Teradata has introduced the Teradata Enterprise Vector Store, an in-database solution designed to enhance vector data management with the power and scale of its hybrid cloud platform. This cutting-edge technology is set to revolutionize Trusted AI by seamlessly integrating with enterprise systems. Future enhancements will include NVIDIA NeMo Retriever microservices, part of the NVIDIA AI Enterprise software platform.

By processing billions of vectors at speeds as fast as tens of milliseconds, Teradata Enterprise Vector Store provides the efficiency businesses need to tackle complex challenges.

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Unlocking Business Potential with Advanced AI Integration

Teradata’s latest offering creates a single, trusted repository for all data. It strengthens retrieval-augmented generation (RAG) capabilities and lays the groundwork for dynamic agentic AI use cases, such as an augmented call center.

Vector stores are crucial for organizations aiming to leverage agentic AI, but most available solutions present trade-offs that make them costly or inefficient for large-scale business problems. Some offer speed but lack scalability, while others can manage high vector volumes but fall short in real-time responsiveness. Teradata’s Enterprise Vector Store solves this by combining high-speed processing with massive computing power, allowing organizations to extract value from both structured and unstructured data.

A New Era in AI-Powered Data Management

“Vector stores are fundamental to grounding truth in generative AI models and agentic AI. While vector stores are vital for data management, their impact is limited when they are slow or siloed,” said Louis Landry, Teradata’s CTO.” With our expertise in high concurrency, linear scaling, and data harmonization, Teradata’s Enterprise Vector Store provides a robust foundation for trusted agentic AI.”

Designed for efficiency and scalability, Enterprise Vector Store enables businesses to harness vector capabilities and RAG applications cost-effectively. Its seamless integration across cloud and on-premises environments makes it an ideal choice for organizations seeking to scale AI-driven insights while optimizing their existing infrastructure.

Transforming Data Management for AI

By managing unstructured data in multi-modal formats—including text, video, images, and PDFs—Teradata’s Enterprise Vector Store unifies structured and unstructured data for holistic analysis. It also:

  • Handles the entire vector data lifecycle, from embedding generation and indexing to metadata management and intelligent search.
  • Integrates seamlessly within existing Teradata systems, supporting flexible cloud, on-premises, and hybrid deployments.
  • Supports industry-leading frameworks like LangChain and RAG while ensuring robust data management and governance for Trusted AI.
  • Plans to introduce temporal vector embedding capabilities, enhancing explainability by tracking data changes over time.

Partnering with NVIDIA for Enhanced AI Capabilities

Teradata’s Enterprise Vector Store will integrate NVIDIA NeMo Retriever, providing advanced information retrieval with high accuracy and data privacy. This will empower businesses to generate insights in real-time, leveraging unstructured data such as PDFs. Developers will also have the ability to fine-tune NeMo Retriever microservices with custom models, enabling scalable document ingestion and RAG applications.

“Accurate AI inference relies on high-quality data,” said Pat Lee, Vice President of Strategic Enterprise Partnerships at NVIDIA. “Integrating Teradata Enterprise Vector Store with NVIDIA AI Enterprise and NVIDIA NeMo Retriever empowers organizations to extract valuable insights from PDFs and other unstructured documents, driving smarter AI agents.”

Real-World Application: Augmented Call Centers

One prime example of agentic AI in action is the augmented call center. This use case illustrates how Enterprise Vector Store leverages AI agents and RAG to enhance customer service by providing fast, context-aware responses. Additionally, AI-driven insights help companies identify upsell and cross-sell opportunities.

For instance, an insurance provider stores customer contracts in PDF format while maintaining structured customer data in a hybrid data platform. When a customer calls, a multi-agent system retrieves and analyzes data in milliseconds to deliver personalized recommendations.

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Example Interaction:

Customer: “I’m traveling to Malaysia. Does my insurance cover medical expenses? Should I add anything?”
AI Agent: Retrieves contract details via RAG with Enterprise Vector Store, extracting information from PDFs using NVIDIA NeMo Retriever.
AI Advisor: Analyzes the contract and recommends adding dental coverage for the trip based on a propensity-to-buy model.
Customer: “Ok, let’s add dental, please.”
AI Agent: Uses operational analytics to generate a contract for the customer’s signature, completing the transaction efficiently.

FAQs

1. What is the Teradata Enterprise Vector Store?

Teradata Enterprise Vector Store is an advanced in-database solution that enables businesses to process, manage, and integrate vector data efficiently. It helps organizations optimize AI-driven applications, particularly agentic AI and retrieval-augmented generation (RAG).

2. How does Teradata’s solution differ from other vector stores?

Unlike traditional vector stores, Teradata Enterprise Vector Store combines high-speed processing with massive scalability. It seamlessly integrates structured and unstructured data, ensuring businesses can leverage AI insights without sacrificing performance or cost efficiency.

3. What industries can benefit from Teradata Enterprise Vector Store?

Any industry that relies on AI for data analysis can benefit, including finance, healthcare, retail, and telecommunications. Use cases include fraud detection, customer service automation, and real-time decision-making.

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