Ladies and gentlemen, thank you for standing by. Welcome to Alarum Technologies Third Quarter 2025 Earnings Results Conference Call. [Operator Instructions] This conference is being recorded. I will now turn the call over to Kenny Green, Investor Relations at Alarum. Kenny, please go ahead.

Thank you. Good day to all of you, and welcome to Alarum's conference call to discuss the results of the third quarter of 2025. I would like to thank management for hosting this call. Today, we are joined by Shachar Daniel, Alarum's CEO; and Shai Avnit, CFO. Shachar will begin the call with an overview of the third quarter, followed by Shai, who will review key elements of the financials. Finally, we will open the call to our question-and-answer session. Before we get started, I want to highlight the forward-looking statements disclaimer. This conference call may contain, in addition to historical information, forward-looking statements within the meaning of the safe harbor provisions of the Private Securities Litigation Reform Act of 1995 and other federal security laws. Forward-looking statements include statements about plans, objectives, goals, strategies, future performance and underlying assumptions and other statements that are different than historical facts. For example, when we discuss our strategy of prioritizing long-term relationships and market share capture over short-term margins and profitability, expected trends in market demand and AI-driven growth, our business momentum and pipeline, our expectations regarding future revenue patterns and margin improvements, the anticipated impact of our strategic investments and product mix and our estimates regarding fourth quarter 2025 revenues and adjusted EBITDA, we are using forward-looking statements. These forward-looking statements are based on current management expectations and are subject to risks and uncertainties that may result in expectations not being realized and may cause actual outcomes to differ materially from expectations reflected in these forward-looking statements. Potential risks and uncertainties include those discussed under the heading Risk Factors in Alarum's annual report on Form 20-F filed with the SEC on March 20, 2025, and in any subsequent filings with the SEC. All such forward-looking statements whether written or oral, made on behalf of the company are especially qualified by these cautionary statements, as such, forward-looking statements are subject to risks and uncertainties, and we caution you not to place undue reliance on these. On the call, the company will also present non-IFRS key business metrics. The non-IFRS key business metrics the company use our EBITDA and adjusted EBITDA, non-IFRS gross margin, non-IFRS net profit or loss and non-IFRS basic earnings or loss per share or EPS. The exact definitions and reconciliations of these non-IFRS key business metrics are described in the company's financial results press release, which is available in the investor lobby of Alarum's website. These measures may differ materially from similarly titled measures used by other companies and should not be considered in isolation from or as a substitute for financial information prepared in accordance with IFRS. And now I'd like to turn the call over to Mr. Shachar Daniel, Alarum's, CEO. Alarum -- Shachar, please go ahead.

Thank you, Kenny, and good morning, everyone. Thank you for joining us. Let me start with the headline. Q3 was a breakout quarter, $13 million in revenues, up 81% year-over-year and 48% sequentially. This is one of the strongest quarters ever in Alarum's history and clear evidence that our platform has begun critical infrastructure for some of the world's leading and most exciting AI labs and global technology companies. This significant jump was driven by increased consumption from major AI customers, continued expansion within existing enterprise accounts and strong adoption of our newer AI-centric products. During the quarter quarter we saw 26% more paying customers, 17% higher average revenue per customer and 48% sequential revenue growth. While our largest customers contributed just over 1/4 of our revenues and our top two contributed just over 40% growth was growth based. We also continue to see significant traction from our major global e-commerce platforms in Asia, which faced repeat and expanding orders, despite the natural volatility of this yearly hyper growth phase of the AI market, we are confident that demand is broadening and growing sharply, and AI will be a core long-term and significant growth engine for us. Profitability. As we noted last quarter, our gross margin will see a short-term impact by the mix of our two very large customers, both globally recognized brands operating at extraordinary scale. There's significant consumption at the start of significant work with them naturally comes with our -- with lower unit pricings, and for us, higher initial infrastructure costs. Part of our delivery for these customers only at the early and initial stages of delivery relies on third-party partners and those costs flew directly into cost of revenue. That said, these customers validate the strength of our technology, reinforce our ability to deliver data…

