Everpure has introduced new platform capabilities to simplify enterprise data management and remove barriers to scaling AI, including fragmented data context, complex deployments, and secure data access for AI agents.
“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 organisations the foundation to move AI out of the lab and into production with the necessary confidence.”
Everpure Data Intelligence
Everpure Data Intelligence enables organisations to discover, classify, and contextualise enterprise information at its source, across the Everpure Platform, public clouds, SaaS applications, and third-party storage. These new capabilities give autonomous agents and administrators direct, secure access to live enterprise context without custom API development.
Key capabilities include:
- Native MCP Integration: Implements the open Model Context Protocol (MCP), allowing AI agents and security tools to query live data catalogues using natural language. Agents can identify relevant data and understand its sensitivity classification as an input for AI, agent workflows and analytics.
- Turn-Key Deployment: Streamlines deployment through the existing Pure1 console, reducing the need for complex professional services engagements and separate management servers.
- Privacy-First File Intelligence: Identifies who can access each file share and assesses data staleness without reading file contents, enabling organisations to detect exposure risks and reclaim capacity before making data available to AI agents.
Everpure also introduced features that bring AI execution closer to enterprise data at its source, reducing the need to move data from its system of record. These include PureKVA (Key-Value Accelerator) for faster LLM inference, DeepReduce Data Compression for ongoing data reduction, and a Token Optimisation Reference Architecture to enhance AI workload efficiency.
