Alarum Technologies Ltd. Q3 FY2025 Earnings Call

Ladies and gentlemen, thank you for standing by. Welcome to Alarum Technologies Third Quarter 2025 Earnings Results Conference Call. 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 uses are 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. 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, we saw 26% more paying customers, 17% higher average revenue per customer, and 48% sequential revenue growth. While our largest customers contributed just over one-fourth 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. Significant consumption at the start of significant work with them naturally comes with lower unit pricing 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 flowed directly into cost of revenue. That said, these customers validate the strength of our technology, reinforce our ability to deliver data at native scale, and represent significant long-term strategic upside. Earlier this year, given the strong potential we saw, we made the delivery decision to aggressively expand capacity at premium residential infrastructure and build a dedicated high-throughput pipeline ahead of revenue. This front-loaded investment is the primary reason for the temporary pressure on margins, and importantly, I strongly believe that this is exactly the right long-term strategy. While remaining profitable overall, we are sacrificing some near-term profitability to strongly capture market share and secure more relationships in the segment growing several hundred percent year-over-year. Looking ahead, margin improvement. This margin pressure is short-term planned and fully addressable. Several initiatives are already underway and are part of our strategy: one, in-house solutions. Our goal is to serve as the customers' leading and most reliable provider. To achieve this, we leverage our deep market expertise and long-standing relationships with numerous vendors. And sometimes, when needed, we select a partner to collaborate with. Once we validate continued demand for the specific product, we will either develop it in-house as an alternative or consider acquiring the solution. This approach will allow us to enhance our capabilities at lower risk and ensure demand while significantly improving our gross margins over time. Two, network optimization. We are identifying large optimization opportunities across our service and network architecture. Improved efficiencies have already begun and will continue to improve over time. Three, shift toward higher-value products. As dataset scrapers and website unblockers grow as a percentage of revenue, unit economics and margins will improve as well. We remain confident in our ability to expand both growth and operating margins as our product mix continues to shift and our infrastructure becomes more efficient. AI market dynamics. We are operating at the frontier of the largest AI model training runs on the planet. At this stage of the AI build-out, demand from leading labs can move sharply quarter-to-quarter. To refresh massive datasets, to test new architectures or shift compute priorities. This volatility is normal in a market that is still in a land grab phase. As models move from research to more structured production and fine-tuning cycles, revenue patterns will naturally become smoother and more predictable. Until then, our major KPIs are year-over-year trends, penetration, and quality of relationships. Across all three, we have never been stronger. Product suite expansion. Our AI-strengthening product suite is scaling rapidly. Dataset and material are fast-growing revenue contributors. Website Unblocker delivered triple-digit sequential growth. Custom scrapers delivered high double-digit sequential growth. IP proxy network is stable to growing in absolute terms and continues to support massive AI workloads. Our revenue mix is evolving from a single product proxy business into a diversified multiproduct data infrastructure platform. This shift is expected to drive stronger long-term margins and healthier unit economics. Outlook and summary. We remain confident that we are in the right position at the early stages of a massive and long-lasting transformation in the data industry. Looking ahead to Q4, and as Shai will detail shortly, we expect revenues of approximately $12 million plus or minus 7%, which is up a very significant 62% year-over-year and will allow us to end the year at around $41 million in revenues, up almost 30% year-over-year and well ahead of our internal expectations earlier in 2025. From global tech leaders to fast-growing startups, all are increasing their reliance on high-quality real-time public web data at unprecedented scale, and Alarum is uniquely positioned to serve this market. Our vision was and remains clear: Alarum will become one of the foundation data infrastructure companies powering the AI area. I will now hand it over to Shai for the financial details and our Q4 outlook. Shai?

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 $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 AI customers, non-IFRS gross margins for the third quarter of 2025 were 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, high-strategic opportunities that we believe can drive meaningful long-term growth and profitability even at the cost of lower short-term margins that we expect to improve in the future. Expenses. Operating expenses in the third quarter of 2025 were $7.4 million compared to $4.1 million in the third quarter of 2024. The increase was driven mainly by planned operating expenses investments that we discussed last quarter. This includes higher employee-related costs, particularly in R&D and sales-related compensation as we continue to grow the team to accelerate product development and expand our capabilities, as well as by the overall increase in the scale of operations. Net profit in the third quarter of 2025 was $0.1 million compared to a net profit of $4.2 million in the third quarter of 2024. As a reminder, the Q3 2024 profit was particularly high due to a sharp fair value decrease of investors' warrants related to the share price decreases in that quarter. Those decreases resulted in high financial income of $3.5 million. The vast majority of the warrants expired in 2025, and hence, they do not impact our bottom line anymore. Adjusted EBITDA. Adjusted EBITDA in the third quarter of 2025 was $1.2 million compared to $1.4 million in the third quarter of 2024. Basic earnings per ADS in the third quarter of 2025 were $0.01 compared to $0.60 in the third quarter of 2024. The high Q3 2024 figure was a result of the one-time financial income I just mentioned. On a non-IFRS basis, basic earnings per ADS were $0.18 in the third quarter of 2025 compared to $0.20 in the third quarter of 2024. Our current share count is approximately 71.2 million ordinary shares or 7.1 million U.S. listed ADSs. As of September 30, 2025, the company's shareholders' equity increased to $31.1 million, up from $26.4 million on December 31, 2024. Our cash, cash equivalents and debt investment balance, including accrued interest as of September 30, 2025, was approximately $24.6 million compared with $25 million at the end of 2024. Alarum's solid cash position supports our ability to continue investing strategically while maintaining a focus on sustainable value creation. Guidance. Moving over to our outlook for the fourth quarter of 2025. Our guidance reflects what we see today based on customers' orders, backlog and current consumption trends. We currently expect that in the fourth quarter of 2025, revenue will be around $12 million with an up and down range of approximately 7%, representing about 63% year-over-year growth. Adjusted EBITDA for the fourth quarter of 2025 is expected to be around $1 million with a range of plus or minus $0.5 million. To summarize, 2025 continues to be a year of strong momentum, a solid balance sheet, and growing market interest. We remain focused on our commitment to generating long-term sustainable value for all our stakeholders. With that, we will now open the call for questions.

