Teradata announced new agentic and multimodal data capabilities for its Teradata Enterprise Vector Store, designed to help organizations deploy and scale AI agents capable of autonomously processing diverse data types such as text, images, and audio. The enhanced capabilities aim to unify structured and unstructured data while enabling enterprises to build production-ready AI systems across hybrid, cloud, and on-premises environments.
The latest release integrates with Unstructured technology to deliver a more advanced enterprise AI infrastructure that combines multimodal data ingestion, hybrid search capabilities, and agentic execution. By bringing these capabilities together, the platform enables organizations to operationalize generative AI and autonomous agents more effectively while maintaining governance, performance, and scalability at enterprise scale.
The Teradata Enterprise Vector Store provides a full pipeline for building AI applications from embedding generation and indexing to metadata management and integration with popular AI frameworks. New features include automated ingestion and processing of documents, PDFs, images, and audio, with planned support for video. The solution also introduces hybrid search functionality that combines semantic and lexical search with metadata-driven techniques to improve contextual retrieval and accuracy. Additionally, the platform supports multimodal embeddings across text, image, and audio data, enabling richer semantic understanding across diverse datasets.
To further enhance AI development, the solution integrates directly with frameworks such as LangChain, allowing developers to build enterprise-scale retrieval-augmented generation (RAG) pipelines and orchestrate agentic workflows. This integration enables AI agents to retrieve contextual data, trigger governed actions, and automate complex operational processes beyond traditional search capabilities.
The introduction of these capabilities comes at a time when enterprises are grappling with a surge in unstructured data. Industry estimates suggest that unstructured data is expanding at a significantly faster rate than structured data, creating new challenges for organizations attempting to deploy AI systems that can effectively interpret multiple data modalities. Traditional vector databases often struggle to support the scale and performance required for such workloads, especially as AI models evolve to process multimodal data simultaneously.
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Teradata’s enterprise vector architecture has been engineered to address these limitations. The platform is designed to support billions of vectors and high-dimensional embeddings while handling thousands of files per hour and maintaining performance across large-scale deployments. The company reports that its infrastructure can support over 1,000 concurrent queries without performance degradation, enabling organizations to run AI-powered workloads at enterprise scale while maintaining optimized cost structures and governance controls.
These capabilities open the door to a wide range of real-world enterprise applications. For example, healthcare organizations can combine structured patient records with clinical notes, medical images, and audio dictations to assist physicians with faster diagnosis and treatment planning. Similarly, insurance providers can automate claims processing by allowing AI agents to analyze damage photos, policy documents, and structured claims data to generate explainable decisions more efficiently.
Hybrid search capabilities also help improve the reliability of AI systems by incorporating contextual data from multiple sources. By combining vector search with lexical and metadata-based retrieval techniques, enterprises can reduce AI hallucinations and generate responses grounded in trusted enterprise knowledge.
“We’re entering an era where AI agents will become the primary interface for enterprise intelligence autonomously orchestrating workflows, making decisions within defined governance frameworks, and uncovering insights across every data type,” said Sumeet Arora, Chief Product Officer at Teradata. “Stand-alone vector databases can’t deliver on this vision.”
“Enterprises shouldn’t have to choose between data security and AI readiness. By embedding Unstructured natively inside Teradata Enterprise Vector Store, Teradata customers get production-quality, AI-ready data at scale, with no external tools, no data leaving the platform, and no compromise on governance,” said Brian Raymond, Founder and CEO of Unstructured.
With open integrations for SQL, Python, and AI frameworks, developers can rapidly design, prototype, and deploy autonomous AI workflows using familiar tools. The platform also supports flexible deployment across cloud, on-premises, or hybrid environments, giving enterprises the ability to scale AI initiatives without architectural constraints.