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Build the data infrastructure AI depends on
The business wants AI everywhere. Your infrastructure decides how fast it gets there.
What’s actually in the way right now?
60%
of organizations aren’t sure their data management is ready for AI.
94%
say data quality is the primary factor in whether an AI project succeeds.
77%
agree that AI initiatives are placing additional pressure on their storage capacity and budget.
Enterprise AI doesn’t stall because the models are wrong
White Paper
Is Your Data Platform AI-Ready?
Move beyond system-by-system storage to one platform built for AI from the start.
Executive Perspectives
Putting Data First
AI has transformed data relationships, and to meet this moment, enterprise IT must rethink years of assumptions.
Analyst Report
Navigating vendor change: A CIO perspective
Learn real-world lessons from leaders who successfully drove change.
Guide
Technical Leader's Guide to Evaluating AI Data Platforms
Five criteria that separate storage vendors from true data platforms.
Unify, govern, and understand your data across every environment
Unify
All your data, finally in one place
Every team and AI project works from the same data, instead of its own copy in its own silo.
Govern
Operations that run themselves
Your team stops babysitting infrastructure and spends its time on work the business actually notices.
Understand
Know what you have and which of it matters
See what data you hold, where it lives, and how sensitive it is, then build resilience and compliance to match.
Manage your data, not your storage
Unified Data Plane
A single cloud of data that spans on-prem, hybrid, and public cloud locations, giving IT a consistent data environment.
Intelligent Control Plane
Global, intent-driven policies automatically control placement, protection, and governance across your estate.
Delivered as a service
A storage-as-a-service model that delivers consistent availability and performance everywhere, backed by guarantees.
What AI at scale needs from your infrastructure
Requirement One
Room to grow without disruption
Take on more data and more AI projects without pausing the business to make room.
Requirement Two
Uptime the business can count on
Once AI is part of daily operations, it can't wait on maintenance windows. Upgrades happen while it runs.
Requirement Three
Costs you can plan around as you grow
Spend that tracks what you use, instead of a surprise hardware bill every time AI demand jumps.
Ready to check where you stand?
The EDC Success Blueprint shows where the gaps sit in your environment.
Don’t just take our word for it
Ford Motor Company
“At the end of the day, what we are talking about is enablement of developers and velocity of delivering services at a particular price point and at a particular quality… There's hardly anything we can do in Kubernetes without having persistent storage and that is what has brought us together with Portworx.”
See how this connects to other solutions
Ready to see what a simpler platform does?
What is AI data readiness?
AI data readiness is the state where an organization’s data can be used by AI applications without manual cleanup. It requires three conditions across all environments: data is stored consistently, governed by policy that applies wherever the data moves, and understood by the systems that use it.
Is AI data readiness the same as AI-ready infrastructure?
No. AI-ready infrastructure refers to compute, networking, and storage with enough performance and capacity to run AI workloads. AI data readiness refers to the data itself: whether it’s stored consistently, governed, and understood. Organizations need both. Fast infrastructure doesn’t fix fragmented or poorly governed data.
What is an Enterprise Data Cloud?
The Enterprise Data Cloud (EDC) is Everpure architecture for managing storage across on-premises data centers and public clouds as one environment. Administrators set policies for performance, protection, and access once, and those policies apply across every location. It reduces data silos and manual work per system.
How do I find out whether my data is AI-ready?
Check three things across every environment where your data lives. Is storage managed the same way in each location? Are access and retention policies enforced without manual steps? Is the data labeled so applications know what it is? A no on any of these is the gap to fix first.
Why do AI projects fail at the data layer rather than the model layer?
AI pilots usually run on a small, cleaned dataset. In production, models pull from data spread across many systems, with duplicate copies, inconsistent policies, and missing labels. The model works, but incomplete or outdated inputs make its answers wrong. Swapping models doesn’t fix that.
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