The challenge
Titles and keywords rarely describe every useful moment in a video. Finding a scene can mean scanning footage manually or relying on incomplete metadata.
The workflow
Choose one to three sample videos
Create frame or scene descriptions
Store searchable representations
Return a match with frame and timestamp
A closer look
Ask, “Show me the moment when the shark attacks.” The planned prototype returns the closest matching frame or scene with its source video, timestamp, and description.
Evidence & status
- A limited sample and minimum data contract are specified.
- The planned demo calls for three representative natural-language queries.
- Model, retrieval index, and suitable sample assets still need to be selected.
- The approximately 48-hour estimate is a proposed implementation timebox, not a completed result.
Proposed proof of concept with open choices for models, vector retrieval, and suitable video assets. It does not include continuous catalog processing or production media infrastructure.
An engineering decision
Start with sampled frames or one keyframe per scene. A small retrieval flow can test the idea without recreating production-scale ingestion.
The opportunity
The opportunity: faster discovery of moments and a clear view of what a larger implementation would require. Search quality, performance, adoption, and business impact have not been measured.
Adapted from “NAB Oct 2026 — AI Use Cases & Stories.” Statuses and findings reflect that source. Names are listed as project contacts, not credited authors.