Alarum Technologies Ltd. Q3 FY2025 earnings call

• Q3 was a breakout quarter with $13 million in revenues, up 81% YOY and 48% sequentially, driven by AI customers and expansion in existing accounts.
• Gross margin pressure due to large AI projects, upfront infrastructure investments, and third-party partner costs, but management sees this as a short-term strategy for market share capture.
• Initiatives to improve margins: in-house solutions development, network optimization, and shift to higher-value products.
• AI market dynamics involve volatility in R&D-stage customers' needs, but revenue patterns are expected to smooth as models move to production.
• Product suite expansion with dataset, website unblocker, and scrapers showing strong growth.

In the third quarter of 2025, Alarum Technologies reported revenues of $13 million, a significant increase of 84% year-over-year. The gross margin was 56% in Q3 2025 compared to 74% in Q3 2024, due to increased investments and large-scale AI projects. Operating expenses rose to $7.4 million in Q3 2025 from $4.1 million in Q3 2024. Net profit was $0.1 million in Q3 2025 vs. $4.2 million in Q3 2024. Adjusted EBITDA was $1.2 million in Q3 2025 vs. $1.4 million in Q3 2024. Revenue contribution was driven by AI customers, with one large-scale AI customer contributing about $3.5 million.

• Q4 2025 revenue expected to be around $12 million (+/-7%), representing 63% YOY growth.
• Adjusted EBITDA for Q4 2025 expected to be around $1 million (+/-$0.5 million).

• Market volatility in AI R&D customers' demand, which can change frequently.
• Reliance on third-party partners affecting short-term gross margins.
• Uncertainty in predicting future data needs and usage from R&D-stage customers.

Q: 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?

A: The large customer's consumption is huge in volume, with high satisfaction. However, R&D-stage customers' needs can change frequently, making long-term revenue consistency hard to predict.

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

A: Usage may become more sustainable but not necessarily higher or lower, with more predictability in the long future as models move to production.

Q: 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?

A: There are existing customers using the product and others in the pipeline for the solution or related products, with great ROI seen in the quarter.

Q: 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?

A: Simulating in-house solutions shows higher gross margins, but using third-party vendors is risky until demand is proven. Gross margin recovery is temporary, with initiatives to improve margins through in-house development and network optimization, expecting improvement as volume and product mix shift.