# Alarum Technologies Is Winning the AI Data Race in a Building With No Sign Out Front Canonical URL: https://www.hordus.ai/blog/alarum-technologies-is-winning-the-ai-data-race-in-a-building-with-no-sign-out-front Markdown URL: https://www.hordus.ai/blog/alarum-technologies-is-winning-the-ai-data-race-in-a-building-with-no-sign-out-front/raw Author: Brandon Goetz, Hordus AI Published: 2026-06-16T08:25:33.249Z Summary: Alarum Technologies is posting 64% revenue growth by selling web data infrastructure to the world's leading AI labs. The irony is that the same AI engines their customers are training have no idea Alarum exists. --- ## Full Article ### TL;DR Alarum Technologies (Nasdaq: ALAR) has built critical web data infrastructure for leading AI labs, posting 64% year-over-year revenue growth in Q1 2026. The message from CEO Shachar Daniel is clear, confident, and backed by numbers. The problem: when enterprise buyers, AI investors, and data science teams ask AI engines who the leaders in web data collection are, Alarum is not reliably in the answer. A Hordus GEO analysis of alarum.io scored the company 5 out of 100, flagging critical gaps in AI discoverability, structured identity, and developer documentation. This article shows what that means in practice and how Hordus can close the gap between Alarum's real market position and its AI-visible one. ### "Data Fuels Intelligence" is the Market Thesis. Now Prove It Owns the Category. When Shachar Daniel, CEO of Alarum Technologies, declared in the company's 2024 annual results that "the companies that will lead this revolution are those that anticipate change, build a strong foundation, and position themselves for long-term success," he was not speaking in abstractions. He was describing exactly what Alarum had done: pivoted cleanly to web data collection, scaled the NetNut IP proxy network, and moved early into AI training data infrastructure while competitors were still debating whether the opportunity was real. That bet has paid off. The timing of that strategy now matters even more, because the market has caught up to the thesis. In May 2026, Proxyway released its annual proxy server market report, and its headline finding was unambiguous: AI-related demand has pushed major providers to grow 50% or more year-over-year, and for some companies, AI has become the largest customer vertical by volume. The report noted that the dominant operational priority for 2026 is ensuring capacity, as AI labs move toward more frequent data refreshes and more complex real-time data pipelines. In short, the infrastructure Alarum has been quietly building for AI customers is not a niche play anymore. It is the center of an industry. The question is whether Alarum owns that narrative when prospects go looking. ### The Customer Signal Is Unmistakable The growth numbers Alarum has posted are not the result of marketing momentum. They come from real, repeating demand from real customers. In the Q3 2025 earnings release, Shachar Daniel put it directly: "The numbers speak for themselves: 26% more paying customers, 17% higher average revenue per customer, and 48% sequential revenue growth in the third quarter, all while we intentionally front-load infrastructure to support the fast-growing demand for AI training runs." This is a company that is not speculating about AI demand. It is serving it, at scale, with repeat orders and expanding scope from the world's leading AI model developers. The Q1 2026 results continued the trajectory with 64% year-over-year revenue growth, driven specifically by demand for high-quality public web data for LLM training, fine-tuning, and updates. Those customers chose Alarum deliberately, and they are coming back. The gap that Hordus is here to close is not between Alarum's story and reality. It is between Alarum's story and what AI engines tell the next buyer who is one search away. ### Who Is Looking for Alarum Right Now Alarum's realistic prospects in 2026 fall into several distinct profiles, each with urgent and specific needs. AI research teams at foundation model companies need residential proxy networks and web dataset pipelines that can reach hard-to-access domains at scale without triggering bot defenses. ML infrastructure leads at enterprise technology companies are looking for compliant, high-throughput web data collection for RAG refresh cycles. Competitive intelligence managers at financial firms, retail platforms, and cybersecurity companies need reliable, geo-targeted data collection without single points of failure. Procurement and vendor evaluation teams at large enterprises may not know NetNut by name but are actively searching for the category. All of these buyers use AI engines to answer early-stage questions. Here is what they are asking. ### What the Hordus GEO Analysis Found The Hordus analysis of alarum.io produced a score of 5 out of 100, graded F (Unusable). This