Thank you, Shachar, and hello, everyone. I will start by reviewing our key financial results for the third quarter of 2025, comparing them to the same period last year, unless otherwise noted. Following that, I will provide our guidance for the fourth quarter of 2025. Detailed definitions and reconciliations of our non-IFRS key business metrics can be found in our Q3 2025 financial results press release. And one final note before I begin, the figures I will be discussing are rounded for clarity and ease of reference. Turning now to our financial performance and first revenues. Revenues in the third quarter of 2025 reached $13 million compared to with $7.2 million in the third quarter of 2024, an increase of approximately 84% year-over-year. As Shachar mentioned, the increase was driven mainly by artificial intelligence customers with a significant contribution from one large-scale AI customer, which accounted for about $3.5 million in revenue in the quarter. At the same time, we continue to see a shift in customer segments with strong growth in the AI vertical, offsetting declines in other segments. Gross margins. As a result of our increased investments into our business to capture opportunities ahead and due to the higher share of large-scale projects with AR customers, non-IFRS gross margins for the third quarter of 2025 was 56% compared to 74% in the third quarter of 2024. As Shachar mentioned, the lower margin reflects the work we are doing with large customers, mainly AI companies, which require data gathering at significantly higher scales, necessitating upfront costs, including a larger volume of servers and stronger, higher quality infrastructure as well as lower unit price charges. In addition, the first material products served in 2025 triggered related third-party costs. Overall, this is consistent with our strategy to engage in large scale,…

[Operator Instructions] Our first question is from Brian Kinstlinger with Alliance Global Partners.

Can you talk about the large project for data set delivery? How is the program going? What's customer satisfaction like? And how should we think about the consistency from this customer in terms of revenue contribution over the next 12 to 18 months?

Okay. Hi, Brian. So first of all, a smaller correction, it's not a project, yes. It's a demand for one of our products, which is -- in this specific case, it's a combination between a scraper and data set. And this demand is a natural demand in our space, like many other customers that have their own needs of some of our products. This specific, let's call it, customer consumption is huge from the volume aspect. And of course, it's taking a significant portion of our revenues. So that's why we can talk about it specifically. Now regarding your question for the future. So as I mentioned, I think, a few times in the last period and periods in plural and also in my pitch a few minutes ago, you know we can divide the world of data needs and data collection at this stage to let's say, to -- we can classify to two groups. One is the group of customers that are using the data and the data needs for their -- in the production stages. They are selling their products, they're selling their analytics to customers and working more or less, they are stable more or less, and we know the need, we know the need for the coming period, and we can predict the future. The second is customers or our customers that are basically in the, let's call it, in the R&D stage, which are developing their LLM and their AI models. In this stage, their needs basically can change often. They can change from month to month, so it can change sometimes from week to week because they are in the stage of developing their model. And as you know, in many other spaces when you are in the R&D stage, you don't have -- you as the company, as a customer, you don't have a real prediction, what is the amount of data that you will need from this or that source, what is the duration? Things are changing often, websites are changing the way they are acting very often. And according to this, their needs and their demand can be changed sometimes on a weekly basis, sometimes on a monthly basis and sometimes it can go for a long time. But to be very honest and transparent, we don't have, and I think our customers, basically don't have reliable information that they can share or I can share with you regarding the future of this or that use case product need, et cetera. The third question is how -- for this point of time, the level of satisfaction is high. We are providing a huge amount of data for these customers and others, the rotation -- the retention is good, and we're in a very good position from this aspect with this customer and some others.

Do you see once R&D customers have developed their models that usage is higher or lower or just more predictable?

Okay. So I think that we can divide it I think maybe to two phases, one phase is in the education stage when they are educating the model, they need a huge amount of data in a very short time. And then when it comes to the stage of production, let's call it, the production stage, then they will use maybe the same amount of data, but it will not be on a short time, they will divide it over the quarters and over the time. The second thing, which is even more important is that the data sources, meaning -- even if you finish to educate your model from one data source, and you go to production stage, then the next big thing is coming. And now you want to train him about this specific vertical or this specific area. And then again, you will get into the cycle of downloading a massive -- a huge amount of data and then go to something that is more sustainable. So I cannot say now it will be higher or lower, but I think that it will be more sustainable. And in this way, we can be -- we can predict, and we can be more accurate in our predictions for the long future.

And then as you've had this announcement and success here, can you talk about what the pipeline to sell this new dataset delivery solution is to other customers?

So that -- what was the first part of your question, Brian?

Yes, yes. You've got this customer that you're delivering large data sets to. It's a new solution for you. What's the pipeline to sell this solution to other customers?

