Enterprises have poured billions into AI infrastructure — graphics processing units, cloud capacity and model tooling — yet most deployments remain mired in experimentation rather than generating measurable business value. The bottleneck is not compute. It is AI-ready data.

The gap between owning data and having AI-ready data is proving to be the defining obstacle of this infrastructure cycle. A commissioned IDC Global AI Readiness Survey found that 94% of information technology leaders identify data quality as the primary factor in AI success — yet most enterprise data remains unclassified, ungoverned and unfit for production AI workloads. Everpure Inc. and Nvidia Corp. are jointly targeting this problem through co-engineering spanning data intelligence, vectorization and GPU-accelerated inference pipelines. The goal is to help organizations move from AI experimentation into production, according to Jason Hardy (pictured, right), vice president of storage technology at Nvidia Corp.

“There is this nervousness to fully commit; there is a cost attached to it, but it’s also where to start,” he said. “They get overwhelmed, and then they kind of freeze out. Where we like to see how we can streamline through that is, ‘Hey, let’s shrink this down into a very focused path and then help walk through what does that mean from the infrastructure, but also the data side of it.'”

Hardy and Shawn Rosemarin (left), vice president of R&D and customer engineering at Everpure, spoke with theCUBE’s Alison Kosik and Christophe Bertrand at Pure Accelerate 2026, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed the Everpure and Nvidia co-engineering partnership, the AI-ready data challenge and how the newly announced Everpure Data Stream reduces AI data preparation from months to minutes. (* Disclosure below.)

Turning raw enterprise data into AI-ready data

The partnership’s central thesis is that buying GPUs without solving the data problem is the enterprise equivalent of building a factory with no raw materials. Most enterprise data today is the equivalent of oil sands — abundant but unrefined — and must be curated, classified and vectorized before it can serve as the raw material for an AI factory, according to Rosemarin.

“That raw material is data,” he said. “It’s not AI-ready data. That is really the crux of the issue: What is the difference between data and AI ready data that allows us to think of it from a term of heavy crude, light crude [and] oil sands? What does the refinement of your data state look like to actually make data an AI-ready raw material?”

The answer, both executives agreed, lies in finding, classifying, securing, vectorizing and indexing enterprise data before pointing a model at it. Everpure Data Stream automates this pipeline — integrating data intelligence, vector databases and GPU-accelerated compute in a single architecture — so that inference runs against a high-context, curated dataset rather than a sprawl of disconnected silos, Rosemarin noted. Nvidia’s role is to ensure its libraries, networking and storage reference designs are co-engineered with Everpure’s software so that every GPU in the factory stays busy, Hardy added.

“I bought GPUs; now I need to reinforce it with the rest of the IT infrastructure to be able to drive that forward,” he said. “The investment by itself isn’t enough. It’s the ecosystem that gets wrapped around it that is needed to drive forward and get, ultimately, that outcome.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of Pure Accelerate 2026:

(* Disclosure: TheCUBE is a paid media partner for Pure Accelerate 2026. Sponsors of theCUBE’s event coverage do not have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE