Four video-analysis layers
Visual analysis
Makes actions, objects, settings, compositions, visual style, and visible emotions searchable.
Speech analysis
Creates timestamped transcripts for finding dialogue, topics, and named speakers.
Face analysis
Groups recurring people so editors can name them and find where they appear.
Summary Analysis
Creates layered descriptions of long-form footage tied to progressively narrower source ranges.
AI video tagging and automated metadata logging
In Jumper, AI video tagging and automated video logging describe the AI-generated analysis and searchable index. Jumper does not write keyword tags into source media files. The four analysis layers above make picture, speech, people, and long-form context searchable. Metadata filters complement that analysis by narrowing results using supported properties such as date, duration, codec, resolution, frame rate, camera, or lens.How can I automatically tag or index video footage?
Analyze the media in Jumper and select the visual, speech, face, and Summary Analysis types the workflow needs. Editors can then search the generated index directly instead of assigning a keyword to every shot. For recurring ingest, Watch Folders can automatically analyze new media added to selected directories. The processing and Watch Folder guides cover settings such as analysis models, speech language, face collections, and exclusions.AI-generated indexing versus Tag Collections
AI-generated indexing and Jumper’s Tag Collections serve different purposes:
Tag Collections do not replace analysis, and automatic analysis does not create editor-curated Tag Collections.
Scene detection
When a visual query matches footage, Jumper detects the continuous time range where that query remains relevant and returns the range as a scene with a start time and end time. Scene detection is query-dependent: the returned range describes where the current visual search matches. Jumper does not automatically divide every media file into traditional editorial shots or claim that each scene boundary is a camera cut. See Scene detection and search results for the detailed behavior.Which AI can analyze or understand video?
Jumper analyzes video with a combination of local visual, speech, face, and summary models rather than relying on a single general-purpose model. Editors can search the resulting information in Jumper, and compatible AI agents can navigate the same analysis through Jumper’s local MCP integration. The available models and hardware support vary by platform. See Machine Learning models and Jumper compatibility for current requirements.How editors and AI agents use the analysis
Editors can directly search for visual concepts, spoken words, speakers, and people. They can also use Summary Analysis to understand unfamiliar or long-form footage before running a focused search. With agentic video editing, a compatible agent can inspect the same analysis, plan searches, retrieve relevant source ranges, review timelines, and prepare clips or sequences. Jumper performs the media analysis and retrieval locally; the agent orchestrates the workflow.Verification and source evidence
Summary Analysis provides orientation and context, not final evidence. Verify exact quotes against timestamped transcripts, identities against named face results or source frames, and edit points against the original media. Any factual claim derived from a summary should be checked against a transcript, frame, or source-media range before publication. This keeps the fast orientation layer separate from the evidence used in an edit.A local editorial system, not a cloud analysis service
Analysis runs locally on supported macOS and Windows computers. Jumper is an editorial application with a local Public API and MCP integration; it is not a cloud video-analysis service, and source footage does not need to be uploaded for analysis. See Local and Offline for the privacy and data-flow details.AI video analysis with Jumper
See how local video understanding supports search and editing workflows.
AI video tagging with Jumper
See how automated video logging fits into an editorial workflow.
Related documentation
Analyzing media
Prepare footage for visual, speech, face, and Summary Analysis.
Watch Folders
Automatically analyze newly added media.
Summary Analysis
Navigate layered descriptions while preserving source references.
Agentic video editing
Let compatible agents work with Jumper’s analysis and editing tools.