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Video Intelligence turns a video into searchable evidence. It transcribes speech, reads visible text, detects objects and anonymous tracks, and preserves timestamps so chat can point back to the relevant moment. Its planner is genre-neutral: it adapts to the question, duration, and measured source signals rather than selecting a hardcoded workflow for lectures, sports, films, entertainment, meetings, or other content. Visual verification records generic subject–relation–value bindings when a clip directly establishes them. For each chat question, the model first supplies a typed investigation directive: a local moment, a temporal relationship, or the whole source; then an answer, comparison, trace, enumeration, or synthesis operation. The evidence engine validates and executes that directive without routing from a language-specific keyword list. Whole-source enumeration reads chronological evidence rather than treating one highly ranked moment as the entire video. During indexing, vision works in chronological waves: identities become continuity candidates only when source evidence grounds them, and similarity of appearance alone is never treated as identity proof.

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Install the tool

Open Settings → Marketplace, install Video Intelligence (New), then open Data → Media → Video. Before indexing, the guide can collect an optional goal, known entities, and questions you expect to ask. The optional goal is a focus, not a filter: the index still keeps source-wide coverage, while the planner and semantic reader spend additional attention on the details the user named. Larkup stores that hint with the media asset, sends it in the ephemeral indexing brief for local or Cloud work, and records it with the resulting knowledge revision so a later reindex remains auditable. The hint text is never placed in usage analytics.

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What the index contains

  • Timestamped multilingual speech from faster-whisper.
  • Visible text from slides, code, scoreboards, signs, and captions.
  • Objects and anonymous motion tracks detected across the video.
  • Source-language story notes that connect speech, named participants, visual layout, and events across scenes.
  • Coverage metadata and an answering guide used to ground chat responses.
Balanced is the normal choice. Use Fast for a quick overview and Thorough for denser visual changes or short-lived details. Before any runtime starts, choose an audio provider and add its API key in the tool settings. Its transcription model is selected automatically. Agent and vision controls default to their providers, models, and keys from Settings → AI Models, while allowing independent per-tool overrides. When the agent uses a text-only provider such as DeepSeek, select a separate vision provider. These accounts pay for model calls; Larkup Cloud provides only the GPU compute that decodes, samples, reads, and structures the video.

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Choose where it runs

Use this when you want processing to remain on your machine. Select Local runtime in the tool settings. Larkup detects Docker automatically and uses it when available; otherwise it installs and runs the native CPU runtime with uv. The runtime starts on 127.0.0.1 and the source copy inside the runtime is deleted after successful indexing by default.The install confirmation shows the selected runtime, download size, available RAM, and model configuration. Saved AI Models settings supply the initial brain and vision models and keys; the tool settings can override them. The selected provider keys are injected only into the local process or container.

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Ask questions in chat

After indexing, ask direct or indirect questions such as “What changed near the end?”, “Which steps were shown before the error?”, or “Summarize the argument and the evidence on screen.” Larkup retrieves the timestamped video evidence first and reads compatible observations as one chronological trail. It performs a bounded source inspection only when that indexed trail is incomplete, conflicting, or cannot ground the requested identity. If an answer uses general knowledge beyond the video, it labels that part as an inference.