VideoVector

Turn video and audio archives into searchable, source-linked metadata.

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Screenshot of VideoVector, Turn video and audio archives into searchable, source-linked metadata

What is VideoVector?

VideoVector is an audio analysis tool that turns video and audio archives into searchable, source-linked metadata. VideoVector converts video, audio and image libraries into structured, timestamped metadata and multimodal embeddings, enabling semantic search that returns source-linked moments and supports MediaRAG workflows.

VideoVector is a media intelligence service for organizations that sit on large video, audio and image collections and need to make them findable. Instead of relying on filenames and manual logging, it processes footage into time-aligned descriptions of actions, entities and relationships, so every claim in the output points back to a specific interval in the source. The system accepts either natural language questions or structured schemas, and returns ground truth you can audit rather than a black box summary. The core of the product is structured understanding. A clip is broken into segments, each carrying start and end times, scene type, event details and entity records (for example a named player linked to a team, or on-screen text tied to the person it belongs to). Fields that cannot be determined are marked as uncertain or unknown, which matters for research and compliance work where a confident but wrong answer is worse than an honest gap. Alongside this, VideoVector builds multimodal embeddings and MediaRAG workflows so a search for a described moment returns the source timecodes and surrounding context, not just a thumbnail. The tool is aimed at media and news archives, sports rights holders, documentary teams, and analytics groups that need to locate a play, a quote or a scene inside hours of footage. It also supports editorial planning: a single recording can be mapped into narrative chapters, each linked to proposed clip boundaries and original timestamps, which shortens the handover between research and edit. A live playground and a comparison page against TwelveLabs make the product easy to evaluate before committing, though pricing is not published on the homepage and must be requested through sales. It is a specialist tool for teams with real archive volume, not a casual clipping app.

How do you use VideoVector?

  1. 1Open the playground
    Start at the hosted playground to try the pipeline without installing anything. The page is the fastest way to see how sample footage is processed.
    VideoVector — Open the playground
  2. 2Upload or connect a small sample
    Begin with a short clip or a handful of images so you can inspect the output structure quickly. This keeps early iterations cheap.
  3. 3Define your output schema
    Decide which fields matter for your use case, such as event labels, entities or scene types. VideoVector shapes its descriptions around this schema while preserving source timestamps.
  4. 4Run a contextual search
    Describe the moment you need in natural language and review the retrieved passages with their original timecodes. Check that the surrounding context is correct before trusting the result.
  5. 5Review the documentation
    Use the docs to understand available output fields, uncertainty handling and integration paths. It is the reference for moving from playground tests to production.
    VideoVector — Review the documentation
  6. 6Talk to sales for production access
    Contact the team to discuss volume, pricing and deployment. Bring your schema and a sample library so the conversation is concrete.
    VideoVector — Talk to sales for production access

Pros and cons

Pros

  • Every extracted field keeps its source interval, so results are auditable rather than opaque.AI
  • Custom schema support lets output match your existing metadata model instead of a fixed template.AI
  • Handles video, audio and images in one pipeline with multimodal embeddings for cross-modal search.AI
  • Uncertainty notes flag ambiguous content, which reduces false confidence in research work.AI
  • Playground and a direct TwelveLabs comparison page make evaluation concrete.AI

Cons

  • Pricing is not published; you must contact sales before you can budget.AI
  • Homepage shows no details on API access, SDKs or prebuilt integrations with editing tools.AI
  • No free tier or trial terms are stated, so testing may require a sales conversation.AI
  • Advanced schema design and MediaRAG setup likely demand engineering time before payoff.AI
  • The product is oriented to large archives; small libraries may see little advantage.AI

How much does VideoVector cost?

Support

A documentation site is available for product reference, and a contact page routes questions to the team. The homepage does not describe support tiers or response times.

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Features

Structured understanding turns footage into time-aligned segments with actions, entities, relationships and on-screen text. Contextual discovery uses multimodal embeddings to return precise source passages. MediaRAG workflows connect retrieval to generated answers with source references. Editorial story mapping splits a recording into narrative chapters with proposed clip boundaries and original timestamps. Output follows a custom schema and flags uncertain fields.

Learn more

Frequently asked questions about VideoVector

  • How do you use VideoVector?
    The walkthrough on this page covers 6 steps: 1. Open the playground 2. Upload or connect a small sample 3. Define your output schema 4. Run a contextual search 5. Review the documentation 6. Talk to sales for production access.
  • What platforms does VideoVector support?
    VideoVector is available on Web App.
  • What are the limitations of VideoVector?
    Pricing is not published; you must contact sales before you can budget. Homepage shows no details on API access, SDKs or prebuilt integrations with editing tools. No free tier or trial terms are stated, so testing may require a sales conversation.

Status

StatusActive
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Added10/21/2026

Platforms

Web App

Pricing

Paid