How Condé Nast built multimodal video discovery with Amazon Bedrock

Condé Nast’s editorial teams cut video‑search time from an average of 250 minutes per task on a 140,000‑video library by building a multimodal discovery pipeline on Amazon Bedrock and Amazon OpenSearch. The system ingests video and text, uses Bedrock’s multimodal foundation models to generate embeddings, and stores them in OpenSearch for low‑latency retrieval. The solution demonstrates how Bedrock can be leveraged for production‑ready multimodal search, trading off higher compute cost for a dramatic productivity boost.

⚡ Key Takeaways

• Condé Nast reduced manual video search time from 250 minutes per task to near real‑time results.
• The architecture couples Bedrock multimodal embeddings with OpenSearch indexing for scalable retrieval.
• Engineers must provision Bedrock endpoints and OpenSearch clusters, balancing compute cost against retrieval latency.
• Integration is achieved via Bedrock’s `invokeModel` API for embeddings and OpenSearch’s

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