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qortex/ai-data-teams

Video data you can trace back to the frame.

For AI and data teams, the hard part of video is not storage. It is knowing what is inside it, and proving it. Qortex provides structured signals about brands, content and creative, with timestamped evidence where available, so your team can connect results to the source.

Developer page asks

How do the signals plug into our system?

Integration patterns
This page asks

What do the signals do for our data, our retrieval and our evals?

Three workflows

Enrich. Retrieve. Evaluate.

Illustrative workflows, not shipped features. Field names and values below are conceptual examples, not a schema or endpoint.

W-01 / workflowIllustrative example

Dataset enrichment

Attach structured, timestamped signals to the video records you already hold, so a dataset can be sliced by brand, topic, scene or creative attribute instead of by filename and free-text tags.

  1. Video records
  2. Qortex signals
  3. Enriched rows
  4. Slices & audits
conceptual_record
video_id
"clip_0412"
brand
"Summit"
evidence
00:12 – 00:19
context
["arena", "basketball"]
Two layers of output

What is in the video, and what it is saying.

Content Intelligence

What appears in video and where: brands across logos, on-screen text and speech, plus the topics and scenes around them.

Where data teams could use it
  • Dataset slicing by entity and context
  • Filterable retrieval metadata
  • Coverage audits across a corpus

Creative Intelligence

What an ad communicates: themes, tone, products shown and calls to action.

Where data teams could use it
  • Labelled attributes for creative sets
  • Comparing briefed vs delivered messages
  • Evaluation targets for creative claims

Bring a sample of your video content and the question you need answered.

Tell us about your data and what you want to enrich, retrieve or evaluate. We’ll talk through which signals are worth testing first.

Talk to the Qortex team