Everpure Data Stream: A Faster Path to AI-Ready Data

Before I joined Everpure, I spent over seven years with customers trying to gain insights and value from their data. When GenAI came on the scene in 2021, we were all filled with hope and excitement that gleaning value from data would now be so much easier. But while AI has made getting “results” faster and easier, it has also shined a huge spotlight on the one weakness that nearly every organization has—data readiness. Or rather, “AI readiness.” 

Every customer conversation I’ve had over the last 10 months since joining Everpure has further reinforced these challenges as AI adoption moves beyond experimentation and into production. Boards are demanding faster AI outcomes. Budgets for AI projects are being approved at scale. And teams are still stuck—not because they lack the compute capacity or infrastructure, but because the data underneath isn’t ready for AI. Our customers are asking for help to be AI-ready.  

I realized that we have a significant advantage at Everpure. Trusted by more than 14,500 customers to manage their most sensitive and demanding data workloads, we’re uniquely positioned to deliver secure, governed AI at enterprise scale.  

The bottleneck stalling AI? It’s data.

Earlier this year, we commissioned IDC to survey more than 1,300 IT leaders across the US, UK, Germany, France, Japan, Australia, and New Zealand on enterprise AI deployments. The findings below should give every CIO pause:

• An overwhelming 86% say their storage is not working very well for agentic AI.
• More than 60% say their data infrastructure needs improvement or a refresh.
• Nearly 40% name data readiness as the #1 barrier to agentic AI adoption after security.
• One in three say their data scientists spend more than half their time waiting on IT instead of building models.

Bring those together, and the conclusion is unavoidable. The constraint on enterprise AI is not models. It’s not tooling. It’s not compute. It’s the data underneath all of it. 

What ‘AI-ready data’ actually means

If data is the bottleneck, the right next question is: What does AI-ready data actually look like? At Everpure, we measure it against four criteria:

• Trustworthy: It’s curated and clean. Duplicates, boilerplate, corrupted files, and sensitive content are filtered out before they’re ever processed, let alone reach a customer. Safety labels are applied at the source.
• Governed: It’s classified and access-controlled, so permissions match the people behind them. An analyst can read a document; that doesn’t mean an AI agent should surface it to everyone in the company.
• Traceable: It’s indexed, vectorized, and converted into embeddings AI can retrieve and reason over. Until this step happens, your data doesn’t exist from your AI’s perspective.
• Findable: It’s reachable from your AI applications through APIs and MCP tools, searchable, and integrated into the systems your teams already use.

We designed Everpure Data Stream to ensure your data can meet all four.

Built with NVIDIA, on the data you already own

We didn’t get here alone. For the last year, Everpure and NVIDIA have been building this together.

NVIDIA designed the AI Data Platform—a reference design for exactly how enterprise data gets refined, understood, and made ready for AI. Think of it as the blueprint for the refinery: a way to bring NVIDIA’s accelerated compute and AI software stack to the data where it already lives, so agents can query real enterprise knowledge in real time, governed and trusted, without ripping out the infrastructure you already run.

Everpure Data Stream extends upon the NVIDIA AI Data Platform reference design. NVIDIA Blackwell GPUs. NVIDIA Spectrum-X networking. NVIDIA NIM microservices. FlashBlade® as the high-performance storage foundation. A certified, full-stack solution—delivered as a complete integrated solution, or available as software to the infrastructure you already have.

Introducing Everpure Data Stream

What it is

Everpure Data Stream is an AI-centric application that takes raw, unstructured data—PDFs, DOCX, and images, for example—and turns it into AI-ready, governed intelligence any application can call in a fraction of the time versus current methods. It runs on infrastructure you own. Your content never leaves your environment. And it collapses months of pipeline work into a packaged service your MLOps team doesn’t have to build or maintain. This provides a uniform and consistent way for data leaders to produce trusted and governed data for AI.

How it works

Today, an enterprise team typically stitches together a seven-stage AI data pipeline—ingest, curate, transform, vectorize, index, retrieve, and generate—using a different vendor or open source project at every stage. The honest numbers we hear from customers: two to four months of pipeline work before AI can read a single document. Multiply that by every new use case, every model upgrade, every new data source. An organization simply can’t respond to rapid market demands if they have to wait months for an answer. 

Everpure Data Stream collapses the full pipeline into one automated, continuous, and governed product:

• Ingest content from your existing NFS and S3 stores.
• Curate by removing duplicates, boilerplate, and corrupted files.
• Classify for sensitivity and apply access controls at the source.
• Embed content into vectors AI can reason against.
• Index for fast retrieval.
• Retrieve through semantic and hybrid search.
• Generate answers your AI applications can act on—all exposed using industry-standard APIs.

It runs continuously, in place, on infrastructure you own. No copies. No new silo. No content leaving your environment. 

Why it’s different

Three things separate Everpure Data Stream from the patchwork DIY pipelines and bolt-on point solutions in the market today:

• Cost, control, and security. Everpure Data Stream runs on a fraction of the GPU footprint as other AI Data Platform solutions, and it’s built on infrastructure you own. Content stays inside your environment, governed by the security model you already trust.
• Time to value. What used to take a specialized internal team months of stitching now arrives as a packaged service. Your ML platform team gets their time back—for higher-value work.
• Speed and scale. Built on FlashBlade for the throughput and concurrency AI demands, with the headroom to grow from gigabytes to exabytes.

Why does this matter? It matters because every agentic application that enterprises want to deploy in production needs fast, trusted access to data.  Each of those apps needs a way to search for and access data securely and accurately.  Data Stream has automated the complex RAG pipeline and offered this capability along with semantic search as APIs so that agentic applications can be deployed with greater accuracy, trust and speed.  That new fraud detection app will be more accurate.  That new real-time co-pilot for supply chain and logistics will be more complete.  This means production ready AI outcomes is now much easier and faster to come by. We’re already seeing this validated with early feedback. Beta customers will use Everpure Data Stream to ask natural-language questions across decades of files to retrieve the right documents in seconds. Even those who have built their own RAG pipelines have stated they’re looking forward to handing off the support and future development of automated data pipelines for AI.

Get started at pure.ai

This is the moment we’ve been building toward. AI runs on data. Data runs on Everpure.

If you’re ready to see what Everpure Data Stream can do with the data you already have, sign up to try it. From there, we’ll work with you to assess where your infrastructure stands today, identify the use cases that will drive the most value for your business, and map a realistic path from pilot to production.

For 15+ years, we’ve provided best-in-class data infrastructure to over 14,500 customers. With Everpure Data Stream, we’re turning that infrastructure into one of the fastest paths to AI-ready data—today.