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. It's a demand for one of our products, which in this specific case, it's a combination between a scraper and dataset. 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. As I mentioned, I think a few times in the last period and also in my pitch a few minutes ago, we can divide the world of data needs and data collection at this stage into two groups. One is the group of customers that are using the data and the data needs for their production stages. They are selling their products, they're selling their analytics to customers and working more or less, they are stable, and we know the need for the coming period, and we can predict the future. The second is customers that are basically in the R&D stage, which are developing their LLM and their AI models. In this stage, their needs can change often. They can change from month to month, 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 a real prediction of what is the amount of data that you will need from this or that source, what is the duration? Things are changing often, and according to this, their needs and their demand can change 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 reliable information that we can share or I can share with you regarding the future of this or that use case product need, etc. 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 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. I think that we can divide it into two phases. One phase is in the education stage when they need a huge amount of data in a very short time. Then when it comes to the production stage, they will use maybe the same amount of data, but it will not be in a short time; they will divide it over the quarters and over time. The second thing, which is even more important, is that the data sources, even if you finish educating 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 on another specific vertical or area. Then again, you will get into the cycle of downloading a massive amount of data and then going to something that is more sustainable. I cannot say now it will be higher or lower, but I think that it will be more sustainable. In this way, 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?

What was the first part of your question, Brian? Yes, you've mentioned a customer receiving large datasets, and it's a new solution for us. What's the strategy for selling this solution to additional customers? Currently, we have some other customers using this product and its capabilities, including smaller clients. Their needs are smaller right now because they are in the R&D stage and require less data, but this doesn't limit future possibilities as they can quickly increase their demand. Firstly, we have other customers utilizing this data, and secondly, we have several others in the pipeline for this specific dataset, other datasets, or products like the scrapers and unblocker, which have shown a great return on investment this quarter.

Okay. And then as revenue scales and maybe you have less reliance on partners for dataset 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 first 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, you could see the gross margin something between almost a 70% gross margin. Okay? But let's say, I want to emphasize something that is very important. The world of data collection and data scraping and datasets has a huge variety of product capabilities and needs. For us, I think it's too risky to start developing everything internally before we see the real demand unless we can predict real demand coming very soon. So in this way, because we have hundreds of customers that basically are themselves scraping companies and dataset companies, we are leveraging the fact that we have the approach and the door to these customers. If something comes in, let's say, a current customer is asking from us a capability that we don't have right now, we will use this third-party white-label solution, stay with the major vendor, maintain control, and if we see that the demand is sustainable, we can very quickly develop it internally or consider acquiring the vendor or solution. This way, we mitigate risks and expenses of R&D for solutions that may not have demand. The downside is that you may see the impact on the gross margin as well as on the EBITDA. So this is my answer for the first part of your question. Can you repeat what was the other part, Brian?

Yes, I'm wondering when. It sounds like pricing is a little low. So when will 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 a heavy load of third party in the fourth quarter?

First of all, even at this point, we are testing our own internal solution in production. Therefore, it will be lower. We anticipate improvement in Q4, assuming everything goes well, and we expect demand next quarter to show significant growth. Regarding unit prices, it appears that demand from certain customers may decrease in size and volume, which could lead to an increase in unit prices. Additionally, we expect many smaller competitors will struggle to stay viable due to the need for robust infrastructure capable of handling extensive data with global coverage and millions of endpoints that require constant updates to avoid website blocking. As a result, we may only remain with the leading competitors, which could naturally push the price per unit slightly higher. However, even at these volumes, the situation is manageable. The more crucial focus is on enhancing our underlying infrastructure to increase efficiency, lower the costs of goods, and improve gross margins. Typically, as volumes rise, unit prices decrease, and that's acceptable as long as we adapt the cost structure behind it.

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

Okay. 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 until 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.