Scaling Agentic RAG With New Enterprise Vector Store Capabilities

Enterprises face explosive growth in unstructured data—PDFs, emails, images, audio, and video—that traditional data stacks fail to fully exploit. Generative AI, RAG, and agent‑driven workloads depend on vectorization to turn this content into actionable intelligence, yet organizations still contend with scale, data fragmentation, and retrieval limits. These challenges are amplified by vector stores that operate in isolation. Maximizing AI outcomes requires a unified data foundation—one that brings vector and non-vector data together rather than locking intelligence in separate silos.

Teradata's Enterprise Vector Store directly addresses these challenges by unifying structured and multimodal data in one governed platform—scaling to billions of vectors with industry‑leading price-performance—and delivering an open, developer‑friendly foundation for building and operationalizing agentic AI. This enterprise‑grade foundation powers high‑impact AI use cases—from fraud detection to call‑center optimization—while eliminating the complexity of point solutions and enabling AI to operate across billions of signals with reliability, scale, and economic efficiency that Teradata is known for.

Watch the live demo replay showcasing Teradata’s Enterprise Vector Store and the end-to‑end experience of building and operationalizing a vector-based workflow. 

You’ll see how to: 

• Create a vector store entirely in the UI using unstructured data and guided pipeline steps 
• Generate embeddings and configuring model options without code 
• Run and interpret hybrid search behavior 
• Apply this to the workflow of a risk/regulatory document set to highlight retrieval quality and lifecycle management 

Speakers include

Tamia van Geloven

Product Manager, AI Analytics at Teradata

Tamia leads product strategy and execution for the Teradata Enterprise Vector Store, driving innovation in vector search, generative AI, and advanced analytics within enterprise data environments. Her background spans political science, computer science, and hands-on data science, with experience building machine learning models, working with text and large language models, and using tools such as Python, SQL, Power BI, R, and TensorFlow.

Kevin Sturgeon

Director, Cloud Engineering at Teradata

Kevin leads a global program focused on developing and delivering live demonstration assets that showcase Teradata’s cloud, data architecture, AI/ML, advanced analytics, and business‑outcome capabilities. His work equips customers with practical, scalable approaches to unify data, accelerate analytics, and operationalize AI across complex environments.