Teradata Launches Integrated Enterprise Vector Store to Help Customers Be Ready to Implement Trusted Agentic AI
Teradata (NYSE: TDC) has unveiled its new Enterprise Vector Store, an in-database solution designed to enhance vector data management for Trusted AI applications. The platform will integrate NVIDIA NeMo Retriever microservices to deliver sub-second response times and process billions of vectors efficiently.
The solution enables organizations to combine structured and unstructured data, supporting various formats including text, video, images, and PDFs. Key features include embedding generation, indexing, metadata management, and intelligent search capabilities, with response times as quick as tens of milliseconds.
The Enterprise Vector Store supports frameworks like LangChain and RAG (retrieval-augmented generation), while offering temporal vector embedding capabilities to track data changes over time. The platform demonstrates its practical application through an augmented call center use case, showcasing real-time customer service enhancement using AI agents.
The product is currently available in private preview, with general availability expected in July.
Teradata (NYSE: TDC) ha presentato il suo nuovo Enterprise Vector Store, una soluzione in-database progettata per migliorare la gestione dei dati vettoriali per applicazioni di AI affidabile. La piattaforma integrerà i microservizi NVIDIA NeMo Retriever per fornire tempi di risposta inferiori a un secondo e gestire miliardi di vettori in modo efficiente.
La soluzione consente alle organizzazioni di combinare dati strutturati e non strutturati, supportando vari formati tra cui testo, video, immagini e PDF. Le caratteristiche principali includono la generazione di embedding, l'indicizzazione, la gestione dei metadati e capacità di ricerca intelligente, con tempi di risposta rapidi fino a decine di millisecondi.
L'Enterprise Vector Store supporta framework come LangChain e RAG (generazione aumentata da recupero), offrendo capacità di embedding vettoriale temporale per monitorare le variazioni dei dati nel tempo. La piattaforma dimostra la sua applicazione pratica attraverso un caso d'uso di call center aumentato, mostrando come migliorare il servizio clienti in tempo reale utilizzando agenti AI.
Il prodotto è attualmente disponibile in anteprima privata, con disponibilità generale prevista per luglio.
Teradata (NYSE: TDC) ha presentado su nuevo Enterprise Vector Store, una solución en base de datos diseñada para mejorar la gestión de datos vectoriales para aplicaciones de IA confiable. La plataforma integrará los microservicios NVIDIA NeMo Retriever para ofrecer tiempos de respuesta de menos de un segundo y procesar miles de millones de vectores de manera eficiente.
La solución permite a las organizaciones combinar datos estructurados y no estructurados, admitiendo varios formatos que incluyen texto, video, imágenes y PDF. Las características clave incluyen generación de embeddings, indexación, gestión de metadatos y capacidades de búsqueda inteligente, con tiempos de respuesta tan rápidos como decenas de milisegundos.
El Enterprise Vector Store es compatible con marcos como LangChain y RAG (generación aumentada por recuperación), al tiempo que ofrece capacidades de embedding vectorial temporal para rastrear cambios en los datos a lo largo del tiempo. La plataforma demuestra su aplicación práctica a través de un caso de uso de centro de llamadas aumentado, mostrando la mejora del servicio al cliente en tiempo real utilizando agentes de IA.
El producto está actualmente disponible en vista previa privada, con disponibilidad general esperada para julio.
테라데이타 (NYSE: TDC)는 새로운 엔터프라이즈 벡터 저장소를 공개했습니다. 이 데이터베이스 내 솔루션은 신뢰할 수 있는 AI 애플리케이션을 위한 벡터 데이터 관리를 향상시키도록 설계되었습니다. 이 플랫폼은 NVIDIA NeMo Retriever 마이크로서비스를 통합하여 1초 이하의 응답 시간을 제공하고 수십억 개의 벡터를 효율적으로 처리합니다.
이 솔루션은 조직이 구조화된 데이터와 비구조화된 데이터를 결합할 수 있도록 하며, 텍스트, 비디오, 이미지 및 PDF를 포함한 다양한 형식을 지원합니다. 주요 기능으로는 임베딩 생성, 인덱싱, 메타데이터 관리 및 지능형 검색 기능이 있으며, 응답 시간은 수십 밀리초로 빠릅니다.
엔터프라이즈 벡터 저장소는 LangChain 및 RAG(검색 증강 생성)과 같은 프레임워크를 지원하며, 시간에 따른 데이터 변경 사항을 추적할 수 있는 시간적 벡터 임베딩 기능을 제공합니다. 이 플랫폼은 AI 에이전트를 사용하여 실시간 고객 서비스 향상을 보여주는 증강 콜센터 사례를 통해 실제 응용 프로그램을 입증합니다.
이 제품은 현재 비공식 미리보기로 제공되며, 일반 출시 예정은 7월입니다.
Teradata (NYSE: TDC) a dévoilé son nouveau Enterprise Vector Store, une solution en base de données conçue pour améliorer la gestion des données vectorielles pour les applications d'IA de confiance. La plateforme intégrera les microservices NVIDIA NeMo Retriever pour offrir des temps de réponse inférieurs à une seconde et traiter des milliards de vecteurs de manière efficace.
