How Everpure plans to stop AI from starving without data

Imagine you are an AI Agent.

You execute inside a $20-40 million-plus Nvidia SuperPOD system’s accelerator hardware, have a skill set and can call up other agents to do your bidding.

An insurance company customer, using a PC browser or smart phone, asks a question: “Am I covered for weather-related damage under my current policy?” and it is handed off to an AI agent.

What happens next? The agent itself runs on clusters of CPU + GPU/accelerator servers that host the large language model, any Retrieval-Augmented Generation (RAG) components, orchestration logic, and tools that look up the actual policy data.

Those same servers (or tightly coupled backend systems) access the policy database/storage to answer the weather insurance coverage question, then return the response to the user’s device.

Everything the agent does depends upon data, and it and all the other agents operating at the same time in the SuperPOD system, need to get that data off a storage system.

A national or large regional-level insurance company will have petabytes, even exabytes, of data it stores so it can manage its insurance business.

There needs to be a central index of this data’s structure, state, location, field names, types and semantics so that AI agents go to one place, a kind of data encyclopedia, to find out what information they need and where it is located.

Agents are in a hurry, because GPU servers are hugely expensive and just a minute of idle time can cost $25 or more, a dollar every 2.4 seconds.

The agent answering this insurance request needs data and a storage system needs to deliver it as fast as possible so that the GPU servers don’t wait and wait and wait some more for data.

This requires the storage system to be able to handle the scale and types of the data involved, for it to be self-describing, and its software to be tightly integrated with Nvidia’s GPU hardware and software server architecture so that there isn’t a wasted millisecond while the GPUs are starved.

A modern approach to data processing for AI Par Botes, Everpure’s VP of AI Infrastructure, discussed the difference between traditional enterprise critical data processing and how it’s done in the AI era: “The characteristics of performance, resilience, availability and governance don't fundamentally change, but access patterns are different.

When searching for information, metadata can become richer than the data itself because it explains the semantics of data, and how it behaves as it becomes enriched.” Building an AI data architecture around standard dual-controller arrays or siloed direct-attached storage (DAS) quickly hits physical limits.

What organizations need is an AI Data Platform, built using the NVIDIA AI Data Platform reference design.