Everpure Unveils Data-Primacy Architecture for the AI Era

55 The traditional IT hierarchy is breaking. For years, enterprises have operated under an application-centric model, where critical data is trapped inside silos dedicated to specific functions like finance or logistics. This fragmentation creates massive data sprawl and costly replication of untrusted information. Everpure is betting that the only way to survive the current AI shift is to move toward data primacy. In this model, information is liberated from individual applications to become a shared, governed system of record. “AI completely upends the traditional IT hierarchy; enterprises that do not shift from app-centricity to data primacy will fall behind,” said Charles Giancarlo, Chairman and CEO of Everpure.

Why Everpure is Abandoning App-Centric Storage

The pivot addresses a fundamental flaw in how companies store information. According to Patrick Smith, field CTO for EMEA at Everpure, environments have been application-centric for decades, meaning data is locked behind the application. With thousands of applications in a single enterprise, the result is thousands of silos. This structural inefficiency acts as an operations bottleneck. When AI agents attempt to access this fragmented data, the result is often poor output and spiraling operational costs. By embedding context, semantics, and governance directly at the data layer, Everpure aims to reduce the fragmentation caused by the proliferation of AI agents. This ensures that privacy rules and lifecycle management are permanently attached to the information itself, rather than being policed by external software.

Everpure Data Intelligence and the Universal Data Intelligence Layer

The center of this strategy is Everpure Data Intelligence (formerly 1touch.io). This tool discovers, classifies, and contextualizes information at its source across public clouds, SaaS applications, and third-party storage. As reported by Computer Weekly, this functionality is part of a broader Universal Data Intelligence (UDI) layer. This layer creates a semantic knowledge graph of relationships across datasets, allowing data science teams to identify relevant information for specific use cases without performing manual extractions. The system provides three primary capabilities:

• Universal Discovery: Visibility into structured and unstructured data, including SQL Server and Oracle databases.
• Automated Governance: Scanning for sensitive information, such as PII and PHI, to ensure secure compliance.
• AI-Ready Context: Mapping raw data to real-world business definitions to reduce token costs and improve response accuracy for AI agents.

This intelligence layer makes data relationships available via APIs and the Model Context Protocol (MCP), providing AI models with highly relevant, accurate inputs.

Accelerating Production AI via Everpure Data Stream

If Data Intelligence is the map, Everpure Data Stream is the engine. Based on the NVIDIA AI Data Platform reference design, Data Stream converts unstructured data into real-time AI results by replacing manual ingestion with a GPU-accelerated pipeline. The efficiency gain is stark: Everpure claims the system reduces raw data preparation time from months to minutes. “The biggest bottleneck to AI adoption right now isn’t the software, it’s the plumbing. Putting data at the absolute center of the enterprise strategy is exactly how IT leaders can rein in runaway operational costs and accelerate rollouts.”Matt Kimball The economic stakes are high. Sabur Mian noted that “idle GPUs are economically destructive,” highlighting the danger of having massive compute power that sits unused because the data pipeline cannot feed it fast enough. Robert Lee, CTO of Everpure, describes the current era as a “massive capital supercycle in AI.” He argues that the ability to scale from small use cases to exabyte capacity on a unified platform will determine which companies become industry icons and which disappear.

Purity Turbo and Azure Native Scaling

Beyond the software layer, Everpure is updating its hardware and cloud offerings to support the extreme demands of AI training and real-time inference. The company introduced Purity Turbo, a software enhancement for the FlashArray//XL190. This update positions the system as the fastest in the FlashArray line, specifically targeting high-frequency AI workloads. Additionally, a new feature called Overdrive allows users in the Evergreen//One consumption model to burst performance 25% above their current service-level agreements. In the cloud, Everpure launched Everpure Cloud Azure Native for virtual machines. This service decouples storage from compute, which allows organizations to scale Microsoft Azure workloads more cost-effectively. It is scheduled for general availability in July. These updates collectively aim to build what Jason Hardy calls “AI factories”—architectures that bridge governed enterprise data with accelerated computing. The shift toward data primacy represents a fundamental change in the power dynamics of the data center. By stripping ownership of data away from the applications and placing it in a governed, universal layer, Everpure is attempting to solve the “plumbing” problem that has stalled many enterprise AI projects. The next 30 days will likely see how quickly early adopters can move from experimentation to full-production intelligence using the NVIDIA-integrated pipeline.