The first thing a company typically discovers when it deploys Everpure Data Intelligence isn't what it expected. It's data it didn't know it had.
Nirav Sheth, VP of Worldwide Systems Engineering at Everpure, described a proof of concept that illustrates the pattern well. A customer connected Data Intelligence to their environment and found sensitive financial data sitting in a Google Drive folder with open permissions — visible to anyone in the company, with no governance controls of any kind. Nobody had flagged it. Nobody had even known it was there.
"I ask customers: do you have a really good handle of all your data across all your applications from the edge to the data center to the cloud?" Sheth told me at Pure Accelerate 2026. "And many actually just start to laugh as an acknowledgment of, yeah, you got me."
That moment — when a customer realizes the scope of what they don't know — is where Sheth's work actually begins. As VP of Worldwide Systems Engineering, he's closer to real-world deployment than the product managers and executives who set roadmap direction. What he sees most often isn't a technology problem. It's an awareness problem.
What the First 90 Days Actually Look Like
When customers first deploy Data Intelligence, the expected conversation is about AI readiness, pipeline automation, and semantic context. What actually happens is simpler and more unsettling: the tool maps the full scope of the customer's data estate, and that scope is almost always larger and messier than anyone anticipated.
Sheth described the sequence clearly. Step one is discovery — understanding what data exists, where it lives, and who can access it. Step two is classification and contextualization — tagging sensitive data, building governance policies, and mapping relationships across systems. Only after those two steps does the conversation about AI pipelines and Data Stream actually make sense, because only then does the customer understand what should and shouldn't be feeding those pipelines.
"Some customers find they actually have to do a lot of data cleanup before they even think about a data pipeline," Sheth said. "You need to make sure the right things are actually going to be going into that pipeline."
The court system demo staged during Wednesday's keynote illustrated the point. A large metropolitan court system — Sheth declined to name it — used Data Intelligence to map its entire data estate across multiple tiers of courts before building any AI models. The reason: too much sensitive data to use any third-party AI service. They needed to understand what they had, wall off what couldn't be touched, and build a secure internal capability — the equivalent of their own Harvey AI — before any pipeline work could begin. Two months after not knowing what data they had, they were extracting value from it.
The Global Manufacturer: What Actually Happened
One of the more detailed customer stories to come out of this week's conference involves a global manufacturer with 80 sites worldwide. Sheth walked through the full arc of that engagement during our conversation, and it's worth being specific about what the case study actually shows — and what it doesn't yet.
The manufacturer was running legacy hyperconverged infrastructure across all 80 sites, with support approaching its end. The security implications were immediate: once the vendor stops issuing updates, every unpatched vulnerability becomes a permanent exposure. That alone was a compelling reason to modernize. But the performance case was equally clear — hyperconverged architecture was struggling to keep pace with production throughput as manufacturing volumes increased.
The engagement ran 60 days, start to finish. The first phase covered the self-assessment, discovery, and transition planning — mapping current-state architecture and modeling what a move to a three-tier architecture based on Everpure FlashArray would look like at scale. The second phase went further: exploring what a video surveillance solution could do for manufacturing quality. The answer, it turned out, was significant. By building AI models around video feeds on the manufacturing line, the company could detect counterfeit parts in real time, reducing defect rates and improving field quality.
One thing worth clarifying: the gross margin expansion Sheth referenced in his keynote presentation is a projection, not a delivered result. The migration plan for 80 global sites is approximately 1 year for phase 1. The business case has been made to the CISO and CIO — and it was strong enough for them to commit — but the results are still being built.
What struck me about the story was Sheth's description of how the engagement was framed. "We anchored on what's most important for them," he said. "Production is money. We really wanted to anchor around what's going to ensure that they have higher production volume, better throughput, and fewer quality issues — versus coming in and saying, hey, we have a really cool technology platform."
That framing — infrastructure partner as business consultant rather than hardware vendor — came up repeatedly throughout our conversation.
The Migration Risk Nobody Talks About
The most honest answer Sheth gave came when I asked about the most common architectural mistake systems engineering teams see when customers try to scale before addressing existing risk.
His answer wasn't what I expected. He didn't describe a technical anti-pattern or a common configuration error. He described organizational inertia. "One of the biggest general challenges is sometimes a customer has been with a certain platform for five years, and they feel like moving to a different provider could end up being a bigger challenge or a bigger risk than staying with the incumbent."
His point is that the decision not to move is itself a risk posture — one that customers often don't examine critically because it feels like the safe default. Everpure's response to that has been to build SLA-backed migration tooling and use Data Intelligence to map what's running on the incumbent environment before any transition begins, making the migration plan more defensible and reducing the perceived risk of change. The goal is to earn trust incrementally, demonstrate value on the customer's existing infrastructure, and let the case for deeper adoption build from there.
"We want to meet them where they're at, we want to earn their trust, and then over time they'll continue to grow with us," Sheth said. The 84 NPS score — independently audited — is the metric he cited as evidence that the approach is working.
Sheth's framing of the EDC Success Blueprint differs slightly from how it was presented in formal announcements, and the difference is worth surfacing for engineering leaders evaluating whether to engage with it.
Officially, the Blueprint is a structured methodology — self-assessment, maturity guides, dedicated workshop — for moving toward an Enterprise Data Cloud architecture. In practice, Sheth describes it less as a methodology and more as a sequencing tool. Its real value is in helping organizations with 15 competing priorities figure out which ones to tackle first, in an order that's actually achievable given their current state, available resources, and internal political realities.
"If you try to take on too much in any one go, you might not have the level of internal support that you need," he said. "We really want to make sure that we're anchoring on what's most important in terms of our customers' priorities and their business outcomes."
The workshops included with Evergreen subscriptions are on-site, dedicated sessions — not a fee-based add-on. Sheth was clear about that distinction because it affects how customers should think about the commitment being asked of them. Everpure is investing its own systems engineering resources in these engagements, which means the company has a stake in the outcome being real rather than just the contract being signed.
A Different Company Than It Was
Sheth's closing comment was the most direct summary of what this week has actually been about, beyond any individual product announcement: "Everpure is a very different company than where we were a couple of years ago."
He pointed specifically to the company's founder, echoing the same sentiment on stage: the company that built its reputation on FlashArray has expanded into a full-stack data intelligence platform where storage is the foundation, not the ceiling. Data Intelligence, Data Stream, Portworx — these products add value regardless of whether the underlying storage is Everpure's. That's an intentional architectural bet, not a side effect.
"We can now add value no matter where the data resides," Sheth said.
For developers, engineers, and architects deciding whether to pay attention to Everpure's announcements this week, that's the most useful frame. The company is no longer selling storage performance as its primary value proposition. It's selling visibility and control over data that enterprises already have, wherever that data happens to live — with Everpure storage as the preferred home but not the prerequisite for value.
Whether that bet pays off at scale, across 14,500 customers with varying levels of maturity and readiness, is the story that plays out over the next several years.
Everpure (NYSE: P) provided press access to Pure Accelerate 2026.
