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Condé Nast targets video discovery across 140,000 clips with multimodal search

aws.amazon.com by Mariah Miller 8 min read Editor score 7/10

Editors spent an average of 250 minutes per discovery task searching a library of more than 140,000 videos; the solution uses Bedrock and OpenSearch for intent-based semantic search. It shows how multimodal indexing can address discovery gaps in large media archives.

What

Condé Nast built multimodal video discovery using TwelveLabs Marengo embeddings, Amazon OpenSearch Service, and a purpose-built query tier.

How

Marengo jointly encodes visual, audio, and transcript signals; OpenSearch indexes the embeddings, and the query tier converts natural-language searches into vector searches and returns timestamps.

Why it matters

Separating embedding generation from search serving lets ingestion and query workloads scale, fail, and evolve independently.

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