Cette solution permet aux organisations de combiner des données structurées et non structurées, en prenant en charge divers formats, y compris le texte, la vidéo, les images et les PDF. Les fonctionnalités clés incluent la génération d'embeddings, l'indexation, la gestion des métadonnées et des capacités de recherche intelligente, avec des temps de réponse aussi rapides que quelques dizaines de millisecondes.
Le Enterprise Vector Store prend en charge des frameworks tels que LangChain et RAG (génération augmentée par récupération), tout en offrant des capacités d'embedding vectoriel temporel pour suivre les changements de données au fil du temps. La plateforme démontre son application pratique à travers un cas d'utilisation de centre d'appels augmenté, montrant l'amélioration du service client en temps réel grâce à des agents d'IA.
Le produit est actuellement disponible en aperçu privé, avec une disponibilité générale prévue pour juillet.
Teradata (NYSE: TDC) hat seinen neuen Enterprise Vector Store vorgestellt, eine In-Datenbank-Lösung, die entwickelt wurde, um das Management von Vektordaten für vertrauenswürdige KI-Anwendungen zu verbessern. Die Plattform wird die NVIDIA NeMo Retriever-Mikrodienste integrieren, um Antwortzeiten von unter einer Sekunde zu liefern und Milliarden von Vektoren effizient zu verarbeiten.
Die Lösung ermöglicht es Organisationen, strukturierte und unstrukturierte Daten zu kombinieren und unterstützt verschiedene Formate, einschließlich Text, Video, Bilder und PDFs. Zu den Hauptmerkmalen gehören die Generierung von Einbettungen, Indizierung, Metadatenmanagement und intelligente Suchfunktionen, mit Antwortzeiten von nur wenigen Millisekunden.
Der Enterprise Vector Store unterstützt Frameworks wie LangChain und RAG (retrieval-augmented generation) und bietet zeitliche Vektoreinbettungsfunktionen, um Datenänderungen im Laufe der Zeit zu verfolgen. Die Plattform demonstriert ihre praktische Anwendung anhand eines erweiterten Callcenter-Anwendungsfalls, der die Verbesserung des Kundenservice in Echtzeit mithilfe von KI-Agenten zeigt.
Das Produkt ist derzeit in einer privaten Vorschau verfügbar, die allgemeine Verfügbarkeit wird für Juli erwartet.
- Integration with NVIDIA NeMo Retriever enhances AI capabilities
- Sub-second response times (as low as tens of milliseconds)
- Scalable processing of billions of vectors
- Unified platform for both structured and unstructured data analysis
- Flexible deployment across cloud and on-premises environments
- Product still in private preview phase
- General availability delayed until July
Insights
Teradata's new Enterprise Vector Store represents a significant product launch that addresses a critical gap in enterprise AI infrastructure. This in-database solution enables organizations to process billions of vectors with response times as quick as tens of milliseconds, positioning Teradata to capitalize on the growing demand for retrieval-augmented generation (RAG) and agentic AI applications.
The integration with NVIDIA NeMo Retriever microservices adds substantial technical capabilities, allowing enterprises to process unstructured data from multiple sources—text, videos, images, PDFs—alongside structured data. This unified approach targets a persistent pain point for large organizations that need to extract value from diverse data types.
The augmented call center use case demonstrates practical business applications, showing how insurance companies could leverage the technology to analyze customer contracts in real-time during calls, potentially identifying upsell opportunities while improving customer experience. This directly connects the technology to revenue-generating activities.
With private preview available now and general availability expected in July, this product launch strengthens Teradata's competitive positioning in the enterprise AI space while potentially opening new revenue streams from existing customers looking to implement advanced AI applications without sacrificing performance or scale.
The technical architecture of Teradata's Enterprise Vector Store addresses several fundamental challenges that have vector database adoption in enterprise environments. By implementing vector processing directly within Teradata's existing database infrastructure, the company has created a solution that leverages its core competency in handling massive data volumes with high concurrency.
What distinguishes this offering is its hybrid deployment flexibility—supporting cloud, on-premises, or hybrid implementations—which aligns with enterprise reality where mission-critical data resides across multiple environments. The temporal vector embedding capabilities mentioned are particularly noteworthy, as they could enhance auditability and explainability—crucial requirements for regulated industries implementing AI.
The NVIDIA partnership provides acceleration capabilities through NeMo Retriever, specifically optimized for document processing and information extraction—a common enterprise use case. This addresses the computational bottleneck that typically occurs when processing unstructured data at scale.
From an implementation perspective, the integration with popular frameworks like LangChain suggests Teradata is prioritizing developer accessibility, which could accelerate adoption. The solution's focus on managing the full vector data lifecycle—from embedding generation through intelligent search—positions it as a comprehensive offering rather than just another vector index implementation.
Designed for cost-effective, sub-second response times at all data volumes, the new offering can solve multi-dimensional, complex problems by combining structured and unstructured data
Enterprise Vector Store will use NVIDIA NeMo Retriever microservices for accelerated compute, optimized RAG
The offering creates a single, trusted repository for all data and builds on the strong support Teradata offers today for retrieval-augmented generation (RAG), while working towards dynamic agentic AI use cases, such as “augmented call center” (see example below).
