Amazon Nova Multimodal Embeddings enables cross-modal retrieval for aerospace documents on Bedrock and S3 Vectors

AI Impact Summary

Amazon Nova Multimodal Embeddings now enables embedding of text, images, and document pages into a shared vector space, enabling cosine similarity retrieval across modalities. For aerospace manufacturing repositories with CAD diagrams, inspection photos, and S-N curves, this reduces OCR-only localization gaps by surfacing diagrams and charts via text queries and vice versa. The post compares a pure multimodal pipeline against an OCR-then-embed baseline, highlighting configurable embedding dimensions (256–3072) and the practical 1024 choice, which informs storage and compute planning for large document collections. Operators should factor in the cost and latency implications of larger vectors as they scale to enterprise workloads.