A large US content company was burning thousands of hours tagging video, transcribing, captioning — and then digging through a messy folder tree to find a single clip. We turned the archive from a black hole into something you can just ask.
Editors and producers were choking on operational work — thousands of hours a year on archive management, hand-tagging clips, transcription and writing captions.
The archive itself was blind: when editorial or marketing needed a specific clip from years of broadcast — "every time politician X spoke about topic Y" — they had to dig for hours through an old, disorganised folder tree. And localisation was a money pit: adapting content for international markets — translation, dubbing, format changes — was done by hand through outside vendors that cost a fortune and took weeks.
Process everything automatically, understand what's in it, and let anyone find the exact moment in plain language.
We ingested all the raw video, audio and text into one unified AI pipeline able to run over enormous volumes of material in the background — the archive processes itself instead of waiting on people.
Computer-vision and language models scan every clip, automatically recognising faces, topics, on-screen text and sentiment, and producing a first-pass transcription and translation at high accuracy.
A simple free-text interface: staff type what they're looking for in ordinary language, and the system returns the exact time-stamp inside an hours-long video in seconds — not a folder, the moment.
With transcription and translation generated in-house by the pipeline, the manual, weeks-long, expensive route through outside localisation vendors shrank to a review-and-polish step.
The pipeline was designed to chew through years of back-catalogue and every new asset as it lands, so the archive stays searchable and current rather than freezing at a snapshot.
The AI does the heavy first pass — tags, transcript, translation — and the editors and translators refine it, spending their time on judgement and craft instead of grunt work.
Finding, translating and shipping content stopped being the slow part.
Finding and pulling archive material fell from two or three days of research work to a few seconds of search, and outside spend on transcription, translation and captioning dropped by around 60%.
Time-to-market for content and promo videos accelerated sharply — the organisation could push out three times as much marketing content with the same headcount, because the slow, manual middle had been automated away.
It holds because the pipeline runs on every new asset automatically and the search speaks plain language, so it doesn't decay the moment the project ends — and people stay in the loop for the finishing touches. Heavy lifting by the machine, judgement by the humans, at a scale no manual team could match — the same discipline behind our natural-language data work.
Reef TRH