Everpure Announces New Data Management Capabilities for Production AI at Scale. Platform innovations give enterprises governed access to their data and predictable AI costs as they move AI from pilot to production.

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Beyond model performance, enterprise AI faces a larger problem: fragmented, poorly governed data and expensive to move or scale. As more AI agents are coming into production, the biggest bottleneck is the data layer itself.

Everpure’s latest platform updates are aimed at this pressure point. The company is expanding its data management capabilities around governed access, faster inference, and predictable AI costs, making the data layer a core part of production AI rather than a background support system. The updates build on the company’s Data Primacy strategy, which places data rather than applications at the focus of enterprise architecture and target three recurring barriers to AI scale: fragmented context, unpredictable inference costs and complex deployment environments.

“Enterprise AI is hitting a wall not because the models are lacking, but because data is not ready for real-time, autonomous agents. We are eliminating that friction. By making enterprise data continuously governed, automated, and instantly accessible, we’re giving organizations the foundation to move AI out of the lab and into production with the necessary confidence,” said Prakash Darji, General Manager, Data & Digital Experience at Everpure.

Making Enterprise Data Management Simple and Secure

Enterprises deploying AI agents often struggle to give them reliable, governed access to data across the business, limiting how much those agents can actually achieve. Everpure Data Intelligence solves this by discovering, classifying, and contextualizing enterprise information at its source spanning the Everpure Platform, public clouds, SaaS applications, and third-party storage.

Building on this, new capabilities give autonomous agents and administrators direct, secure access to live enterprise context without custom API work. It implements the open Model Context Protocol (MCP) so AI agents and security tools can query live data catalogs using natural language. It streamlines deployment through the existing Pure1 console, without complicated professional services engagements, and accelerates time-to-value without the operational overhead of setting up separate management servers. And it shows who can access each file share and how stale it is, without ever reading file content, so teams can fix exposure and reclaim capacity before opening shares to AI agents.

To support growing workloads from archive to AI, Everpure is expanding the bounds of performance and capacity efficiency in a non-disruptive way. The new capabilities bring high-performance AI execution directly to data at the source and deliver production speed without ever moving data from its system of record

The industry’s shift is getting clearer. Production of AI is dependent on data architecture as it is on different models. As businesses scale agentic AI deployments, the ability to govern access, reduce latency and control costs is a growing challenge. With Everypure’s update in capabilities, companies may be able to solve the data layer problems first before moving into pilots and scaling.

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