AvePoint, Inc. Class A Common Stock Oppenheimer 29th Annual Technology, Internet & Communications Conference
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Hi. Good morning, everyone, and thanks for joining us for Oppenheimer's 29th Annual Technology Conference. We have with us today AvePoint CEO, TJ, CFO, Jim Caci, and Jamie Arestia from Investor Relations. Gentlemen, thank you so much for joining us today. Before we begin, for the audience, we do have a question bar for you that you can send questions, or separately, you can email me at param.singh@oppco.com, and I can ask the question on your behalf. First, thanks again, TJ, Jim, for joining us. I wanted to thank you again for taking the time today.
Again, very high level to begin for some of the audience here, who may not be as familiar with AvePoint, if we could start with where you participate in the market, what are some of the niche areas where you are advantaged, and give a lay of the land for the audience here.
Good morning, Param. Thank you for having us. Yeah. AvePoint, we are an industry leader in cloud data management, governance, and security, where we play as really helping enterprises curate and govern their unstructured data, which includes all emails, chats, files, contracts, and what have you. We have been doing this for 20 plus years, and we have done this very successfully globally in the large enterprise public sector, regulated industry, as well as now SMB. Of course, in the AI era, folks very quickly realize that to have good, high-quality AI deployments, you need to also have high-quality data that AI grounds on. We have extended since last few years into the space of AI governance, agentic asset governance, and cost control, and discovery.
This is now the really massive tailwind for us as we lean into this narrative of being the AI trust layer for our customers and partners. We are the layer between the model and the data. As everyone know, the commercial large language models, frontier AI companies, their models are trained on publicly available data. But for companies, for them to leverage AI to be productive for their business, they need to have AI refined on their own corporate decades worth of corporate industry data. That is context aware. That is also dynamic. In this framing, increasingly the ability to be able to manage both the foundational data layer as well as the AI agentic layer to work in cohesion with a right dataset, with a time sensitivity, with a right access control, lifecycle management, permission control, and also, very importantly now, also cost visibility.
It's what allow our partners and customers globally to have comfort and confidence in their AI deployment. The two top of mind issues we help companies, large and small address, one is AI risk and AI cost. That boils down to fundamentally what we do. We are the largest such cloud player, specializing in Microsoft Cloud ecosystem. But we have extended beyond that because customer is multi-cloud. We now also cover Google, cover Salesforce, and Atlassian, and a number of other ecosystems as well.
Fantastic. There's a lot of different topics I want to dive into, but maybe first, the most intriguing one for everybody in the audience and myself has been AI and how different vendors are participating in it. TJ, you touched upon that a little bit, that you're adding the governance and the compliance layer to the agent side, the AI side. Maybe we can delve into a little that, especially with your new product, AgentPulse.
Yes. How are you specifically addressing that market?
Because it's so nascent. How do you get confidence around what the agents are doing? What are they doing from a rules and regulated perspective? Or maybe some of the advantages that AvePoint brings as an organization that would allow people to use your product versus somebody else for monitoring and governing, let's say, agents here.
Right. That's a great question. For a decade plus, our expertise has been helping companies manage the risks and exposures of employees working on corporate data. What data the employees have access to internally, externally, how long those data should reside, should live for, and how do these data get ingested into the corporate system, and how do they get retired? Whether it's archived out, record managed out, tier storage out, or completely purged. In the age of AI agents are no different than employees in the sense that they have access to systems, process data, make decisions. Except now AI is operating at machine speed, so everything is accelerated. We already have this governance framework for quite a number of years in the cloud to manage large data estates. We're talking about petabytes of data, and for our customers around the world.
When Microsoft first introduced Power Platform, Power Apps, we did the same thing. We just elevated our delegated governance framework to include Power Platform and Power Apps. This delegated model means IT doesn't really understand what data and what business applications are doing for the business users. Our framing allows the business user to provide context richness to that. This delegated governance framework, compliance framework, then implement and actuate the corporate policy. In the age of agentic AI, it's the same thing. Basically, business user can provide that context and richness of what this agent have access to. If you vibe code an AI agent, by default, that agent inherits your permission structure. But that may not necessarily be the actual intended outcome.
We have the governance framework to allow users to actually provide that context so that our software can then start to track those agents, just like any other semi-autonomous or fully autonomous computer applications, their access rights, their data rights, and also, of course, their lifecycle, and then monitor their cost. We help enterprises go out and discover all the agents running in cloud, whether it's Microsoft or Google. Very shortly, we're extending onto the devices, so we can actually discover open cloud agents that's running there as well. Today, of course, we support Microsoft Copilot, Google Gemini, and then also Anthropic Claude, and ChatGPT, those type of AI services and agents, and then bring them under control. This is something we've been doing for a long time. It just really, in the agentic era, we then extend to the agents.
Again, no different than previously how we govern employee access, except now everything's just operating at machine speed.
That's great. And that's one point, TJ, I kind of wanted to delve into. I think people kind of forget that there is some inherent advantage of taking the skill set from dealing with human-based governance and compliance to machine governance and compliance. And of course, everything is at machine speed, which means it's exponentially higher. But That's right. Maybe for my edification, too, if you can kind of talk about what are some of the inherent advantages in practical terms that you are leveraging from your existing expertise, one.
Then two, when you're dealing with things at a machine speed, what are some of the newer items you had to introduce to be able to handle that, both from a monitoring perspective, but also from a response perspective? Because you also need to kind of manage much larger databases, have the correct, accurate information, and kind of stop things at machine speed as well. So maybe some clarity that would certainly be better for my edification here.
Yeah. That's a great question, Param. Firstly, the benefit that we have the right to own the space is that, again, we've been in this space for a long time, and our software are certified by some of the most rigorous security agencies, FedRAMP certification, U.S., ISMAP in Japan, which is like 3,000 security check certifications. So we are running government data centers, already in commercial data centers. So that fully vetted compliance and regulation and scaling and that capability around cloud security, cloud app, has already been vetted out for the last 15 plus years for some of the most rigorous commercial and public sector enterprises, including the biggest banks on Wall Street. So that's a massive basically credit and trust that's already there in a highly scalable software.
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