Dell AI Data Platform Review: What It Offers for Enterprise RAG, AI Agents, and Production GenAI

Key takeaways

• The Dell AI Data Platform combines Storage, Data, and Orchestration Engines into a modular architecture that functions as an enterprise-wide context layer for AI workloads.
• Dell’s federated approach can reduce unnecessary data movement across hybrid environments.
• NVIDIA certifies select Dell PowerScale F710 configurations across multiple certification categories.

Enterprise AI projects often run into a data problem before they run into a model problem. The information needed for retrieval-augmented generation (RAG), AI agents, and other generative AI applications is often scattered across the organization. Before that data can support production workloads, teams need to find the right sources, make them usable, and keep access governed without creating unnecessary copies.

The Dell AI Data Platform serves as the data layer within Dell AI Factory with NVIDIA. It brings together storage, metadata, ingestion, search, retrieval, and orchestration capabilities to help enterprises use distributed data for RAG, AI agents, and production generative AI.

Gartner reports that lack of data readiness is a major barrier to enterprise AI adoption, driving more than 75% of organizations to prioritize AI-ready data investments. Gartner also recommends metadata management and semantic layers to add context to AI-ready data.

The Dell AI Data Platform at a glance

The Dell AI Data Platform brings together the data capabilities used within Dell AI Factory with NVIDIA, but it is not a single storage product. Different components handle storage, metadata, ingestion, search, and retrieval, while Elastic and NVIDIA provide additional RAG and accelerated-search capabilities. 

The architecture is modular: Storage Engines provide file, object, and parallel-file foundations; Data Engines provide analytics, processing, search, and retrieval; and the Data Orchestration Engine coordinates pipelines, models, and data sources across the AI lifecycle. Together, these engines function as an enterprise-wide context layer, carrying governed metadata and consistent structure across distributed data so RAG, agents, and other AI workloads retrieve information with the context they need, wherever it lives.

The table below maps each engine to the modular architecture described above.

How the Dell AI Data Platform supports enterprise RAG

Enterprise RAG depends on a reliable process for identifying, preparing, indexing, and retrieving content while keeping source data current and access governed. PowerScale, ObjectScale, MetadataIQ, the PowerScale RAG Connector, the Dell Data Engines, the Dell Orchestration Engine and NVIDIA retrieval technologies each support different stages of that workflow. The Data Orchestration Engine can coordinate these stages when included in the selected deployment.

1. Connect and identify enterprise data

PowerScale provides unified file and object access for unstructured enterprise data, including file protocols and S3 in supported configurations. ObjectScale provides enterprise S3/object storage optimized for object-first workloads, including AI data lakes, training data, model checkpoints, backup, archive, and globally distributed data at scale. According to Dell, MetadataIQ saves PowerScale file system metadata to an external Elasticsearch database so the workflow can track files that have already been processed.

2. Process new and updated content

The open-source PowerScale RAG Connector returns only new or modified files to the RAG framework, helping reduce repeated processing across compute, network, and storage resources.

3. Prepare, index, and search content

Files can be chunked and embedded using standard methods or NVIDIA NeMo Retriever. According to Dell, the Data Search Engine uses NVIDIA CUDA Vector Search (cuVS) for GPU-accelerated vector search, while ObjectScale provides an S3 Vector store. Teams that need keyword or hybrid search should confirm support for their planned configuration.

4. Retrieve governed context

Dell describes retrieval across multiple storage and database backends with document-level security, helping restrict results to information a user is entitled to access. It also documents an NVIDIA AI-Q enterprise RAG workflow running end to end on the Dell AI Data Platform on premises. Elastic powers Data Search Engine, while NeMo Retriever, NVIDIA Inference Microservices (NIM), and cuVS are NVIDIA integrations.

Supporting AI agents at scale

The Dell AI Data Platform can support AI agents at scale by giving them reliable access to current, governed data. As usage grows, teams also need to account for retrieval performance, concurrency, and the infrastructure required to support more demanding workloads.

PowerScale and ObjectScale provide the storage foundation, while the RAG Connector helps keep source content current by processing changed files. Dell Data Engines support search, retrieval, and structured context across that content, and document-level security can help control access across backends when the applicable integrations are configured and validated. As agent workflows grow more complex, the Data Orchestration Engine can help coordinate the pipelines and data sources multiple agents rely on, when included in the selected deployment. Capacity planning should also reflect both the expected workload and the performance demands around it.

How the Dell AI Data Platform works with existing storage and data environments

Dell says its Data Engines can connect to databases, data lakes, object stores, and file systems, allowing organizations to work with existing data environments rather than centralizing every source first. MetadataIQ can expose file metadata without relocating the underlying PowerScale content.