is not a criticism of the product or the company. It is a specific and fixable technical profile of how AI engines currently experience the Alarum web presence. The core finding: Alarum has basic web accessibility but lacks public API access and developer documentation entirely. For AI engines that rank sources by how well-structured, citable, and authoritative they appear, this is the equivalent of having no public presence at all. ### How Hordus Turns the Analysis Into Competitive Advantage Hordus works with companies to close the gap between what they have built and what AI engines can see. For Alarum specifically, that work looks like this. AI answer share. Today, when a prospect asks an AI engine "who are the top web data collection platforms for AI training," Alarum is not reliably in the set. Hordus builds the structured content and entity signals that make Alarum a cited source in those answers, not an invisible competitor. Citation strength. The CEO quotes and financial milestones that Alarum has published are compelling. But AI engines need those claims anchored to a recognized entity with consistent, structured metadata across the web. Hordus builds and distributes that entity layer so Alarum's own statements get pulled into AI answers with attribution. AI-readable content architecture. The alarum.io site currently lacks the heading structure, FAQ schema, and summary layers that AI engines use to extract answers. Hordus redesigns that content layer so that the site becomes a source AI engines cite, not a site they skip. Category positioning. "Web data collection solutions" is the current descriptor. Hordus works with Alarum to sharpen and own a more specific AI-visible category label, something like "AI training data infrastructure" or "enterprise residential proxy network for LLM development," so that when the category is named in an AI answer, Alarum is the entity that fills it. Head-to-head AI comparisons. Buyers ask AI engines to compare Alarum against Bright Data, Oxylabs, and others. Without structured competitive positioning content, Alarum loses those comparisons by default. Hordus creates the comparison content that gives AI engines the material to represent Alarum accurately and favorably. ## FAQ Q: Why does Alarum Technologies score so low on AI engine visibility if the company is growing so fast? A: Revenue growth and AI discoverability are separate tracks. Alarum's customers already know the company and are coming back with repeat orders. But new prospects who discover vendors through AI-generated answers are encountering a different Alarum: one with no structured identity layer, no API documentation, and no entity signals that AI engines use to cite sources. Hordus addresses this by building the GEO infrastructure that makes Alarum findable and citable in AI answers, without changing a single thing about the product. Q: What specific questions about Alarum Technologies are AI engines currently failing to answer well? A: Based on the Hordus GEO analysis, questions like "what is the best residential proxy for AI training data," "which company provides web data collection for large language models," and "how does NetNut compare to Bright Data for scraping at scale" are all returning incomplete or competitor-favoring answers. Hordus builds the content and structured signals to make Alarum the cited answer to each of those prompts. Q: How would Hordus help Alarum Technologies compete in AI-generated vendor comparisons? A: AI engines pull comparison content from structured, citable sources. Because alarum.io currently lacks developer documentation, schema markup, and comparison-ready content architecture, Alarum is missing from or underrepresented in those outputs. Hordus creates the comparison content layer, category definitions, and entity anchors that put Alarum on the right side of those comparisons. Q: Does Alarum Technologies's executive narrative need to change, or just its AI visibility? A: The narrative does not need to change. Shachar Daniel's messaging around data infrastructure, AI customer growth, and long-term market positioning is coherent and well-documented. What Hordus does is make that narrative legible to AI engines by structuring it as citable, entity-attributed, schema-marked content. The message stays the same. The audience expands to include every AI engine that a prospect might ask. Q: What is the business case for Alarum Technologies to invest in GEO and AI visibility now? A: Alarum is in the middle of a category-defining growth window. The proxy and web data collection market is expected to reach $2.28 billion by 2030, with AI as the dominant demand driver. The companies that establish AI answer-share now will be the default vendor recommendation for the next three to five years of buyer discovery. Hordus helps Alarum claim that position before a better-optimized competitor fills it by default.