Vector stores are foundational for any organization looking to leverage agentic AI, but most vector stores require trade-offs that make it prohibitively hard or expensive to use in solving the most challenging (and potentially the most lucrative) business problems. They can be fast, but only for small data sets. Or they can manage vector volumes, but not at the speed that agentic AI use cases require. The real magic happens when organizations can apply both lightning-fast speed and massive compute to unstructured datasets that hold real value when combined with mission-critical structured data.
“Vector stores are at the root of how we bind truth to generative AI models and agentic AI. They are essential to any data management practice, but their impact is limited when they are slow or siloed,” said Louis Landry, Teradata’s CTO. “Teradata’s long-standing expertise in high concurrency and linear scale, as well as the critical ability to harmonize data and support RAG, means Teradata Enterprise Vector Store delivers on the dynamic, trusted foundation large organizations need for agentic AI.”
Teradata’s Enterprise Vector Store is designed to be a performant way to enable use cases that require vector capabilities and RAG applications. With cost-efficient scaling and near seamless integration built-in, Enterprise Vector Store is expected to help enterprises maximize value and insight from unstructured data while reducing spend. Given Teradata’s advantage in hybrid, Enterprise Vector Store is a natural choice for organizations that want to scale flexibly across cloud and on-premises environments, building towards an agentic AI future while making the most of current infrastructure.
By managing unstructured data in multi-modal formats — text, video, images, PDFs, and more — Teradata’s Enterprise Vector Store unifies structured and unstructured data for holistic analysis. It also:
- Engages with the full lifecycle of vector data management, from embedding generation and indexing to metadata management and intelligent search
- Processes this work within the existing Teradata system, which thrives in flexible deployment options including cloud, on-premises, or hybrid
- Supports industry-leading frameworks like LangChain and RAG, along with the comprehensive data management and governance practices needed for Trusted AI
- Adds planned temporal vector embedding capabilities, which is designed to boost trust and explainability by tracking changes to data over time, improving accuracy and decision making.
A scalable, in-database vector solution built with NVIDIA AI
Teradata Enterprise Vector Store is expected to integrate NVIDIA NeMo Retriever to provide a leading information retrieval solution with high accuracy and data privacy, enabling enterprises to generate business insights in real-time. Developers can fine-tune NeMo Retriever microservices in combination with community or custom models to build scalable document ingestion and RAG applications which can be connected to proprietary data wherever it resides. NVIDIA NeMo Retriever extraction is designed to enable customers to use information and insights from unstructured data sources such as PDFs, enabling developers to build RAG-based applications which leverage real-time knowledge appended with information from across the corporate IT estate.
“Data is essential to accurate inference for AI applications,” said Pat Lee, Vice President of Strategic Enterprise Partnerships at NVIDIA. “Teradata Enterprise Vector Store, integrated with NVIDIA AI Enterprise and NVIDIA NeMo Retriever, can unlock the institutional knowledge stored in PDFs and other unstructured documents to power intelligent AI agents.”
Use Case: Augmented Call Center
The augmented call center use case demonstrates how the Teradata Enterprise Vector Store uses agentic AI and RAG to transform customer service to be faster, more efficient and tailored to each customer’s needs. AI agents also enable upsell and cross-sell opportunities during customer interactions.
For example, an insurance company stores contracts for its millions of customers in PDF format in an object store. It also uses a hybrid data platform for mission-critical customer 360 data. When a customer calls in, a multi-agent system uses lightning-fast access (as low as tens of milliseconds) to harmonized data to provide precise, context-aware answers to each individual customer.
-
“Hello, how can I help you today?”
- “Customer Interaction” Agent communicates in real time with the customer using a natural language interface which is powered by popular LLMs running as NVIDIA NIM on NVIDIA accelerated compute.
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“I’m traveling to
Malaysia . Does my insurance cover medical expenses? Should I add anything?”- “Contract Analyzer” Agent quickly retrieves coverage details from the PDF copy of the contract using RAG with Enterprise Vector Store, which has extracted the information from PDFs and stored as embeddings using NVIDIA NeMo Retriever in Teradata Enterprise Vector Store.
- “Insurance Advisor” Agent uses reasoning and decision making to recommend adding dental coverage for the duration of the trip, using a propensity-to-buy model and Teradata’s trusted predictive and explainable AI capabilities.
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“Ok, let’s add dental please.”
- “Actions” Agent uses operational analytics and customer 360 (structured) data in Teradata’s hybrid environment to create a contract for customer signature.
Availability
Teradata Enterprise Vector Store is now available in private preview, with general availability expected in July.
About Teradata
At Teradata, we believe that people thrive when empowered with trusted information. We offer the most complete cloud analytics and data platform for AI. By delivering harmonized data and trusted AI, we enable more confident decision-making, unlock faster innovation, and drive the impactful business results organizations need most.
See how at Teradata.com.
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MEDIA CONTACT
Jennifer Donahue
jennifer.donahue@teradata.com
Source: Teradata
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