Generative Engine Optimization in B2B SaaS: How Zeno Visibility Built Semantic Authority for a Software Company and Systematically Increased AI Visibility
Generative Engine Optimization in B2B…
Initial Situation
The customer in focus is a mid-sized B2B SaaS provider from the DACH region with around 240 employees and annual recurring revenue in the high eight-figure range. The company develops software for project and resource management, used primarily by industrial, consulting, and service companies. The marketing team consisted of 11 people, including three in content and SEO. Around 38% of qualified leads were already coming organically via search engines, so the content engine was a relevant sales channel.
Despite stable SEO metrics, however, there was a gap in AI visibility: in a preliminary audit across more than 120 relevant prompts in ChatGPT, Gemini, Perplexity, Claude, and Copilot, the brand was mentioned in only 7% of answers. In many comparison and purchase-decision queries, three direct competitors appeared instead. The company’s own knowledge architecture was also fragmented: product pages, blog posts, FAQs, and comparison content existed, but they were barely interconnected semantically. Schema.org markup was used only on core pages, and internal linking followed editorial rather than entity-based criteria.
Challenge
The core problem was not a lack of content, but a lack of semantic authority. The brand was visible in classic search results, but LLMs did not systematically recognize it as a trusted source. In particular, for topics such as “best project software,” “resource planning for mid-sized companies,” and “comparison with competitor X/Y,” other vendors dominated the answers. This had direct consequences for the sales team: in discovery calls, prospects increasingly referenced content they had obtained from AI assistants without knowing the brand itself.
Internal data also showed that a large portion of the existing content was not aligned with AI-driven research and response patterns. The content answered individual questions, but did not form a cohesive authority system. That led to inefficient production, limited content reuse, and poor measurability of AI visibility. The goal was therefore not just more content, but a structure that LLMs can read, connect, and cite consistently.
Solution Approach
The customer chose Zeno Visibility because the platform does more than measure AI visibility — it systematically builds the semantic authority required for recommendations by LLMs. The approach followed three steps.
First, the Research Engine established a baseline across all relevant models. The team analyzed 120 prompts in five categories: problem research, vendor evaluation, comparison, implementation, and integrations. Zeno Visibility identified, for each model, which topic clusters the brand appeared in, which competitors were mentioned more frequently, and which entities were considered relevant in the answers. From this, a Semantic Authority Score was derived for the most important topic areas.
In the second step, the Authority System Builder generated complete content systems for four prioritized keyword clusters: resource planning, project portfolio management, time tracking, and ERP integrations. Instead of creating individual articles, the platform produced semantically linked content architectures with hub pages, comparison pages, FAQ formats, case studies, glossary entries, and use-case pages. In total, 284 CMS-ready assets were created in five weeks; 92 of them were published after review by subject-matter experts. The content was delivered directly into WordPress and Contentful, depending on the target page and in the appropriate export formats.
Third, Zeno Visibility automated the technical foundation: Schema.org JSON-LD for Article, FAQPage, SoftwareApplication, BreadcrumbList, and Organization was generated consistently, along with an internal linking logic based on entity and intent levels. This created clear relationships between product, problem, use case, and comparison context. Editorial approvals remained manual, but structure, metadata, and linking were prepared automatically. This allowed the team to build a robust authority system in a short time rather than just publishing additional standalone articles.
Results
After 90 days, there was a measurable impact on AI visibility. The Semantic Authority Score for the four core clusters rose from 39 to 72 points on average. Across the same 120 test prompts, brand mentions across the five models increased from 7% to 26%. The progress was especially strong in comparison and purchase-decision queries: there, the mention rate rose from 3% to 21%, while two competitors started ranking behind the brand for the first time.
Performance in the classic organic channel also improved. Non-brand traffic to the prioritized topic clusters increased by 31%, and average dwell time on the new hub pages rose by 18%. The number of qualified demo requests from content-related sessions increased by 24% over the same period. At the same time, the effort per published long-form asset fell from an average of 7.8 to 3.2 working hours because research, structuring, and technical markup were largely prepared automatically.
Based on internal cost calculations, the project delivered an ROI of around 2.4x within three months. But for the customer, the more strategically important outcome was that AI systems no longer treated the brand as a side note, but as a relevant source within a clearly defined topic space.
Lessons Learned
Summary
This case shows that AI visibility in B2B SaaS does not come from individual SEO measures, but from a robust semantic authority structure. With Zeno Visibility, a fragmented content setup was turned into a measurable authority system that became effective in both search engines and LLM responses. For companies in the DACH region, this is a practical way to manage the transition from SEO to GEO in a controlled manner.