Dell has announced updates to the Dell AI Data Platform, introducing a new ‘unstructured data engine’ that provides real-time access to large-scale unstructured data sets to gain relevant insights from.
To realise these capabilities, Dell has entered into a collaboration with Elastic, a leading open-source AI search technology provider. This will offer users advanced vector search and semantic retrieval, and hybrid keyword search capabilities.
This unstructured data engine operates in conjunction with other tools in Dell’s AI Data Platform, such as a federated SQL engine for querying dispersed unstructured data, a processing engine for large-scale data transformations, and storage optimised for AI access. It will also leverage built-in GPU acceleration to achieve exceptional performance.
The unstructured data engine will be available in the Dell AI Data Platform later this year.
Dell’s AI Data Platform is a modular, enterprise-grade architecture, which, according to the company, is built on three core pillars that focus on placing, protecting, and processing data across hybrid storage, GPU compute, and AI engines, thereby enabling secure, scalable, end-to-end AI workflows.
“India’s AI journey is shifting from experimentation to execution. The real challenge now lies in enabling fast, secure, and intelligent access to the right data — not just building advanced models,” said Venkat Sitaram, senior director and country head for the Infrastructure Solutions Group at Dell Technologies India.
“With the Dell AI Data Platform, powered by NVIDIA and Elastic, organisations can rapidly turn complex datasets into actionable insights. This helps drive real-world impact — from better healthcare outcomes to smarter manufacturing and enhanced customer experiences,” he added.
Dell has also announced the launch of the PowerEdge R7725 and R770 servers, which are now equipped with NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs.
These 2U servers will be the first to incorporate the NVIDIA AI Data Platform reference design, enabling high-density AI workloads and achieving up to six times the token throughput for LLM inference compared to earlier models.
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