Okay. So it's not the pipeline. We already have some other customers that we are leveraging this product and these capabilities to other customers, which are smaller, not -- it doesn't have to be smaller customers, it's -- their need is at this point of time is smaller again because it's in the R&D stage. So basically, they are in a different stage. So now they need a lower amount of data, but it doesn't say -- it says nothing about the future because they can ramp up very fast and increase the demand. So first of all, we have current -- other current customers that are using this data. And second, we have a few others in the pipeline for this specific data set or for other data sets or for the scrapers, the unblocker and other products that we started to see a great ROI from them in this quarter.

Okay. And then as revenue scales and maybe you have less reliance on partners for data set delivery, how should we think about the gross margin recovering as you've used the word temporary pressure on gross margins. And as part of that, what volume would you need maybe -- that would trigger more investments in infrastructure and capacity, and how do you think about the recovery long term in pricing or unit economics?

Okay. Let's start from your -- the first part of your question. So basically, if we simulate a situation in this quarter that we wouldn't use any third-party vendors and all of the solutions were in-house. So basically, you could see the gross margin something between almost a 70% gross margin, okay? If we simulate exactly the same situation in this quarter. Now -- but let's say, I want to emphasize something that is very important. The world of data collection and data scraping and the data sets has a huge variety of product capabilities and needs. And for us, I think it's too risky to start and develop everything internally before we see the real demand unless we can predict a real demand coming very soon. So in this way because we have hundreds of customers that basically are by themselves are scraping companies, data set companies, we are leveraging the fact that we have the approach and the door to these customers. And if something is coming in, let's say, a current customer that is asking -- existing customer that is asking from us a capability or a need that we don't have it right now, so we will use this third-party white label solution, then we will stay with the major vendor, we will have the control in our hand, and if we see that the demand is sustainable, is here to stay and some more other customers have this need, we can very fast develop it internally or vice versa or to buy even to as a kind of acquisition to acquire this vendor or this solution. In this way, we mitigate risks because we mitigate expenses of R&D and solutions that maybe we don't have -- will not have a demand. We are leveraging the fact that we have basically approached to all the markets. And the downside is that you will see -- you can see sometimes if we use this approach, again, for example, you can see the impact on the gross margin and, of course, on the EBITDA. So this is, I think, my answer for the first part of your question. Can you repeat what was the other part, Brian?

Yes. I'm wondering when -- yes, it sounds like pricing is a little low. So when will -- when and how do you think about the unit economics, which is what I assume pricing is, when is that improving? And are you using -- to your response of the other answer, are you going to be using heavy load of third party in the fourth quarter?

Lower, first of all, lower even we are -- now at this date, we are -- we have our own, for example, our own internal solution that we are testing it even in production stage. So it will be lower. And so it might -- even in this quarter, we can see this in Q4, we can see the improvement. And hopefully, if everything goes well, and the demand this year in the next quarter, it will be a material improvement. And second thing, regarding the unit price. So I think that as time is running, it looks like that we talked about in your previous question, the need or the demand from this kind of customers even -- maybe will go down from the size and volume. And of course, directly the unit price will go up. Second thing, it looks like that for this amount, we will see a lot of small players that are basically competing are kind of competitors now will not be able to stay in the game because what stands behind this huge amount of data is an infrastructure with global coverage and millions and millions of endpoints that need to be changed or replaced all the time in order to be preventing from blocking by the website. So at this point, I think that we will stay only the leaders, and then naturally, it might make the price per unit go up a little bit. But still, in these amounts, which is not bad, by the way, it's not bad. It's not a bad thing, but the more important thing is to make our infrastructure as we're talking about third parties, our servers, our -- all the DevOps behind our company to make it more and more efficient. And by this, by the way, to decrease the cost of goods and to improve the gross margins because naturally, when volumes are going up, unit price is going down. It's okay, it's good. It's a good thing. And if you know how to adapt the cost of goods behind, then you can be in a very good situation.

[Operator Instructions] With no further questions, I would like to turn the conference back over to Mr. Daniel for closing comments.

Okay. So thank you very much for joining us for this third quarter investors call, it was a pleasure, and hope to see you next quarter and hope Alarum will keep the delivery and the amazing achievements to achieve till now. Thank you very much.

Thank you. This will conclude today's conference. You may disconnect at this time, and thank you for your participation.