Everpure’s Nathan Hall on a new brand identity and the AI-first road ahead

Pure Storage has a new name and a bigger pitch. The company has rebranded as Everpure, positioning the shift as a move from reshaping storage to defining the future of data management. It has also signed a definitive agreement to acquire 1touch, a data intelligence and orchestration player that aims to give enterprises a unified view of their information, and help make data more secure, accessible, intelligent, and ready to perform.

But the larger story is not the name change or the deal in isolation. It is what sits underneath both: AI has pushed enterprise infrastructure into an inflection point, exposing the limits of siloed data, manual processes, and rigid architectures that cannot keep up with the speed, scale, and intelligence demands of modern AI. In this conversation, Nathan Hall, VP and General Manager, Asia-Pacific and Japan, Everpure, talks about what it takes to manage perpetually evolving storage, the need for a holistic data management platform, and Everpure’s play. He also underscores the need for data to be continuously protected, instantly available, and enriched with context, so it can be trusted and used, not just stored.

So what is the thinking behind the new name “Everpure”? What philosophy sits behind that brand change?

The name “Everpure” combines our two strongest and most familiar brand ideas: Pure and Evergreen (the company’s subscription-based storage as a service). It reflects the idea of continuous improvement, both in how we innovate as a company and in how customers experience our technology. With Evergreen, customers do not face disruptive upgrades or forklift migrations. The platform keeps improving over time, non-disruptively.

That idea is a big part of who we are. We wanted the new name to reflect that continuity and simplicity, while also giving us room to grow beyond a hardware-only perception. So this is really a case of the brand catching up with where the company and the technology already are. The strategy, vision, standards, and innovation roadmap remain the same. This is about making sure the market understands us in the way we already operate.

Also read: Pure Storage rebrands as Everpure and plans to acquire 1touch

AI is changing everything. From Everpure’s vantage point, what is changing most for enterprises in data infrastructure and strategy?

AI is changing how customers think not just about infrastructure, but about data itself.  A lot of the old problems enterprises had are still there, and in many cases AI has made them worse.

The classic ones are data silos, too many copies of data, complex storage environments, and very labour-intensive management. These environments are often hard to operate, and that increases the chances of human error. In fact, storage outages are still one of the top reasons for application outages.

Then you add today’s realities: cyber risk, compliance pressure, data sovereignty, and rising costs. Space and power are major concerns, especially across Asia Pacific, where density is increasing and infrastructure expansion is under pressure.

AI amplifies all of this. It is extremely power hungry, it creates even more data sprawl, and now the business wants data to be ready for AI agents. That means organisations need to know what data they have, whether it is current, whether it is secure, and whether it can be trusted. Uptime also becomes critical because if the data is not available, AI cannot use it.

So this is no longer just a storage issue. It is really a data management and data context issue. The context, lineage, and relationships around data are now central to AI success.

If we look at the evolution from DAS, NAS and SAN to software-defined storage, AI now seems to be forcing another shift. How is Everpure playing out in this AI-driven transformation, especially for enterprises with a lot of legacy systems?

There are two dimensions to that. The first is how we use AI ourselves to improve the experience of managing infrastructure. We have used AI for years to optimise efficiency and uptime across our platforms. We are also making it easier for customers to interact with storage in a natural language way, through a Copilot-style experience, so they can describe the outcomes they want instead of clicking through complex interfaces.

The second dimension is more strategic. A lot of the work we have already done, like moving our platforms to a single operating system with Purity and creating a unified storage cloud with Fusion, actually puts us in a strong position for AI.

The biggest AI challenge for most enterprises is not just compute. It is data fragmentation. Data sits in different silos, across different tiers, and in different environments. And with AI, you often do not know in advance which data will become important, so it all needs to be accessible quickly.

The other big challenge is preparing data in time for AI. If you spend too long copying and transforming data, your expensive compute sits idle. That is a major cost issue. GPUs are expensive, and so are data scientists. If both are waiting while data is being consolidated and transformed, that becomes a very expensive delay.

So we see our role as solving both sides: unifying access to fragmented data and reducing the time it takes to make that data usable for AI.

You mentioned Everpure is moving beyond the traditional “storage company” . What is driving that shift, and why now?

The reality is that our innovation has moved well beyond storage hardware for a long time, but our brand was still anchoring us to that original category.

These are strategic priorities. Our intent is to root the conversation in our combined strengths across storage, software and data management, so leadership teams see the full scope of what we can help deliver. We continue to serve day-to-day operational needs, while also supporting larger transformation goals.

At the same time, we did not want to leave behind the strongest elements of what customers already know us for. Two things matter deeply there: “Pure” and “Evergreen.” “Pure” is already how many customers refer to us. And “Evergreen” is one of the strongest ideas we brought to the market: the idea that customers should not have to buy hardware, replace it every three to five years, go through a forklift migration, take outages, and repeat that cycle forever. That is what we wanted to preserve and elevate.

When you assess enterprise AI readiness, what are the top data gaps you see most often, and what should CIOs do first without a massive replatforming exercise?

The first and biggest issue is data fragmentation. Most enterprises, especially older ones, have built up a very fragmented environment over time. That happens across applications, across storage vendors, and even within a single vendor, because many vendors have multiple storage products running on different operating systems.

So the first step is not to try and solve everything overnight. The first step is to create a clear data strategy and stop the problem from getting worse.

I often describe this as “stop the bleeding.” Build a unified enterprise data cloud approach so that new data starts landing in a single policy-driven environment. If you do that, then over time, as older applications are retired and new ones come in, the fragmentation naturally starts to reduce. Most enterprises will not wait passively, though. If they want to move faster with AI, they will identify their most valuable applications and data sets and begin consolidating those first.

The second challenge is making data usable for AI quickly. Data is created in many formats and systems, so organisations need a way to convert it into something AI can use with much less delay. Traditionally, this has meant long ETL ( extract, transform, load) chains, which are slow, expensive, and labour-intensive. The direction we are taking is to make that process far more real-time, by adding context and metadata much earlier so the data becomes usable much faster.

So the two big steps are:

• Unify and govern data to reduce fragmentation.
• Accelerate data preparation so it becomes AI-ready faster

From an APJ lens, and especially India, where do you see the biggest growth and opportunity right now?

India is absolutely central to our APJ strategy. It is one of the fastest-growing markets for us across IT spend, storage spend, and storage as a service. In fact, it was the last place I visited before coming to the US for our sales kickoff, because I spend a lot of time there personally.

What stands out about India is both the growth and the scale. You have banks with hundreds of millions of customers, and telecom operators with hundreds of millions of subscribers. That level of scale is rare globally, and it is still growing.

That means India is generating enormous volumes of data, and data is now the key input to AI. So I see a very strong runway for AI investment in India. You can already see this in government initiatives, telecoms, and large industries. That combination makes India one of the most important growth markets for us in the region.