Powering Multimodal Intelligence for Video Search

This article describes Netflix's multimodal video search system designed to help filmmakers quickly locate specific moments across massive media archives.

• •An ensemble of specialized AI models analyzes video across multiple dimensions: character recognition, scene detection, and dialogue parsing
• •A three-stage pipeline handles data flow: Apache Cassandra for transactional persistence, Apache Kafka for triggering async offline processing, and Elasticsearch for real-time indexing
• •Temporal bucketing maps continuous model detections into fixed one-second intervals, enabling precise multi-modal annotation intersection
• •A hybrid scoring engine weighs symbolic text matches against semantic vector embeddings to rank and deduplicate results
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