One platform, four enterprise AI data challenges solved: Inside Dell AI Data Platform

As AI sweeps across the business world, enterprises are rapidly shifting from generic chatbots to custom AI models, domain-specific agents, and Retrieval-Augmented Generation (RAG) systems powered by private corporate data.

Bringing these custom AI systems to life requires high-speed storage, processing engines, vector search, orchestration, and security. The Dell AI Data brings these capabilities together in a single, unified data context layer that helps resolve data fragmentation – allowing enterprises to scale AI from testing to production.

TL;DR

• Enterprises are increasingly building custom AI models and RAG applications on their own proprietary data rather than relying solely on public tools
• Stitching together separate tools for storage, data prep, search, orchestration, and security leads to tool sprawl, bloated costs, system lag, and data privacy risks
• The Dell AI Data Platform combines storage, data, and orchestration engines into one integrated foundation, turning siloed data into an enterprise-wide data context layer while embedding security and NVIDIA-accelerated performance directly into the architecture

Moving enterprise AI into production requires data that models and agents can access, interpret and use under the right controls. Information spread across disconnected repositories can be difficult to retrieve, while manual preparation and indexing create delays. As workloads grow, inefficient GPU use and rising token consumption can also make costs harder to manage.

The Dell AI Data Platform addresses these barriers by connecting storage, data engines and workflow orchestration within an open, modular architecture. It helps organizations prepare and serve governed enterprise context while scaling capacity and processing resources independently.

The Dell AI Data Platform resolves fragmentation by consolidating storage, data, and orchestration engines into a unified architecture. Built around these primary engines, it turns scattered data repositories into an integrated data context layer, allowing enterprises to scale AI from early testing to production deployment without managing a complex web of disparate tools.

What is the Dell AI Data Platform?

Dell AI Data Platform, the data layer of Dell AI Factory with NVIDIA, is an integrated software and hardware framework structured around three core layers: storage engines, data engines, and workflow orchestration. Together, these engines unite data storage, processing pipelines, vector search, and security capabilities into an enterprise-wide data context layer rather than requiring custom API bridges or third-party middleware.

This approach is particularly useful for enterprises building RAG-based AI applications, which work by pulling facts directly from private company files such as internal PDFs, support tickets, or financial databases. Traditionally, feeding data into a RAG system requires exporting files, converting them into vector embeddings, and copying them into a separate cloud or vector database. Moving data this way creates duplicate "shadow data," incurs heavy bandwidth costs, and strips away original file permissions, risking sensitive HR or financial data being exposed to unauthorized users through AI prompts.

The Dell AI Data Platform solves this by letting AI models query files securely on-premises without making duplicate copies or altering file access controls. To keep search speeds fast across millions of documents, the platform integrates directly with NVIDIA AI Enterprise software and GPU acceleration. This hardware-software pairing handles the heavy compute required for chunking, vectorizing, and searching enterprise data, allowing AI agents to deliver real-time answers while keeping data strictly governed under existing corporate security policies.

An integrated data layer resolves key bottlenecks in enterprise AI deployment

Transitioning AI initiatives from sandbox testing to enterprise production pipelines requires eliminating severe data bottlenecks caused by fragmented tools and isolated silos. Consolidating these capabilities removes operational friction, allowing teams to move AI models into production without sacrificing speed or governance.

How does the Dell AI Data Platform coordinate data across fragmented environments?

The Dell AI Data Platform coordinates data across fragmented environments by establishing a shared access and orchestration fabric that connects structured, semi-structured, and unstructured data into a unified data context layer without forced data movement. Through integrated Dell Data Engines – featuring federated SQL querying via Starburst, distributed batch processing via Apache Spark, and full-text vector search via Elastic – teams can query data directly where it lives.

Dell Data Orchestration Engine further automates ingestion and metadata tagging across hybrid infrastructure. Functioning as a complete data layer for AI, this architecture enables engineering teams to place, process, and protect data across its entire lifecycle – eliminating duplicate shadow copies while turning isolated silos into a cohesive enterprise-wide data context layer.

How do NVIDIA-accelerated engines speed up AI ingestion and processing?

