Tuesday, March 10, 2026
Teradata has introduced new agentic and multi-modal data capabilities for its Teradata Enterprise Vector Store, enabling organizations to process text, images, and audio autonomously at enterprise scale. With this release, the company strengthens its enterprise AI infrastructure and helps businesses unlock the full potential of generative AI and intelligent agents across hybrid, cloud, and on-premises environments.
To achieve this, Teradata integrated the platform with Unstructured, allowing organizations to ingest and process diverse data formats more efficiently. As a result, the solution combines multi-modal data integration, advanced hybrid search, and agentic execution capabilities to deliver smarter and faster AI-driven insights.
The enhanced Teradata Enterprise Vector Store introduces a comprehensive pipeline that supports embedding generation, indexing, metadata management, and AI framework integration. One of its major updates includes Unstructured Integration, which automatically ingests and processes documents, PDFs, images, and audio files, with video support expected soon.
Additionally, the platform now offers Hybrid Search, which merges semantic and lexical search with metadata-driven techniques. Consequently, enterprises can retrieve more accurate and context-aware results. Meanwhile, multi-modal embeddings enable the platform to process text, images, and audio together, allowing AI systems to interpret complex data relationships more effectively.
Another key advancement is higher embedding dimensions, which now support up to 8K dimensions. This improvement enhances the precision and nuance of AI insights. Furthermore, the platform integrates directly with LangChain, enabling organizations to build enterprise-scale retrieval-augmented generation (RAG) pipelines and orchestrate agentic workflows that extend beyond traditional search.
As the volume of unstructured data continues to surge, enterprises face increasing challenges when scaling AI systems. According to research from Gartner, unstructured data is growing three times faster than structured data. At the same time, modern AI models increasingly process multiple data types simultaneously—including text, images, audio, and video.
However, many organizations struggle to scale AI initiatives due to fragmented data silos, limited infrastructure scalability, and the absence of unified access to both structured and unstructured information. Therefore, enterprises require a vector data platform capable of delivering the scale, performance, and governance necessary for modern AI applications.
Teradata designed its Enterprise Vector Store specifically to address these challenges. Research from Forrester notes that achieving large-scale performance with vector data—especially when handling tens of billions of vectors—remains technically demanding.
Unlike many vector solutions that reach operational limits after a few hundred million embeddings, Teradata’s platform can ingest millions of documents and thousands of files per hour. In addition, it supports large multi-modal data streams depending on configuration and workload characteristics.
Combined with the company’s Vantage architecture, the platform offers linear scalability across billions of vectors and high-dimensional embeddings. Moreover, it can support over 1,000 simultaneous queries without performance degradation while maintaining optimized infrastructure costs and enterprise-grade governance across hybrid environments.
Through its partnership with Unstructured, Teradata simplifies the process of transforming unstructured content into high-quality embeddings. Consequently, AI agents can automatically access unified enterprise data and execute complex workflows with minimal manual intervention.
For instance, in healthcare, AI systems can combine structured patient records with medical images, clinical notes, and voice dictations to support faster diagnosis and treatment planning. Similarly, in insurance, AI agents can analyze damage photos alongside policy documents and structured claims data to accelerate claims processing while maintaining transparency and compliance.
In defense environments, organizations can process field images, terrain patterns, and threat signatures together to provide real-time intelligence insights. Meanwhile, financial services firms can build AI-powered loyalty agents that merge structured data with policy documents to answer complex business questions.
"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. Teradata Enterprise Vector Store fundamentally reimagines how enterprises operationalize AI by unifying structured and multi-modal unstructured data with autonomous agent capabilities within a single governed platform. Organizations can now move from prototype to production-grade agentic systems in some cases within hours, not months—while maintaining the governance, security, and sovereignty that mission-critical AI demands."
"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.
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