Power video semantic search with Amazon Nova Multimodal Embeddings

Amazon has introduced Nova Multimodal Embeddings, a foundational model designed to power advanced semantic search across complex media like video. Unlike traditional approaches that ground all video signals into text—losing critical temporal and contextual information—this model natively processes text, documents, images, video, and audio, mapping them into a single, shared 1024-dimensional semantic vector space. This unified representation allows a search query like "a tense car chase with sirens" to simultaneously match both the visual event and the audio cue, a significant leap over transcription-dependent methods.

The model is the core of a new reference architecture on Amazon Bedrock for building video semantic search solutions. The system uses a hybrid search approach, fusing semantic understanding with lexical keyword matching. An ingestion pipeline automatically segments videos, uses Nova to generate embeddings for visual and audio streams, transcribes dialogue, and detects celebrities via Amazon Rekognition. All this metadata and the vectors are indexed in Amazon OpenSearch Service, enabling a search pipeline to intelligently route user queries and return ranked, precise video moments.

This technology addresses a growing demand from industries like sports broadcasting, media studios, and news organizations that need to instantly surface specific clips from vast archives. By understanding intent across multiple unstructured signals without converting everything to text first, it enables faster content discovery, personalized trailer creation, and rapid publishing of breaking news footage, ultimately helping companies monetize their video libraries more effectively.