduces the time spent manually scrubbing through long timelines or raw footage. The tool supports multilingual speech recognition, making it useful for international productions where dialogue may be in different languages. For video professionals, Jumper offers precise timeline navigation. Once a clip is found, it can be instantly inserted into a sequence, or its in/out points can be adjusted within the search result view without leaving the plug-in. The search index is built and updated automatically as new media is added, and the index itself is stored locally. The system is designed to be “ag