Pivotree Inc.

Good morning, everyone, and welcome to the Pivotree third quarter 2025 earnings call. All participants are currently in listen-only mode. Following the presentation, we will open the line for a question and answer session for analysts. To ask a question, we would ask the analyst to click the icon to raise their hand. Before we begin, Pivotree would like to remind listeners that certain information discussed today may be forward-looking in nature. Such information reflects the company's current views with respect to future events. Any such information is subject to risks, uncertainties, and assumptions that could cause actual results to differ materially from those projected in the forward-looking statements. For more information on the risks, uncertainties, and assumptions relating to the forward-looking statements, please refer to Pivotry's public filings, which are available on CDAR. During the call, we will reference certain non-IFRS measures. Although we believe these measures provide useful supplemental information about our financial performance, they're not recognized measures and do not have standardized meetings under IFRS. Please see our MD&A for additional information regarding our non-IFRS measures, including for reconciliations to the nearest IFRS measures. Now I'd like to pass the call over to PIDGRI's CEO, Bill DiNardo. Bill?

Thank you, Peter. Good morning, everyone. Thanks for joining us on our third quarter 2025 conference call. With me today, as always, is Mo Ashour, our Chief Financial Officer. As we normally do each quarter, we already published a CEO letter in conjunction with our earnings results. It's available on our website, filed on CDAR. I'll be covering some of that material today. So we've now delivered our fourth straight quarter of positive EBITDA. We reported adjusted EBITDA of $1.8 million and just under $1 million of net income, making Q3 our third consecutive quarter producing positive net income. We're committed to operating in the 7% to 10% adjusted EBITDA range annually and expect to see us reinvest anything above that range in our go-to-market efforts. As a reminder that the current EBITDA is net of investments that we're making in R&D and product-based initiatives. Our MIPS and professional services total contract value bookings totaled $14.4 million. Due to the somewhat lumpy nature of both PS and MIPS, we tend to look at a longer horizon to see what our trends are showing, and we're up about 6% on a trailing 12-month basis. MIPS and PS revenues totaled $13.4 million this quarter. The MIPS Q3 revenues actually reached their highest since Q2 2024 at $3.9 million, but we're still down on a trailing 12 despite bookings being up significantly. Some of that can be explained by longer-term contracts starting to make up more of the MIPS bookings mix. So the MIPS sequentially was up quarter over quarter, but again, we do tend to look at things on a trailing 12, and our PS is pulling us down a little bit on that front. This earnings call, I really want to take a moment to really help remind people about the business we're in and share a little bit about how it's changing. I have seen some stuff getting published recently on folks trying to explain our business, and I thought it best that we take a moment just to do a little bit of a refresher and an update. Our business really is built on a foundation of helping clients with creating cleaner, more accessible data. That's that first rung. We call that part of our business SDS or strategic data services. This is also where we deploy many of our MIPS solutions. You've heard me talk about SKU build and SKU enrichment. This is the area we do that in. This layer is also the layer that we've been leveraging machine learning and AI for many years to help automate that process of clean data. That next level is where we integrate systems, and this is somewhat to communicate accessibility of data. We integrate systems to each other and ultimately help move data between systems. It's generally done in the form of PS, but we do have managed services that are around managing the microservice framework we use to do integration. In fact, some of the work we're doing now is independent of the platforms that we deploy, which is that next layer up. We design and build enterprise applications, generally in the commerce ecosystem, and we integrate them through our integration services. Many of you will be familiar with the enterprise applications we specialize in. Again, I think people tend to think about us mostly at this light layer and mostly in retail, and it's not really a good description of what we do. We have the managed services layer that sits on top of that, and that's the observability layer. It is, again, where we do manage services. We've also built and deployed our MIPS solutions in this layer. And this is the layer where we've started to introduce agentic AI. These solutions sit on top of the clean, accessible data. They sit inside our control tower databases, and they allow us to do some things that you wouldn't otherwise be able to do without the benefit of AI. But increasingly, AI is finding its way into every one of these layers, and it's going to really continue to evolve and increase in importance in our business particularly. As I say, we've been doing it in Layer 1 and Layer 4 for quite some time, but it is now increasingly finding its way into the other two layers. I think it's important to reframe a little bit of what we're doing in part because of the changing landscape that's going on. And, you know, I'm going to share some observations about what we've seen. But what is really becoming clear to us is how important AI is becoming in the conversation, how it's shaping the discussions and even how it's shaping some decision making. So we had a really strong quarter for new logo acquisitions and our entry point solutions are helping really overcome what I'm still seeing as budget pressures. Folks continue to refer to budget constraints. We hear about that a lot in the market still. And it's our entry point solutions that have really helped us overcome that. Smaller starting points. A lot of those conversations are now really including where does AI fit in the mix? So what we're seeing really is that the commerce landscape is shifting from a rules-based automation. So again, some of the software that you hear us talk about and deploy. And again, what we integrate and stand up for customers. But it's moving towards a more autonomous and semi-autonomous decision-making landscape. And this is really where AI and AI agents are starting to play a more prominent role. As a result of folks still being somewhat uncertain about how AI is going to play, I think we are going to continue to see folks reluctant to do really big, expensive, long-term projects because they're still not really sure how AI is going to affect that ecosystem. So we're seeing experiments, we're seeing POCs, and I think as a result we're also seeing some delays in making big decisions to do wholesale platform shifts. The data highlights continue to be super relevant to what we're doing. Data discussions are turning into AI, but ultimately, it means we're actually in the data conversation already, and it was allowing us to seamlessly shift into an AI conversation about what is going to happen to that data in the AI world. As I said, there's lots of experiments, not just us, but many of our customers doing experiments with AI as feature enhancement. Again, I think what we're seeing is people taking AI and applying it to their existing infrastructure. But what we're starting to see are more questions about AI agents being more foundational. Rather than put it on top of an existing infrastructure, the questions are starting to get asked, do I redesign this process to start with AI in mind? Again, it's early days. I don't expect this to translate into overnight boom. I think, you know, there's a lot, it has practical application and it's going to increasingly become more critical and and central to many of the functions that we perform in the commerce transaction. And we've been doing the data cleaning piece for over five years. We acquired a business that really allowed us to entrench ourselves in there. It's foundational, and it's not just operational. It is creating potential moats. When you think about the 7 million SKUs, clean SKUs that we have in our library, that number grows seven figures a month. This really has the potential to create a moat with clean data. What can you do with it? It's also allowing us to demonstrate to customers what they could do with it if they had the clean data. The last thing I'm going to really chat about, and I'll share a little bit more in the next couple of slides, is our client profiles. We really have a couple of key distinct segments. Now, retail, which seems like what most people think of us exclusively as, that's not the case. Retail is an important segment. There's a lot to be learned in there. There's a lot of things going on in the AI realm in there. But we're very active in industrial manufacturing and distribution. And one of the subsets in that is automotive. Now, these categories represent significant revenue and margin contribution. They are more than 50% of our total revenue now. And we love these categories as they have catalog complexity, many operating with millions of SKUs that are driven by technical specifications and compatibility requirements. And that demands very specialized solutions, but it also really lends itself to AI and machine learning. There's a lot of white space here, and we're investing a lot of our go-to-market dollars to win in this space. One of the things we wanted to really socialize the market to is how we're thinking about frictionless commerce.

But really what's happened with the advent of... Bill, can you hear me? Yeah, maybe just turn off video. It's getting choppy. We can hear you, but maybe video is kind of impacting your bandwidth. Okay, thanks. Does that make it better? Right now we can hear you. I'll let you know.