AI Search Visibility for a B2B Service Provider: How Zeno Visibility Turned Search Intent into a Semantically Connected Content System
AI Search Visibility for a B2B…
Initial Situation
The customer is a medium-sized B2B service provider from the DACH region with around 240 employees and multiple service lines in IT outsourcing, cybersecurity, modern workplace, and compliance consulting. The company generates mid-double-digit millions in revenue and derives a significant share of its pipeline from organic search and referrals. Until 2024, the classic SEO model worked reliably: around 18,000 organic sessions per month, strong rankings for transactional keywords, and solid visibility in the top 10 for about 1,600 search terms.
However, with the shift toward generative search interfaces, the search behavior of target audiences changed. Decision-makers increasingly asked not only, “Who ranks?” but “Which solution does the AI recommend to me?” This is exactly where the gap emerged. In initial tests with ChatGPT, Gemini, Perplexity, Claude, and Copilot, the brand was only rarely mentioned for relevant intent combinations. At the same time, the content was thematically fragmented: individual blog posts, a few guides and product pages, but no semantically connected system that mapped the full expertise of a topic cluster. The four-person marketing team could not close this gap manually, even though the need for AI Visibility Monitoring had already been identified internally.
Challenge
The core problem was not a lack of traffic, but a lack of machine authority. The brand was found in classic search, but LLMs and AI assistants did not reliably understand it as a trustworthy source. For prioritized search intents such as “managed SOC provider,” “ISO 27001 consulting DACH,” or “cybersecurity service provider for mid-sized businesses,” competitors, industry portals, or generic recommendations appeared more often than the company’s own brand.
This had two consequences: first, a growing share of the early information and comparison phase was lost before a website visit even occurred. Second, sales lacked solid arguments for why the company’s own content should be cited or recommended by AI systems. From a user perspective, the website was structured well enough; from a machine perspective, it was too loosely connected, too imprecise, and insufficiently supported by semantic relationships.
Solution Approach
For the project, Zeno Visibility was used as the platform for AI visibility and semantic authority building. The crucial factor was that not only was the current state measured, but a content infrastructure could also be derived directly from the monitoring. The team started with the Zeno Visibility Research Engine. This simultaneously checked brand presence in ChatGPT, Gemini, Perplexity, Claude, and Copilot and determined a baseline-ready Semantic Authority Score for 48 prioritized search intents. This made it clear for which topics the brand was already functioning as a source and where the models were instead falling back on external sources.
Based on this analysis, the team used the Authority System Builder to develop a content system for the three most important business clusters. For each cluster, search intents were modeled across the entire journey: informational, comparative, problem-oriented, and decision-oriented. From a core keyword, each cluster produced a connected set of more than 100 semantically linked assets, including hub pages, blog articles, FAQs, comparison pages, case studies, and social variants. In the pilot, 32 assets were initially prioritized and implemented in an 8-week program.
From a technical standpoint, the automatic generation of Schema.org JSON-LD and an internal linking structure that connected terms, entities, and topic clusters in a machine-readable way was especially important. Instead of isolated articles, a knowledge graph was created within the content system. Output was delivered CMS-ready directly into WordPress; individual formats were also exported for Contentful workflows. This significantly reduced coordination between strategy, editorial, and web teams. In this setup, Zeno Visibility was not just a monitoring tool, but the foundation for an operationalized GEO strategy: measure, prioritize, build, publish, measure again.
Results
After 12 weeks, the first stable effects became visible. The Semantic Authority Score for the prioritized clusters increased from 31 to 67 points. In the AI systems, the likelihood of being mentioned as a source or recommendation for relevant prompts rose from 6% to 24%. The comparison and decision stage performed particularly well: for five tested core prompts, the brand appeared in the top answers of the models for the first time in four cases.
The effects also remained visible in classic SEO. Organic sessions on the newly built cluster pages increased by 142%, and the click-through rate on hub pages rose by 1.9 percentage points. The number of qualified leads from organic content increased over the same period from an average of 9 to 21 MQLs per month. At the same time, production time per asset dropped from around 10 hours to 3.5 hours because structure, linking, metadata, and export formats were prepared automatically. Based on the saved agency and editorial costs, the pilot generated an estimated annual cost efficiency of around 58,000 euros; after six months, return on investment was approximately 3.4x.
Lessons Learned
Summary
With Zeno Visibility, the customer successfully made the transition from classic SEO to GEO in practice: from fragmented content to a semantically connected authority system. Through structured AI Visibility Monitoring, prioritized intent clusters, and automated content delivery, both mentions in AI systems and the organic pipeline increased. The project showed that AI visibility does not come from monitoring alone, but from the systematic development of machine-readable authority.