Integrating GPU acceleration directly into the Dell AI Data Platform’s data processing capabilities removes traditional CPU bottlenecks, drastically shortening model training and inference cycles. By embedding libraries such as NVIDIA RAPIDS (cuDF) for accelerated data transformation in memory, and cuVS for high-speed vector indexing, the architecture ingests, prepares, and queries enterprise data at hardware-level speed.

When benchmarked against standard non-accelerated CPU pipelines, this optimization delivers up to 12x faster vector indexing* and 19x faster time to first token during inference**. This acceleration ensures complex Retrieval-Augmented Generation (RAG) and agentic AI workflows operate without the latency spikes that often bottleneck production deployments.

How are security and resilience embedded directly into the data layer?

Security capabilities include access controls, data masking, encryption, data isolation and air-gapped architectures. Together, these measures help protect enterprise data and support governed AI operations.

How can organizations scale from AI experiments to enterprise production?

Scaling AI into production without over-provisioning requires matching the storage architecture directly to each stage of the data lifecycle. Raw data ingestion and long-term retention rely on cost-effective S3 object storage through Dell ObjectScale. At the same time, active training workloads use Dell PowerScale file storage or parallel file systems like the Dell Lightning File System to keep thousands of GPUs fully saturated.

What are some key capabilities of the Dell AI Data Platform?

• Data Analytics: Powered by Starburst and built on Trino, this engine lets teams query structured data across databases, data lakes, file systems and object stores without first consolidating it into a separate repository. It supports on-premises and cloud sources, with Hive, Iceberg and Delta Lake connectors providing governed access for analytics, AI models and agents.
• Data Processing: Built on Apache Spark, this engine prepares and transforms data for large-scale batch processing, streaming and AI workloads. It combines fine-grained access controls, resource management, notebooks and support for customer-supplied Spark images, alongside resilience features.
• Data Search: Powered by Elastic, this engine ingests, indexes and retrieves information from documents, support tickets, transcripts, logs and images. It combines keyword search for exact matches with vector search for meaning-based discovery, and accelerates vector index creation within the data layer. Integration with the Analytics Engine allows queries to draw on both structured records and unstructured content.

AIDP supports open standards like Iceberg and Delta Lake, together with file formats such as Parquet, Avro and ORC. Its integrated metastore connects to multiple data lakes and supports Iceberg v3. Metadata, tags and labels associated with unstructured data remain accessible outside the platform, helping organizations retain flexibility as their tools and workloads change.

For enterprise IT teams, the next step is to identify which data barriers are holding their AI projects back. That may mean connecting isolated information, improving retrieval, strengthening access controls or planning for growing demand. The Dell AI Data Platform brings these capabilities into a shared foundation, giving organizations a way to build on their existing data environment and develop AI services that can be operated and expanded over time.

If you think the Dell AI Data Platform could benefit your business, find out more on the Dell website.

*Based on Elastic’s benchmark of Elasticsearch with NVIDIA cuVS (December 2025). Testing compared GPU-accelerated and CPU-only vector indexing on a single AWS g6.4xlarge instance with local NVMe storage, using 2.6 million 1,536-dimensional vectors at recall levels up to 95%. Results may vary.

**Based on internal Dell testing (October 2025) using the vLLM inference engine, LMCache with Dell connector, and NVIDIA NIXL library. Testing measured a 100% Key-Value (KV) cache hit rate on four NVIDIA H100 GPUs, using the LLaMA-3.3-70B Instruct model with Tensor Parallelism=4. Results may vary based on system configuration, workload, and other factors.

FAQs

What enterprise AI challenges does the Dell AI Data Platform solve?
The Dell AI Data Platform addresses data silos, unpredictable AI costs, security and governance gaps, and complex data pipelines. It combines governed data access, automated workflows and independent scaling to help organizations move AI applications from experimentation into production.

How does the Dell AI Data Platform securely support Retrieval-Augmented Generation (RAG)?
RAG workflows query private company files (such as PDFs, support tickets, and databases) directly on-premises without exporting files, making shadow copies, or stripping away original file access permissions.

How does the Dell AI Data Platform lower data center operational costs during scale-out?
High-density storage tiering – across PowerScale, ObjectScale, and the Lightning File System – delivers full GPU throughput while requiring up to 50% lower rackspace storage footprint.