When ransomware hits, the standard playbook is to roll back the entire environment to the last known clean snapshot. It works, but it's blunt. You lose every legitimate change made since that snapshot, not just the malicious ones.
Prakash Darji, General Manager of Digital Experience at Everpure, described a more targeted approach during a conversation at Pure Accelerate 2026. Instead of rolling back everything, Data Intelligence can identify exactly which files were affected by an attack and execute a micro-rollback — restoring only the compromised data while leaving everything else untouched.
It's a small example with a big implication: once you actually understand what your data is and how it connects to everything else, you can act on it with precision instead of brute force.
That's the premise behind Everpure Data Intelligence, the product formerly known as 1touch.io, and the centerpiece of what Everpure is calling data primacy — a fundamental shift in how enterprise data architecture should work in the AI era.
How the Semantic Layer Actually Works
It's easy to talk about "semantic knowledge graphs" in the abstract. Darji walked through what it actually looks like in practice, using a purchase order as an example.
A PO might start as a file sitting in a file share. That file lives inside a database, which follows a particular schema, which contains specific fields. A content classifier identifies what those fields actually represent — this field is a vendor name, that one is a dollar amount. All of that gets mapped into a knowledge graph that captures the relationships: this purchase order connects to a specific entry in Oracle, which connects to a specific transaction in the billing system.
That's seven layers of context — file share, database, schema, field, content classifier, knowledge graph, relationships — built around a single piece of data that, in a traditional application-centric architecture, would just be an opaque record sitting in a silo.
The payoff is that once that semantic structure exists, AI agents can actually reason across systems instead of guessing. An agent looking at a purchase order can understand not just what the document says, but how it fits into the broader business process around it — without a developer manually writing integration logic to connect Oracle to the billing system every time someone needs that context.
Two Systems, Converging
One detail worth flagging for anyone tracking Everpure's roadmap: the fleet telemetry data Everpure has collected from its storage systems for years, and the semantic layer that Data Intelligence provides, are currently separate systems. According to Darji, they're expected to converge within the next year.
That's not a small integration. Everpure's storage telemetry operates at the infrastructure level — looking at volumes, file systems, and performance data. Data Intelligence operates at a higher level — understanding what the data means and how it relates across applications. Bringing those together means an AI agent could eventually reason about both what a piece of data represents and how the infrastructure underneath it is actually performing, in a single context.
It's a useful reminder that data primacy isn't a single product launch. It's an integration project that will play out over the next several quarters, built on a still-recent acquisition.
Small Models, Better Data
One of the more counterintuitive points Darji made — echoed elsewhere at the conference — is that small language models with focused, relevant data are outperforming large language models fed raw, fragmented data. That cuts against the assumption that bigger models always win.
The logic follows directly from the semantic layer argument: if an AI system has to wade through unstructured, poorly contextualized data to find what's relevant, it needs a much bigger model and a much bigger context window to compensate. If the data is already classified, contextualized, and connected through a knowledge graph, a smaller, more efficient model can get better results with a fraction of the token cost.
For engineering teams evaluating AI infrastructure investment, that's a meaningful trade-off to understand. The instinct to solve data fragmentation by throwing a bigger model at the problem may be solving the wrong layer entirely.
The Adoption Gap
Darji was candid about where most customers actually stand. By his estimate, 80 to 90% of customers still don't fully understand semantic data management — what it means, why it matters, or how it changes the way they should think about their data architecture.
That's a notable admission from a product leader at a company betting heavily on this direction. It also explains why so much of the messaging at this conference, from the keynote through every press conference session, spent as much time explaining the concept of data primacy as it did describing the products that implement it. Everpure is still in the education phase of this transition, not just the deployment phase.
Why Bet Horizontal
Darji was direct about Everpure's competitive logic: Data Intelligence works across any storage, not just Everpure's own platform. That's a deliberate choice, not an oversight. Everpure isn't using Data Intelligence as a wedge to sell more hardware — it's positioning the product as a horizontal semantic governance layer that adds value whether or not a customer runs Everpure storage underneath it.
He compared it directly to the company's earlier bet on all-flash storage. There were plenty of competitors in that race too, and Everpure backed the bet anyway because it believed the architecture was right, not because the path was uncontested. The same logic applies here: data primacy is a horizontal architectural bet, and Everpure is betting that being right about the architecture matters more than controlling every layer of the stack.
From Application GUIs to Persona-Based Workflows
The most forward-looking part of the conversation was Darji's prediction about how enterprise software itself changes once a shared semantic layer exists.
Today, most enterprise software is organized around applications — you log into your CRM to do CRM things, your ERP to do ERP things, and switching between them means manually carrying context in your own head. Darji's prediction is that this shifts toward persona-based workflows: instead of organizing around which application owns which task, software organizes around what a specific role actually needs to accomplish, pulling from a shared semantic schema regardless of which system the underlying data lives in.
In that world, the work shifts for engineers too. Less time goes into building one-off integrations between specific applications, and more time goes into managing schema consistency — making sure the shared semantic layer accurately represents what the business actually means by its data, so that every persona-based workflow built on top of it stays trustworthy.
It's a meaningful shift in what application development looks like, and it's a direct extension of the data primacy argument Everpure has been making all week: once data carries its own context, the application stops being the thing organizing the work.
The Pragmatic Reality of Migration
None of this means existing enterprise systems disappear overnight. Darji's view here is pragmatic, echoing a line he attributed to a former PeopleSoft CEO: nobody builds the world in seven days when there's already an install base to deal with.
Nobody is ripping out SAP. The realistic path is that new workloads get built data-first from the start, while legacy complexity gradually thins out over time as the economics and the use cases shift. Migration is a multi-year process, not a single cutover — a point Charlie Giancarlo also made directly when discussing Everpure's own roughly two-and-a-half-year internal journey toward data primacy.
The Practical Takeaway
For developers, engineers, and architects evaluating where to invest attention, Darji's conversation reinforces a theme that's run through this entire conference: the bottleneck for enterprise AI isn't model capability. It's whether your organization actually understands its own data well enough to feed AI systems something trustworthy.
Data Intelligence is Everpure's bet that solving that problem requires a dedicated semantic layer — not a bigger model, not another data lake, and not another copy of the same fragmented data. Whether that bet pays off depends on execution over the next several quarters, as Everpure works through its own admission that most of the market isn't there yet either.
Everpure (NYSE: P) provided press access to Pure Accelerate 2026.