Everpure has announced new platform capabilities designed to help enterprises overcome data management challenges as they move Artificial Intelligence from pilot projects into production. The enhancements focus on providing AI agents with secure access to enterprise data, improving inference performance and reducing costs while strengthening data governance and cyber-resilience.
Everpure has announced new platform capabilities designed to simplify enterprise data management, advancing its Data Primacy vision.
Introduced at Pure//Accelerate in June, Data Primacy is the principle that data – not applications – must be the core driver of enterprise architecture in the AI era. The updates continue to advance the modern data management capabilities required to solve bottlenecks stalling enterprise AI at scale: fragmented context, unpredictable inference costs and slow, complex deployments.
Prakash Darji, General Manager, Data & Digital Experience at Everpure, said: “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 organisations the foundation to move AI out of the lab and into production with the necessary confidence.”
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 contextualising 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:
- Native MCP integration: Implements the open Model Context Protocol (MCP) so AI agents and security tools can query live data catalogues using natural language. Agents can now find relevant data and understand its sensitivity class as an input to AI, Agent Workflows and Analytics.
- Turn-key deployment: Streamlines deployment without complicated professional services engagements through the existing Pure1 console and accelerates time-to-value without the operational overhead of setting up separate management servers.
- Privacy-first file intelligence: 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.
Delivering AI performance, next-generation efficiency and cost predictability
In order 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:
- Accelerated LLM inference via PureKVA (Key-Value Accelerator): Everpure FlashBlade now pre-stages context directly into GPU memory to deliver up to 20x faster Time to First Token (TTFT). Supports enterprise multi-tenancy with zero dataset relocation, eliminating GPU idle time, increasing token throughput and decreasing response lag for real-time apps.
- Always-on DeepReduce data compression: Scans storage blocks continuously across FlashBlade systems to find sub-block data similarities that traditional deduplication misses, even on pre-compressed content. Usable storage capacity expands automatically without impacting write performance or requiring manual scheduling, significantly reducing hardware footprint and cross-cloud expenses.
- Intelligent token optimisation reference architecture: Given the need for enterprises to own their own data and optimise AI spend, Everpure now delivers a reference architecture using open weight models. This allows more control over data and predictable AI costs, cutting overall API token usage from external providers.
Individually, these capabilities close specific gaps in simplicity, security, performance and cost. Together, they give enterprises a single, continuously updated foundation for managing their data and running AI in production, built to keep pace as agentic workflows scale.
Because these capabilities classify what data is sensitive and who touches it, they also strengthen cyber-resilience, since the same context that makes data safe for AI to use is what determines how it gets protected and what gets recovered first.
