Everpure has announced the availability of Everpure Data Stream, a new platform component based on the NVIDIA AI Data Platform reference design. It brings AI processing closer to enterprise data while addressing common challenges related to data preparation, governance, and scalability. The release expands the company’s broader strategy of delivering AI-ready data infrastructure for enterprise environments.
As organizations move from AI experimentation to production deployments, many face obstacles related to ingesting and preparing enterprise data, enforcing security and governance policies, and scaling infrastructure to support growing AI workloads. Everpure says Data Stream reduces data preparation timelines from months to minutes while maintaining stream-level access controls that keep data within enterprise boundaries. Its scale-out architecture also allows storage and compute resources to scale independently as AI requirements evolve.
According to Everpure CTO Robert Lee, organizations building AI platforms require flexible architectures that can support both rapid deployment and long-term scaling. He noted that enterprises need secure, high-performance data pipelines that accelerate data processing and reduce time-to-results.
Connecting Data Readiness to Production AI
Everpure positions Data Stream as part of a broader end-to-end AI data platform focused on preparing enterprise information for AI use. The company argues that AI-ready data requires classification, contextualization, governance, security, and scalable access before it can be effectively used for training, inference, or agentic AI applications.
A key component of this strategy is Everpure Data Intelligence, formerly known as 1touch. The platform discovers, classifies, and contextualizes enterprise data across SaaS applications, cloud services, on-premises infrastructure, and mainframe environments. It maps relationships between datasets into a data relationship graph, creating a metadata layer accessible via APIs and the Model Context Protocol (MCP).
The platform also applies attribute-based access controls and governance policies, enabling enterprises to maintain security and compliance requirements as AI models and agents interact directly with business data.
GPU-Accelerated Data Processing
Data Stream is built on the NVIDIA AI Data Platform reference architecture and is designed to simplify the conversion of unstructured enterprise data into AI-ready information. Rather than relying on manual ingestion and data preparation processes, the platform uses a GPU-accelerated pipeline spanning data ingestion through inference.
The goal is to reduce operational complexity while improving the speed at which organizations can deploy AI services and generate actionable results.
NVIDIA Vice President of Storage Technology Jason Hardy said modern AI infrastructure requires architectures that connect secure, governed enterprise data with accelerated computing resources. He noted that Everpure’s integration with the NVIDIA AI Data Platform is intended to help organizations move AI initiatives from proof-of-concept stages into production deployments.
Everpure also disclosed ongoing work on next-generation AI-native storage technologies based on NVIDIA Vera and the NVIDIA BlueField-4 STX storage processor. The effort is focused on bringing acceleration, security, and intelligent data services closer to enterprise datasets as agentic AI deployments continue to expand.
Scaling AI Infrastructure
To address storage bottlenecks that can limit AI training and inference performance, Everpure highlighted FlashBlade as the storage foundation for Data Stream deployments. The platform delivers low-latency data access and incorporates KV Cache Accelerator technology to improve memory efficiency during inference workloads.
Everpure’s Evergreen architecture allows organizations to scale from FlashBlade//S systems to FlashBlade//EXA deployments without disruptive migrations, supporting growth from smaller AI projects to large-scale AI factory environments. Portworx provides the container platform layer for deploying and managing AI pipelines across edge, core, and data center environments.
By combining data intelligence, data streaming, storage, and container orchestration within a unified architecture, Everpure aims to reduce infrastructure fragmentation and eliminate the need for separate AI data silos.
The announcement aligns with findings from a recent IDC Global AI Readiness Survey commissioned by Everpure, which reported that 94% of IT leaders view data quality as the primary factor influencing AI success. Everpure positions its integrated approach as a way for enterprises to maintain flexibility while adapting to rapidly changing AI requirements.
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