Unstructured has announced a strategic partnership with Teradata to introduce native data ingestion and processing capabilities within Teradata Enterprise Vector Store. Through this collaboration, enterprises will be able to automatically ingest, process, and transform unstructured data directly inside the platform. The new capability is expected to become available to eligible Teradata customers starting in April 2026.

With this integration, organizations can convert various forms of unstructured content such as documents, PDFs, spreadsheets, emails, images, videos, and audio into high-quality, AI-ready data without relying on external pipelines or additional infrastructure. As a result, enterprises can simplify data workflows while accelerating the deployment of generative AI and advanced analytics initiatives.

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Unlike standalone solutions, Unstructured’s document preprocessing and enrichment features are embedded natively as a service within Teradata Enterprise Vector Store. Consequently, Teradata customers can ingest and preprocess unstructured content in the same platform they already use for structured analytics. Furthermore, the processed outputs are delivered directly into Teradata Enterprise Vector Store as vectors, structured data, or both, ensuring seamless accessibility for downstream AI applications.

“This partnership is a validation of what we’ve been building toward: making unstructured data processing a core part of the enterprise data stack,” said Brian Raymond, Founder and CEO of Unstructured. “Teradata’s customers run some of the most demanding, highly regulated workloads in the world. Embedding our platform inside Teradata Enterprise Vector Store means those customers can now unlock their unstructured data for Gen AI with the same governance, security, and operational rigor they expect from everything else in their environment.”

Industry estimates suggest that nearly 80% of enterprise data exists in formats that artificial intelligence systems cannot readily use. These formats include PDFs, images, videos, emails, and scanned documents. By addressing this challenge, Unstructured expands the possibilities for enterprises working with large volumes of unstructured information. Specifically, the platform can preprocess more than 70 different file types into chunked JSON while generating production-grade embeddings all within Teradata Enterprise Vector Store.

In addition, the integration supports Teradata’s hybrid deployment model. This means organizations can run the solution across AWS, Microsoft Azure, Google Cloud Platform, on-premises infrastructure, and even air-gapped environments. For industries such as financial services, healthcare, defense, and government where strict data sovereignty requirements exist this flexibility allows companies to process and ingest data exactly where it resides.

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“Our customers manage some of the world’s most complex, regulated data environments, and they need AI-ready data they can trust,” said Sumeet Arora, Chief Product Officer at Teradata. “Unstructured brings the depth of production-grade preprocessing our customers need delivered natively inside Teradata Enterprise Vector Store across multi-cloud and on-premises environments. That means the reliability, governance, and compliance they require, with the flexibility to deploy wherever their data lives without adding complexity or additional tools to their existing environment.”

Moreover, the integration addresses every stage of the preprocessing lifecycle. Unstructured manages parsing, enrichment, chunking, and embedding generation for different content types, including text, images, and audio. Once processed, the outputs flow directly into Teradata Enterprise Vector Store, making them immediately ready for hybrid search, retrieval-augmented generation (RAG), agentic AI workflows, and traditional analytics.

Importantly, the generated embeddings align with existing role-based access controls and governance policies already defined within Teradata. At the same time, the platform maintains SLA-compatible reliability and deterministic outputs at enterprise scale.

Ultimately, the partnership creates a fully governed pipeline that transforms raw enterprise content into AI-ready data within the existing Teradata ecosystem. Instead of assembling fragmented tools such as open-source libraries, separate vector databases, and external ingestion services, enterprises can now rely on a unified, end-to-end solution built directly into their Teradata environment.

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