Wedoany.com Report, March 9, 2026, San Francisco, USA – Data platform Unstructured announced a partnership with Teradata to natively integrate data ingestion and processing capabilities into the Teradata Enterprise Vector Store. Starting in April 2026, eligible Teradata customers are expected to gain access to this integration, enabling them to automatically process unstructured content such as documents, PDFs, spreadsheets, emails, images, videos, and audio, transforming it directly into high-quality, AI-ready data. In typical deployments, this eliminates the need for external pipelines or additional infrastructure management, streamlining enterprise AI data processing workflows.

Unstructured's document preprocessing and enhancement capabilities are embedded as a service natively within the Teradata Enterprise Vector Store, rather than operating independently. Customers can process unstructured content on the same platform they use for structured analytics, with the output flowing directly into the Teradata Enterprise Vector Store as vectors, structured data, or both, enhancing data processing efficiency.

Brian Raymond, Founder and CEO of Unstructured, stated: "This partnership validates our long-standing goal: to make unstructured data processing a core part of the enterprise data stack. Teradata's customers run demanding, heavily regulated workloads. Embedding our platform within the Teradata Enterprise Vector Store means these customers can now unlock their unstructured data for generative AI with the same governance, security, and operational rigor applied to the rest of their environment."

Approximately 80% of enterprise data exists in formats not natively usable by AI systems, such as PDFs, images, videos, audio, emails, and scanned documents. The Unstructured platform preprocesses over 70 file types into chunked JSON and generates production-quality embeddings within the Teradata Enterprise Vector Store. This integration supports Teradata's hybrid deployment model, operable on AWS, Azure, GCP, on-premises, and air-gapped environments, meeting data sovereignty requirements in sectors like financial services and healthcare by ensuring ingestion and preprocessing occur where the data resides.

Sumeet Arora, Chief Product Officer at Teradata, said: "Our customers manage complex, regulated data environments and need trusted, AI-ready data. Unstructured brings the production-grade preprocessing depth customers require—delivered natively within the Teradata Enterprise Vector Store across multi-cloud and on-premises environments. This means they get the reliability, governance, and compliance they need, along with the flexibility to deploy where their data resides, without adding complexity or extra tools to their existing environment."

This integration covers all stages of preprocessing, including parsing, enhancement, chunking, and embedding generation for text, images, and audio. The processed output goes directly into the Teradata Enterprise Vector Store, ready for hybrid search, RAG, agentic AI workflows, and traditional analytics. The embeddings are designed to align with role-based access controls and governance policies already defined in Teradata, with the platform offering SLA-compatible reliability and deterministic output at enterprise scale.

The result is a complete, governed pipeline from raw enterprise content to AI-ready data, delivered as a native platform capability. Enterprises no longer need to piece together open-source libraries, standalone vector databases, and external ingestion services. Instead, they gain an end-to-end solution within their existing Teradata environment to advance their AI data processing. For more information, visit unstructured.io.