Everpure Unveils Data-First AI Strategy, Targets $21B Intelligence Market

Everpure Unveils Data-First AI Strategy, Targets $21B Intelligence Market

Data-first strategy: The company aims to make data—not individual applications—the foundation of enterprise IT, offering a neutral management layer across cloud, on-premises, mainframe, structured and unstructured environments. It estimates the data-intelligence market could expand from $6 billion to $21 billion by 2030.

AI and autonomous infrastructure: Its Enterprise Data Cloud combines unified data services, policy-driven automation and data-intelligence tools, while offerings such as Data Stream and key-value acceleration target retrieval-augmented generation and faster, lower-cost AI inference.

Neocloud opportunity—and an identity issue: FlashBlade//EXA is designed to remove storage bottlenecks in large AI deployments and has reported performance gains for an early customer. However, the article appears to conflate this technology company with Everpure, Pentair’s water-filtration brand; NYSE:P is not identified as a separately listed Everpure entity. Interested in Everpure? Here are five stocks we like better . Treasury Yields Hit a 19-Year High—These 2 Bond ETFs Offer Monthly Income

Everpure NYSE: P outlined an expanded technology strategy centered on what executives called “data primacy,” arguing that artificial intelligence is pushing enterprises to organize IT architectures around data rather than individual applications. At its financial analyst meeting in Santa Clara, Chairman and Chief Executive Officer Charlie Giancarlo said the company believes its business is accelerating while its opportunity is expanding into additional high-growth areas. Giancarlo said AI is driving a fundamental architectural shift in which organizations increasingly organize around data, rather than applications, creating new opportunities for the company. Get Everpure alerts: Sign Up

Data management expansion

Prakash Darji, general manager of Everpure’s Digital Experience Business Unit, said enterprises have historically purchased applications and then spent heavily integrating data among them. He estimated that 80% to 90% of enterprise spending goes toward integration work across technology assets. Darji said the growing use of AI agents could further increase those costs because relevant context is often fragmented across applications, data warehouses and other systems. Everpure’s proposed approach is to create shared context around data wherever it resides, allowing organizations to reuse data in place rather than continually copying it between systems.

"Agents work better directly on data," Darji said, arguing that data context must persist with the data as it moves across storage locations and performance tiers. He said the company views the storage operating system as a place to maintain persistent context and policy controls for data. The company positioned its approach against what Darji described as “walled garden” strategies from hyperscalers, application vendors and data platforms that require customers to move data into their respective platforms. Everpure instead aims to provide a neutral data-management layer that can work across cloud, on-premises, mainframe, structured and unstructured data environments.

Ashish Gupta, general manager of Data Management, said Everpure Data Intelligence discovers and classifies data regardless of where it resides, including on Everpure systems and third-party storage. The offering is designed to identify sensitive information and contextualize data based on business processes and relationships among records. Gupta said customers are increasingly bringing together CIOs, CISOs and chief data officers whe...