Challenge
A leading broadcaster in the Middle East was sitting on decades of untagged or poorly tagged footage. Producers relied on institutional memory and manual scrubbing to find assets, while metadata was scattered across spreadsheets, legacy MAM systems, and handwritten notes. Valuable archival content went unused in new programming and digital channels.
Approach
We deployed MediaMaster Archive Cloud, an AI-native platform that performs semantic indexing on video and audio, understands scenes and dialogue, and links them back to existing catalogs. Automated quality assessment and AI upscaling restored legacy footage for HD and OTT, while a rights-aware workflow engine ensured only cleared content moved into downstream edit and distribution systems. Editors and producers now search in natural language — “sunset shots over the Corniche,” “Asia football finals in the 1990s” — and get frame-accurate results in seconds.
Outcome
Roughly 100K hours of archive are now searchable by visual content and dialogue, manual QC time has been reduced by about 80%, and the broadcaster has unlocked new revenue from repackaged archival series, digital shorts, and branded content. The archive transformed from a cost center into an AI-powered content factory for the wider MENA region.
"Search finally understands what's in the footage, not just what we tagged." — Archive Operations Director