What is 'find me the moment'? Semantic video search across archives explained

Short answer: 'Find me the moment' is semantic search across video archives. You type a plain-English description of the bit you half-remember and the agent returns the exact media file and frame range in under two seconds. It works because every asset is indexed as vector embeddings at three levels - description, scene and segment - so queries match meaning rather than filenames or transcript keywords.

Why does the old way of finding archive footage break down?

Most newsrooms still find video the way they did in 2010: a producer remembers the rough date, scrubs through rushes, or shouts across the desk. MAM systems help if the metadata was tagged at ingest, but tagging is the first thing that gets cut when a deadline lands. The result is years of footage sitting on storage that nobody can retrieve in time to use.

Keyword search on a transcript is better, but only just. It misses paraphrases, can't reason about what is happening on screen, and returns hundreds of utterance-level hits with no sense of which ones matter. What producers actually need is a way to ask for an idea - "the bit where the minister gets visibly annoyed about the leak" - and get a frame range back.

"Producers spend an average of 10 hours per week scrubbing rushes for known clips." [Internal Electric Sheep customer benchmarks.]

How does the three-level embedding hierarchy work?

When you search, the agent runs against the right level for the question. Asking for an idea ("environmental protest footage from last summer") hits description and scene vectors. Asking for a quote hits segment vectors. Asking for both - "the protest where someone shouts about the river" - runs in parallel and merges the results.

What does a worked example look like, from query to result to edit?

Say a producer at a national newspaper wants to cut a 60-second explainer on housing policy. They remember an interview from a few months ago where the housing minister got cornered on a specific number. The flow looks like this.

Behind the scenes, the agent batches the semantic search, the audio-timing refinement, the reframe call and the caption render in parallel - because every LLM turn costs 15 to 30 seconds and serial calls would feel slow. The producer never sees that orchestration; they see a clip that lands roughly where they pictured it.

What does this unlock for archive monetisation?

Find me the moment is the capability that makes archive monetisation real. The site line - "monitors trends, monetises your archive" - only works if you can actually retrieve the relevant footage when a trend breaks. Without semantic search you are guessing; with it, the agent can scan archive against the day's trending topics and surface the clips that match a fresh angle, ready for a per-platform repackage.

This is the same primitive that lets the agent build story-driven b-roll. When a journalist writes a script, the agent semantically matches every line of narration against the archive and pairs it with the right shot - without anyone scrubbing through rushes. Find narratives, not moments - but you need to be able to find the moments first.

"Before find me the moment, our producers were spending half a day on a single archive pull. Now it's the first thing they reach for." - Jonty Harrison. Crowdsauced.

What is 'find me the moment' not?

Find me the moment is not a viral-clip generator that scores 30-second windows by some opaque "virality index". It does not invent footage, it does not extend a quote past the speaker's word boundary, and it does not let the agent commit anything to the timeline without a human approving the change list. It is retrieval - the precondition for every other clever thing the agent does.

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Frequently asked

What is 'find me the moment' semantic search? 'Find me the moment' is Electric Sheep's semantic video search. You type a plain-English description of the bit and the agent returns the exact media file and frame range in under two seconds, by matching meaning across description, scene and segment-level embeddings rather than filenames or transcript keywords.

How does semantic search work for video archives? Every ingested asset is embedded at 3 levels. Queries are routed to the right level and merged in parallel.

How is this different from filename or tag search?
Filename and tag search depend on someone having tagged the asset correctly at ingest, which is the first task to get cut when a deadline lands. Semantic search needs no manual tagging - it indexes meaning automatically, so paraphrases, on-screen context and visual cues are all retrievable from a single natural-language query.

How is this different from keyword search on a transcript?
Keyword search only matches the words a speaker actually said. Semantic search matches meaning - paraphrases, synonyms, on-screen context, even camera framing. It also returns a single best frame range rather than a list of utterance hits.

What makes the result frame-accurate? Segment-level embeddings carry word-level frame timing from Whisper plus diarisation, so once a quote is matched the agent knows the exact in and out frames. The find-audio-timing tool then refines the boundary so the cut starts on a clean word rather than mid-syllable.

How does a producer use 'find me the moment' day-to-day?
A producer types a sentence describing the clip they want, picks one of the candidates the agent returns in under two seconds, and the same agent loop handles boundary refinement, 9:16 reframe, caption burn-in and per-platform export. The producer reviews the change list and approves - total time from question to approved clip is minutes, not hours.

Does it work across multiple languages?
Yes. Embeddings are language-agnostic at the meaning level, so a query in English can return clips spoken in French, Arabic or Spanish if the meaning matches. Whisper plus diarisation handles the underlying transcription across the major newsroom languages.

How long does it take to index a new asset? Indexing happens once at ingest. Scene detection runs inline with transcoding, then audio and video metadata extraction and embedding generation run in parallel. Assets are searchable within minutes of upload, not hours.

Is my archive used to train the underlying models?
No. Electric Sheep does not train on customer media or workflows. Embeddings are stored per-tenant with user-keyed isolation, configurable data residency at the infrastructure layer, and clear data processing agreements.

Can I search across archives that pre-date Electric Sheep?
Yes - that is most of the value. Back-catalogue ingest is a standard part of the 7-day onboarding workshop. Years of historical footage become searchable without re-tagging by hand.

What happens when nothing matches?
The agent says so. The find-audio-timing tool returns a not_found with an explanation of which parts of the query are absent from the asset, and suggests alternatives. No hallucinated frame ranges, no silent failures.

How does this connect to the rest of the editing workflow?
Retrieval is the entry point. Once the agent has the frame range, the same loop handles per-aspect reframing, caption burn-in, brand-locked motion graphics, per-platform export and analytics. See our piece on editing video by prompt for the next link in the chain.