Teradata partners with Unstructured to simplify vector operations

Embedding models determine how AI systems store, search, and retrieve enterprise data. The model used to embed data must match the model used to look it up. A wrong choice weakens every AI application built on top of it, and switching models after deployment forces organizations to re-embed their entire data corpus. On March 9, 2026, Teradata announced new agentic and multi-modal capabilities for its Enterprise Vector Store, built on a native integration with Unstructured. The combined offering targets the embedding problem by automating the pipeline, supporting multiple model providers, and offering model recommendations matched to specific use cases and data types. This gives enterprises a way to get embedding model selection right the first time, avoid costly re-embedding cycles, and extend vector capabilities across text, images, and audio within a single governed platform.