Everpure Announces New Data Management Capabilities for Production AI at Scale

Everpure (P) announced new data management capabilities designed to support enterprise AI in production, with availability planned for October 2026.

Rhea-AI Summary

Everpure (P) announced new data management capabilities designed to support enterprise AI in production, with availability planned for October 2026.

The updates add integration with the Model Context Protocol, an open standard connecting AI tools to data, alongside deployment through the Pure1 console and file-access analysis that does not read file content. FlashBlade updates include accelerated language-model inference and continuous data compression. Everpure says its PureKVA accelerator delivers up to 20x faster time to first token, the wait before a model begins responding. A reference architecture using open-weight models is designed to reduce external providers' API token usage and give enterprises more control over their data and AI costs.

Key Figures

Faster time to first token: up to 20x faster Capability availability: October 2026

Faster time to first token

up to 20x faster

LLM inference via PureKVA

Capability availability

October 2026

New capabilities announced in this release

Previous AI Reports

1 past event · Latest: Jun 17

Same Type 1 event

1. Jun 17

   Data-primacy architecture

   24h Move

   -0.8%

   Established Data Intelligence architecture for discovering and contextualizing data across clouds and storage.

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Key Terms

model context protocol, llm inference, time to first token (ttft), deduplication

4 terms

model context protocol technical

"Implements the open Model Context Protocol (MCP)"

A model context protocol is a set of rules or guidelines that determine how a financial model interprets and applies information within a specific situation. It helps ensure consistent and accurate analysis by clarifying what data or assumptions are relevant in a given scenario. For investors, it provides clarity on how predictions or assessments are made, increasing confidence in decision-making.

llm inference technical

"Accelerated LLM Inference via PureKVA"

llm inference is the process of asking a large language model a question or giving it new text and getting its generated response or prediction in real time. Think of it as consulting an already-trained expert who reads your prompt and produces an answer; this stage determines how fast, accurate, and costly the model’s output is. Investors care because inference affects product performance, user experience, cloud and hardware costs, and regulatory or security risks tied to how models are deployed and scaled.

time to first token (ttft) technical

"deliver up to 20x faster Time to First Token (TTFT)"

The elapsed time between sending a request to a generative AI model and the model producing its first output token; a latency measure that captures how quickly a system begins responding. It differs from total response time or throughput because it stops when the first token appears rather than when the entire answer is complete, and it can be affected by model architecture, server processing, network delay, prompt handling, and whether the model streams outputs.

deduplication technical

"that traditional deduplication misses"

Deduplication is the process of identifying and removing duplicate records or entries so a dataset or report contains only one accurate version of each item. Think of it like cleaning a contact list that has the same person saved multiple times — it makes the information clearer and smaller. For investors, deduplication matters because it improves the accuracy of financial data, prevents double-counting of revenue or risks, and leads to more reliable analysis and decisions.

AI-generated analysis. How Rhea-AI works. Not financial advice.

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Platform innovations give enterprises governed access to their data and predictable AI costs as they move AI from pilot to production.

LONDON, Sept. 30, 2026 /PRNewswire/ -- Everpure (NYSE: P), the company revolutionizing storage and data management, today 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. Today's 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.

"Enterprise AI is hitting a wall not because the models are lacking, but because data is not ready for real-time, autonomous agents," said Prakash Darji, General Manager, Data & Digital Experience at Everpure. "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."

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:

• Native MCP Integration: Implements the open Model Context Protocol (MCP) so AI agents and security tools can query live data catalogs 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-Gen Efficiency, & 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 Optimization Reference Architecture: Given the need for enterprises to own their own data and optimize 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. The new capabilities announced today will be available this October.

Additional Resources

• Executive Perspective: From Data Processing to Data Primacy: The Agentic Inversion of Work and IT Architecture
• Press Release: Everpure Unveils Data-Primacy Architecture for the AI Era (June 2026)
• Blog: Your AI Strategy Is Stuck Behind Your Application-Centric Architecture

About Everpure

Everpure (NYSE: P) allows organizations to take control of their data with an industry-leading, ever-evolving storage and data management platform. We help companies unleash the power of their data by ensuring it is accessible, intelligent, and ready to perform in the AI era. We make data management effortless while simultaneously scaling performance and significantly reducing energy consumption. With one of the highest Net Promoter Scores for over a decade, Everpure is the choice of the world's most innovative organizations. For more information, visit www.everpuredata.com.

This press release contains forward-looking statements regarding Everpure's business and product roadmap. The timing, development, and release of any Everpure product, feature, or functionality described remain at Everpure's sole discretion. The material provided is for informational purposes only and is not a commitment, promise, or legal obligation to deliver any material, code, or functionality. It should not be relied upon in making purchasing decisions, nor incorporated into any contract. Discussed performance metrics are informational and not a promise of performance; results may differ materially from those provided herein based on variances in deployed environmental and dataset conditions.

 

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SOURCE Everpure