AI DATA PLATFORM
Confident AI starts before the GPU
Hammerspace and Secuvy deliver an AI Data Platform — built on NVIDIA's reference design — that moves enterprise data to your GPUs fast. Secuvy is the control layer in front of it: discovering, classifying, and governing every source so only appropriate data reaches your models, with a data bill of materials proving exactly what fueled each one.
What is the AI Data Platform
Hammerspace and Secuvy’s AI Data Platform, in plain terms
An AI Data Platform (AIDP) is the system that moves enterprise data to your GPUs fast and at scale. Hammerspace builds one of the leading AIDPs on NVIDIA’s reference design — uniting accelerated compute with data-in-place storage so AI infrastructure can ingest, transform, retrieve, and serve data continuously.
Secuvy completes it. Where the platform moves data, Secuvy decides what belongs in the pipe — discovering, classifying, and governing every source so only appropriate data ever reaches your models. Together, Hammerspace and Secuvy turn raw enterprise data into AI-ready data you can prove.
See the Hammerspace + Secuvy partnershipThe platform at a glance
- What it is
- An AI Data Platform on NVIDIA’s reference design — Hammerspace moves the data, Secuvy controls it
- Hammerspace’s role
- Data-in-place storage that feeds GPUs fast, at enterprise scale
- Secuvy’s role
- Discovers, classifies, and governs every source so only appropriate data reaches AI
- The proof
- A verifiable data bill of materials (DBOM) — the system of record across your data
Works across storage environments
Why NVIDIA built it
Three reasons, in order of strategic weight
AIDP isn’t a side project — it’s how NVIDIA keeps its GPUs running hot and its ecosystem compounding.
Remove the data bottleneck gating GPU utilization
Multi-million-dollar GPU clusters sitting idle, waiting on storage, are a problem for AI investments. AIDP standardizes how data reaches the GPU so the chips can run fully utilized.
NVIDIA powers a unified foundation for AI infrastructure
Publishing the reference architecture gives the industry a common foundation — BlueField for the data plane, Spectrum-X for the fabric, and a thriving ecosystem of storage innovators building on top — driving faster adoption and a more cohesive AI infrastructure market.
Agentic AI changed the math
Long-running agents need continuous data retrieval and persistent context, not one-shot training. With CMX and STX as building blocks, AIDP delivers faster inference, persistent agent memory, and the scale to run agentic workloads across the enterprise.
The gap
AIDP moves data fast — it’s silent on whether the data should move at all
The reference architecture assumes you already know exactly what’s in your data. In practice, almost no enterprise does — and the moment the AI pipe goes live, the unanswered question becomes: what are we pumping through it?
AIDP assumes you already know:
Get it wrong and you’ve built a faster way to send the wrong data — including PII, IP, and regulated content — into models at scale.
Where Secuvy fits
As you stand up AIDP-based infrastructure across any storage environment, you're industrializing the AI pipe. Secuvy's self-learning filtering engine, contextual data linkage, and non-pattern classification answer the question AIDP leaves open — what belongs in the pipe — so every GPU cycle is spent on the right data.
That upstream control is exactly what makes the AIDP investment defensible. Without it, you've built a faster way to send the wrong data — including PII, IP, and regulated content — into models at scale.
The data bill of materials (DBOM)
Secuvy's self-learning engine, contextual linkage, and non-pattern classification produce a verifiable data bill of materials — the system of record across all your data — so you can prove your AI pipeline is fueled by the right data, and only the right data.
- The system of record across all your data
- Proof the right data — and only the right data — fueled every model
- Lineage you can defend to a regulator, an auditor, or a board
The outcome
Make your AIDP investment defensible
What changes when only appropriate data reaches your GPUs.
Lower Risk
Prove that PII, PHI, IP, and classified data stay out of training and inference — with lineage you can defend to a regulator, auditor, or board.
Lower Cost with Better Data
Cleaner, smaller datasets shrink GPU and storage costs and cut duplicates — so AI spend drives value instead of burning compute.
Faster Time to Value
Replace months of manual scrubbing with continuous filtering. Idea to first results in days, not quarters.
Confidence to Decide
A verifiable DBOM proves exactly what's feeding your AI — turning it from a promising pilot into infrastructure you can stand behind.
Take it back to your team
Read the briefs behind the architecture
The partnership and the platform, in plain terms — straight from Secuvy and Hammerspace.
Solution brief · Secuvy + Hammerspace
Confident AI, Less Risk, Faster Returns
How Secuvy and Hammerspace make enterprise data AI-ready across hybrid and distributed storage.
View the brief →Solution brief · Hammerspace AIDP
The Hammerspace AI Data Platform
Hammerspace’s AI Data Platform — a data-in-place path to AI-ready data, built on NVIDIA’s reference design. Read it on hammerspace.com.
View the brief →Prove the right data is fueling your AI.
Book a demo →What is an AI Data Platform?
An AI Data Platform moves enterprise data to accelerated computing and GPU environments at scale. Hammerspace's implementation is built on NVIDIA's reference design, with Secuvy adding the control layer that determines what data belongs in the pipeline.
Why does AIDP need a data control layer?
AIDP is designed to move data quickly, but it does not determine whether that data is sensitive, duplicate, regulated, or fit for purpose. Secuvy discovers, classifies, and governs each source first so only appropriate data reaches your models.
Does Secuvy replace storage infrastructure or NVIDIA AIDP?
No. Secuvy sits in front of AIDP-based infrastructure across storage environments. It provides the upstream classification and governance signals that help those systems move the right data to your GPUs.
How does Secuvy decide which data is appropriate?
Secuvy's self-learning engine uses contextual linkage and non-pattern classification to understand data beyond known rules and dictionaries. It identifies what belongs in the pipeline and what should be held out before data reaches your models.
What does the data bill of materials prove?
The DBOM records what data fueled each AI pipeline, what was held out, and why. It gives your team current lineage they can defend to a regulator, an auditor, or a board.
Only appropriate data reaches your AI.