PowerScale for Azure supports NFS, SMB, and S3 with unified permissions, allowing AI workflows and existing applications to connect through standard storage protocols. Organizations should still verify specific integrations, identity controls, licensing, and architecture requirements.

How the Dell AI Data Platform helps move GenAI from pilot to production

Moving generative AI from pilot to production changes the demands on the data environment. Retrieval data needs to stay current, preparation must become repeatable, and teams need a reliable way to govern and operate the workload at scale. The Dell AI Data Platform can help operationalize that transition by keeping retrieval data current and coordinating data preparation across the AI lifecycle. The PowerScale RAG Connector helps keep RAG content current. Because production workloads depend on the context layer described earlier, the Data Orchestration Engine keeps pipelines, models, and data sources coordinated as GenAI moves from pilot to scale.

Dell Data Management Services can help organizations standardize, govern, and operationalize data. Monitoring, infrastructure optimization, and ongoing lifecycle management sit in the wider Dell AI Factory ecosystem through Dell Services and Dell AIOps rather than in the data platform alone.

How to evaluate the Dell AI Data Platform

Before adopting the platform, teams should check three areas:

1. Data and placement: Identify where structured and unstructured data resides, what can move, and what should remain in place for security, residency, or latency requirements.
2. Workload requirements: Estimate ingestion volume, retrieval latency, data growth, expected agents or users, and the compute capacity required around the data platform.
3. Integration and operations: Confirm database, catalog, cloud, and AI tool integrations, along with governance requirements, licensing, operational expertise, and relevant NVIDIA certification or validation.

Dell and NVIDIA certification and reference architectures

Dell’s NVIDIA AI-Q reference architecture

Dell is one enterprise AI platform vendor that publishes a validated reference architecture with NVIDIA. Its NVIDIA AI-Q enterprise RAG design runs on premises, with enterprise data remaining in the customer environment. The design shows how Dell storage, retrieval, and orchestration technologies can work with NVIDIA technologies for enterprise RAG.

NVIDIA-certified Dell storage

NVIDIA includes Dell PowerScale F710 among its NVIDIA-Certified Storage systems at the Foundation and Enterprise levels. PowerScale F710 is also certified for NVIDIA Cloud Partner deployments and DGX SuperPOD appliances.

Certification applies to listed F710 configurations. NVIDIA integrations such as cuVS or NIM are separate from NVIDIA-Certified Storage status and should not be extended to every Dell AI Data Platform component.

FAQs

What is the Dell AI Data Platform?

The Dell AI Data Platform is the data layer within Dell AI Factory with NVIDIA. It combines storage, metadata, ingestion, search, retrieval, and orchestration capabilities to support enterprise RAG, AI agents, and production generative AI.

What Dell AI Data Platform offerings are suitable for RAG use cases?

PowerScale and ObjectScale provide storage, while MetadataIQ and the PowerScale RAG Connector support metadata tracking and incremental ingestion. Dell Data Search Engine handles vector retrieval, and NVIDIA NeMo Retriever can support extraction, embedding, and reranking. The Data Orchestration Engine can coordinate the end-to-end pipeline, while the Data Processing and Data Analytics Engines support preparation and structured context where required.

Can the Dell AI Data Platform support AI agents at scale?

The platform supports growing agent workloads through scalable storage, incremental ingestion, vector retrieval, and governed data access. Capacity planning should account for data growth, retrieval latency, concurrent agents or users, and the compute required for each workload.

Can the Dell AI Data Platform work with our existing storage and data stack?

Dell says its Data Engines can connect to databases, data lakes, object stores, and file systems, while some deployments can use standard protocols such as NFS, SMB, and S3. Buyers should still verify their specific integrations, identity controls, licensing, and architecture requirements.

Can the Dell AI Data Platform help operationalize GenAI beyond pilots?

The Dell AI Data Platform can support the move from pilot to production through repeatable data preparation, current retrieval data, governed access, and orchestration across data pipelines, models, and data sources. Dell Services and Dell AIOps can support broader operational requirements such as monitoring, infrastructure optimization, and lifecycle management.

Which enterprise AI data platform vendors publish validated reference architectures with NVIDIA?

Dell is one documented example. Its NVIDIA AI-Q enterprise RAG reference architecture shows how Dell storage, retrieval, and orchestration technologies can work with NVIDIA technologies in an on-premises deployment.

Which storage platforms are NVIDIA-certified or validated for AI training and inference?

For Dell, NVIDIA lists PowerScale F710 under Foundation, Enterprise, NVIDIA Cloud Partner, and DGX SuperPOD certification categories. Certification applies to the listed F710 configurations and should not be extended to every Dell AI Data Platform component.