Everpure Investor update

Hello, everyone. Welcome to the Everpure Financial Analyst Meeting, held for the very first time here at our headquarters in Santa Clara. We have a full agenda for you today. We will start off this morning with technology deep dives on data management and AI. This will be followed by a technology-focused Q&A session. We will then break for lunch and resume again at 12:00 P.M. with the main session featuring Charlie, Tarek, and Rob. For anyone needing information on things like restrooms or Wi-Fi, we do have people in the hallway who can help you with any questions. When we return from lunch, Charlie will lay the groundwork with an overview of our strategy and our expanding opportunity. Actually, our significantly expanding opportunity. Rob will cover our fundamental advantages and our right to win in four high-growth market areas. These include our core plus three additional high-growth market areas.

Tarek will conclude with our foundation for future growth and our long-term financial framework. Following that, we will conduct a final Q&A session with Charlie, Rob, Kaz, Tarek, Bill, and Prakash. After the final Q&A session, for those of you here in person, we will have an informal cocktail hour. Before we get started, I will remind you, as I do every year at this event, that the value of everything that we do at Everpure is driven by our software. This includes our core business, as well as our three additional high-growth market areas. For all of our high-growth market areas, once again, it is always about?

The software. Okay, now I'll turn it over to Charlie for his opening welcome.

Good morning, everyone, and welcome. For those of you who are here in the immediate audience, thank you so much for coming out. We know that it's a long trip for most of you and many of you, and we really do appreciate your physical presence here and also want to thank everyone who's online who will be going through our discussion today. We're very excited. The team here is quite excited to speak to you about everything that we're doing. A lot is changing. Obviously, a lot is changing in the outside world, a lot is changing in our industry, but a lot is changing at Everpure as well, and it's a great time for us to be able to express this to you in many ways. One is we believe that our business is accelerating.

We hope to be able to show that to you today in numbers. It's also expanding into a number of areas, and that's obviously what Paul alluded to, and as you can see from the agenda, different areas that we'll be providing you a lot of background in. One additional thing, for those of you in the audience that is here physically, we left you with a paper on the table. It is a bit of a long read. It's about a dozen pages. But we think it's a seminal view of how IT architectures are going to fundamentally change over the next decade. We believe that this, brought on by AI, is driving the need for organizations to start to organize around data rather than around applications. That's a very fundamental change in how IT architectures are built.

That fundamental change really is going to open up very new opportunities that we, as Everpure, are pursuing, and other vendors are going in that direction as well. But unless you understand where we are today and where we're going as an industry, how that's changing and moving towards a different architecture and a different relationship between applications and data, you won't fully be able to predict all the changes that take place. So I'd highly recommend that in your leisure time, you scan that document and get a good understanding of it. With that, I'm going to invite our first speaker on the stage, Prakash Darji.

All right. Thank you. All right. I am going to double-click on the first area Charlie mentioned, which is this idea of data centricity. We call this data primacy. It is an inversion of the application architecture. As we get into our business, we are really talking about how we can help customers maximize the value of data wherever it sits. Pure, non-Pure. We are getting into the focus of managing the value of the data and getting the most leverage out of it. Two, why we believe we can do so is we already have the trust of lots of mission-critical data across a customer's landscape, both structured and augmented with unstructured as well. The third thesis we are going to get into here is why a storage company has to do that.

Because as you think about data, why does context need to be both traveling with the data and be persistent in storage? If you think about the biggest data management challenge today, and I bring this back to, prior to here, I worked at SAP 13 years building applications, both financial and database. A large portion of the world today is customers buy applications, and IT departments integrate the data between those applications. If you think about the total spend, actually 80%-90% of all of the spend goes into the integration work between all the assets you have. It just compounds the bigger you get. Largely, the data integration costs are largely the biggest cost associated with running an enterprise today.

If you think about agents and agents' access to data, where it sits, where context is trapped in applications or in lake houses, et cetera, that actually is going to blow open and be more exponential in terms of integration cost. These silos of information that sit in applications, warehouses, et cetera, need shared context to do anything with AI. Agents work better directly on data. That is a challenge we are seeing in the industry today.

Now, if people are running applications, you will see SAP strategy saying, "Hey, you want to approve sales orders, so bring all your Salesforce data into SAP." Salesforce is saying, "Hey, you should use Agentforce and bring all your SAP data into Salesforce." Databricks is saying, "Okay, bring all your data into Databricks." Everyone right now has a part of the context picture, and this is what leads to the fragmented context across the application. Our first approach is how do you create shared context with data wherever it sits? Leverage the data in place and understand how to build shared context. Two, instead of moving the data between systems, how do you allow reusing the data where it sits? What that does with the integration complexity is it inverts the way you develop apps. Historically, your data has been owned and encapsulated by the application.

This is my finance data sitting for my finance application. This is my sales data sitting in my sales application. We do see a world in data primacy where application workflows plug and play and reuse data across multiple apps. It is a different way of building and architecting. In the early days of SAP, when I was there, we were trying to build a big schema to handle what we would consider end-to-end ERP. Largely that became siloed and fragmented as people started buying different applications and not consolidating to a single referential data model. In the world of AI, we do believe that this idea of plug-and-play workflows will allow you to reuse data where it sits. When you hire a new employee, you don't need to copy all your financial cost center data, you can just update your financial costs.

The separation between HR and finance should not be as wide as it is today. Let's get into it. Data primacy. If you take this approach, it reduces data integration, reduces your data copy, increases the data coherence, meaning the meaning of your data across your landscape. Obviously, less is always better in a cyberattack surface. By doing so, you can also optimize your token spend, meaning the inputs you have that are relevant to models, instead of building a large language model factory, an SLM with the right data and the right context will get you better results at lower cost. We've seen that over and over again, and that's how agents are being built. They're being purpose-built with targeted data. Our beliefs as a vendor. First, we believe data will be distributed. There won't be one thing creating data on the planet.