Accelerating AI & GenAI Data Pipelines
85% of AI projects fail — most often due to fragmented storage and poor data access. SANDataWorks deploys unified, high-performance infrastructures built specifically to feed data-hungry models.
The data engine, not the model, is the bottleneck
The numbers below are why AI initiatives stall in pilot. Fix the data plane and the rest follows.
85%
Of AI projects never escape the lab into production — usually a data pipeline problem.
300 GB/s
Read throughput per NVIDIA DGX SuperPOD cluster — GPUs stay fed, not starved.
< 1 ms
Sub-millisecond response — the metric that matters more than raw bandwidth for inferencing.
−90%
Reduction via Workload Factory automation in cloud RAG deployments.
Direct answers for the AI architect
How do we safely prepare proprietary data for Retrieval-Augmented Generation (RAG)?
Successful RAG starts with unified, governed data.
SANDataWorks deploys NetApp Data Classification to automatically map and remove duplicate, stale, or PII-bearing data before it reaches your language models. The NetApp GenAI Toolkit then securely combines public foundation models with your private data — pinpoint accuracy without leaking sensitive information into public LLMs.
How do we eliminate data bottlenecks that slow model training and GPU utilization?
Legacy storage cannot keep up with modern GPUs.
You need high-performance, all-flash architectures — NetApp AFF A-Series and NetApp AFX — delivering ultra-low latency and massive throughput. SANDataWorks deploys these as turnkey NetApp AIPod stacks or as part of NVIDIA DGX SuperPOD validated architectures, so expensive compute stays fully utilized during training and inferencing.
How do we govern PII before it reaches our AI models?
AI training on sensitive data is a compliance time bomb.
Use AI-driven NetApp Data Classification to discover, map, and isolate PII across your hybrid estate — aligned to GDPR, CCPA, and HIPAA. SANDataWorks integrates the classification pipeline with your training workflow so sensitive records are filtered out before vectorization, not flagged after a breach.
The stack we deploy for production AI
Validated architectures and integrated tooling — the engine for fast, governed AI POCs and production rollouts.
GenAI Toolkit & RAG Pipelines
Securely combine public foundation models with your proprietary enterprise data for accurate Retrieval-Augmented Generation.
Learn moreNetApp AIPod
Validated converged infrastructure built on AFF + NVIDIA accelerated compute. No integration risk.
Learn moreDGX SuperPOD with ONTAP
300 GB/s read throughput per cluster, sub-1ms latency. The validated reference for enterprise-scale training.
Learn moreNetApp AI Data Engine
Integrates discovery, policy guardrails, and real-time vectorization at the storage layer. Always fresh data.
Learn moreDon't commit to massive CAPEX without proof of value.
SANDataWorks deploys cost-effective, secure RAG POCs in days using NetApp Workload Factory — so your AI project ships before your competitors finish a Statement of Work.
Three actionable best practices
Eliminate data silos first
Before investing heavily in compute, unify your data plane. AI workloads need seamless mobility across edge, core, and public cloud — ONTAP gives you that single OS everywhere.
Unified Data StorageGovern data before feeding the model
Actionable tip: use AI-driven Data Classification to discover, map, and filter out Personally Identifiable Information before it enters your training pipeline.
Data ClassificationUse the Rapid POC Program
Actionable tip: engage SANDataWorks to deploy a secure, cost-effective POC environment using cloud-based tools — accelerating your AI project cycle from day one.
Start a Rapid POC85% of AI projects fail. Yours doesn't have to.
SANDataWorks brings the validated architectures, the data governance, and the Rapid POC discipline that turn experiments into operational systems. AI POCs in days — not months.