> ## Documentation Index > Fetch the complete documentation index at: https://docs.getjumper.io/llms.txt > Use this file to discover all available pages before exploring further. # AI Video Analysis, Understanding, and Tagging > Learn how Jumper analyzes and automatically indexes visual content, speech, people, summaries, and technical metadata without manual video tagging. Jumper combines complementary AI video-analysis layers to help editors and compatible AI agents understand footage. The analysis creates a searchable local index of visual content, speech, recurring people, summaries, and supported technical metadata without requiring an editor to label every shot by hand. Each result stays connected to the underlying media and source time range. Jumper stores the index in separate [analysis files](/core-concepts/processing#analysis-files) and leaves the original video, image, and audio files unchanged. ## Four video-analysis layers Makes actions, objects, settings, compositions, visual style, and visible emotions searchable. Creates timestamped transcripts for finding dialogue, topics, and named speakers. Groups recurring people so editors can name them and find where they appear. 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](/interface/search-tab#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](/core-concepts/processing) 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](/guides/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: | Feature | Who creates it | Purpose | | ------------------------------------------ | ------------------------- | --------------------------------------------------------------------------------------------------------------- | | AI video tagging and analysis | Jumper | Makes the content and metadata of analyzed footage searchable without manual shot-by-shot labels. | | [Tag Collections](/guides/tag-collections) | Editors and collaborators | Saves selected segments or whole clips for review, comments, color coding, sharing, filtering, and later reuse. | 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](/core-concepts/visual-search#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](/core-concepts/machine-learning) and [Jumper compatibility](/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](/core-concepts/agentic-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](/core-concepts/speech-search), 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](/api-reference/introduction) 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](/core-concepts/data-sovereignty) for the privacy and data-flow details. See how local video understanding supports search and editing workflows. See how automated video logging fits into an editorial workflow. ## Related documentation Prepare footage for visual, speech, face, and Summary Analysis. Automatically analyze newly added media. Navigate layered descriptions while preserving source references. Let compatible agents work with Jumper's analysis and editing tools.