Global: Dell Enhances AI Data Platform with RAG Integration and Data Management Innovations

Shoppers and IT leaders are turning to Dell’s upgraded AI Data Platform to tame scattered, messy data and speed up AI projects, especially for training, retrieval-augmented generation and on-premises inference. The modular updates , from faster PowerScale and ObjectScale storage to new data-search and analytics engines , promise more reliable, lower-latency AI across the enterprise.

• Modular foundation: Storage engines, data engines, resiliency and management services work together to turn siloed data into AI-ready assets.
• Storage gains: PowerScale F710 and ObjectScale claim big efficiency wins , less rack space, lower power and major throughput boosts that feel noticeably snappier.
• Smarter search: A Data Search Engine with Elastic enables conversational, semantic queries across billions of files for quicker, contextual answers.
• Analytics with LLMs: Starburst-powered Data Analytics Engine adds an “agentic” LLM layer to automate insights and embed AI in SQL workflows.
• Enterprise control: GPU-accelerated vector search and NVIDIA integrations aim to deliver high performance while keeping data on-premises and governed.

Why Dell’s upgrades matter now for enterprise AI projects

AI projects stall on messy data more often than model choice, and Dell’s pitch is simple and tactile , make the data usable first. The platform upgrades look and feel like an attempt to remove friction: faster storage, smarter search and tools to link spreadsheets, databases and lakehouses. For IT teams that have wrestled with slow queries and fractured pipelines, the improvements should translate into noticeably faster prototyping and production rollouts.

Dell’s move also reflects the wider market push for integrated AI infrastructure. Rather than stitching together point products, businesses want systems that just work together; Dell’s partnerships with NVIDIA, Elastic and Starburst are meant to create that single, smoother experience. In other words, it’s not just new bits of kit, it’s an attempt to make the whole stack feel more joined-up.

What’s new in storage and why you’ll probably notice the difference

Storage’s where the rubber meets the road for AI, and Dell has enhanced both PowerScale (NAS) and ObjectScale (S3-native object storage). PowerScale F710 has gained NVIDIA Cloud Partner certification and promises efficiency wins that reduce data-centre footprint and power draw , a practical improvement if your rack space and energy bills matter. Expect less noise from swapping hardware and a denser, tidier setup.

ObjectScale’s software and appliance options claim dramatic performance boosts versus previous all-flash generations, with upcoming S3 over RDMA features offering sharply higher throughput and lower latency. That’s important for workloads that rely on quick small-object reads; your RAG app or vector store will feel more responsive and less I/O-bound.

The new data engines that turn files into answers

Dell’s Data Search Engine, built with Elastic, is designed for conversational and semantic search across huge file sets, and it ties into MetadataIQ for discovery. Practically, that means someone in sales or legal could ask a plain-English question and get focused results without hunting through shared drives , a small UX win that can save hours.

The Data Analytics Engine, from Starburst, is more about unifying queries across diverse data sources. Its agentic layer leverages LLMs to automate documentation, produce insights and embed intelligence into SQL workflows. That combination helps teams ask better questions and get richer analysis, faster, while integrated vector-store access supports hybrid search scenarios.

How NVIDIA and GPU acceleration change the game for vector search

Dell integrates NVIDIA tech , including GB200/GB300 NVL72 accelerators and cuVS vector-search optimisations , to deliver turnkey, GPU-accelerated hybrid search on-prem. For enterprises wary of cloud-only stacks, that’s a big deal: you get the speed of GPU-backed vector search while retaining control over sensitive data. The practical effect is faster nearest-neighbour lookups, slicker semantic retrieval and more responsive RAG applications.

This partnership also targets ease of deployment, so IT teams don’t have to hand-wire GPU pipelines from scratch. The result should be shorter setup times and better performance out of the box.

Security, governance and where this fits in production pipelines

Dell bundles built-in cyber resiliency and data management services into the platform, which is reassuring for heavily regulated industries. The analytics engine adds monitoring and governance tools, and the collaboration partners emphasise enterprise-grade controls. In practice, that means teams gain auditability and policy enforcement alongside the performance wins , crucial when you shift models from pilot to production.

You’ll still need the usual organisational buy-in: data hygiene, well-scoped use cases and clear access controls. But the platform is positioned to reduce the plumbing work required to get from messy data to reliable AI-driven features.

Who should consider this and what to test first

If you run large on-prem datasets, handle sensitive customer or industrial telemetry, or need faster RAG and inference without moving everything to the cloud, Dell’s updates are worth a look. Start by testing small but high-value workflows: semantic search over support tickets, vectorised knowledge bases for customer service, or GPU-accelerated analytics for telemetry data.

Measure latency, throughput and operational overhead before and after, and see whether your models actually produce better business outcomes with the same data , that’s the true test. And remember, it’s often the improved developer experience and reduced ops friction that delivers the biggest gains.

Ready to make enterprise data more useful for AI? Check current Dell platform details and partner integrations to see which configuration suits your environment best.