Agentic and Gen AI Use Cases With Teradata Enterprise Vector Store
Agentic and generative AI use cases rely on vast amounts of unstructured data, including text, audio, PDFs, and videos, often involving millions of objects. This could result in billions of inferences. These use cases also need to leverage structured data for operationalization purposes.
Teradata Enterprise Vector Store efficiently handles both structured and unstructured data, enabling companies to scale and build toward an agentic AI future for dynamic customer experiences and more. By integrating seamlessly with enterprise data, it reduces data movement and maximizes value.
Join our live demo to see Teradata’s Enterprise Vector Store in action as we demonstrate a generative AI use case for state-of-the-art call center augmentation, featuring our partnership with NVIDIA.
Learn how to:
Teradata Enterprise Vector Store efficiently handles both structured and unstructured data, enabling companies to scale and build toward an agentic AI future for dynamic customer experiences and more. By integrating seamlessly with enterprise data, it reduces data movement and maximizes value.
Join our live demo to see Teradata’s Enterprise Vector Store in action as we demonstrate a generative AI use case for state-of-the-art call center augmentation, featuring our partnership with NVIDIA.
Learn how to:
- Ingest PDF documents
- Generate embeddings
- Perform intelligent search and retrieval
- Integrate with operational data
Speakers include:
Tim Miller
Principal Software Architect, Product Management, at Teradata
Tim boasts a 30-year tenure at NCR Corporation and Teradata, playing pivotal roles in enterprise software development, including the creation of the first commercial in-database data mining system. His expertise in partner integration and data science consulting has led to his current position in Product Management managing the ClearScape Analytics suite of capabilities and as an instructor on Business Analytics at UCSD Halıcıoğlu Data Science Institute.
Doug Frazier
Software Architect, Product Engineering, at Teradata
Doug has expertise in vector databases, AI-driven analytics, and cloud elasticity. He led the development of Teradata’s Enterprise Vector Store, enabling high-performance storage and retrieval of vector embeddings for AI/ML workloads. His work on vectorized query execution and distributed processing has significantly improved scalability and efficiency for enterprise AI applications. With over 30 years in database development, he has contributed to Teradata’s QueryGrid, federated query processing, and in-database analytics. His innovations have enhanced performance, scalability, and cost efficiency for large-scale data platforms.