Digital Media Platform · Media · Video
An AI pipeline that tags, categorises, and summarises a video library at scale.
Computer vision and speech-to-text feeding an LLM summariser — turning a 200k-hour video library into a searchable, structured archive.
200k+
hours of video processed end-to-end
9 min
average processing time per hour of video
+47%
lift in search-driven engagement on the platform
Media · Video
Digital Media Platform
Client
Digital Media Platform
Media · Video
Headline metric
200k+
hours of video processed end-to-end
Deliverables
4
shipped to production
Stack
5+ tools
across the build
Challenge
A digital media platform's 200,000-hour video library was almost entirely unsearchable. Editors hand-tagged the top 5%; the long tail rotted. Search-driven engagement was capped because the catalogue was dark.
Approach
Built a pipeline that ingests video, runs scene detection and computer vision tagging, transcribes audio via Deepgram, and feeds the transcript + visual tags into an LLM summariser. Outputs structured metadata: scene tags, speakers, topics, summaries, and chapter markers — all indexed for search and recommendation.
Outcome
200,000+ hours processed end-to-end in eight weeks. Average processing time of 9 minutes per hour of video. Search-driven engagement up 47% because the long tail of the catalogue is now discoverable.
200,000+ hours processed end-to-end in eight weeks. The team owned this end-to-end.
Digital Media Platform
Media · Video
hours of video processed end-to-end
average processing time per hour of video
lift in search-driven engagement on the platform
What we shipped.
- Video processing pipeline
- Scene + speaker tagging
- Search index
- Editor-facing review UI
The tools we used.
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