AI Visibility Monitoring in B2B SaaS: How Zeno Visibility Restructured a Software Provider’s Visibility in Generative Answers
AI Visibility Monitoring in B2B SaaS…
Situation
The fictional Astera Systems GmbH is a German B2B SaaS provider of software for quote, contract, and approval automation in the mid-market segment. The company employs 128 people, generates around €14 million in annual revenue, and operates with an inbound-heavy go-to-market model. For a long time, the organic channel had been a major pillar: around 34% of demo requests in 2024 came from SEO traffic, primarily via comparison, advisory, and category pages.
In spring 2025, however, a structural problem became apparent. The brand was easy to find in traditional search results, but appeared only rarely as a recommendation in generative answers. Internal tests with 30 priority prompts on topics such as “best contract software for the mid-market” or “alternatives to X” showed that Astera Systems was mentioned in fewer than 10% of responses. In most cases, ChatGPT, Gemini, Perplexity, Claude, and Copilot recommended competitors with a more extensive semantic footprint. The company had content, but no systematic AI Visibility Monitoring and no robust structure for generative visibility.
Challenge
The core problem was not lack of reach, but lack of machine-readable authority. The existing content had grown over several years, but was organized in silos: blog, product pages, PDFs, and knowledge base were only partially linked internally, Schema.org markup was largely missing, and key entities such as product categories, use cases, and comparison terms were not consistently connected.
The consequences were measurable. In generative answers, the brand was rarely included as a trusted source, even when user queries clearly matched the product category. This affected early-stage buying phases, where LLMs are increasingly used as research and pre-selection tools. At the same time, internal effort increased: marketing, SEO, and product marketing had to develop individual assets manually for every new campaign, without knowing which topic areas were already covered in the models. What was missing was both visibility measurement and a system for closing semantic gaps in a targeted way.
Solution Approach
Astera Systems decided on a two-stage approach using Zeno Visibility as the platform for AI Visibility Monitoring and for building semantic authority. The goal was not only to measure the brand’s presence in generative answers, but also to create the content and technical foundations needed to ensure the brand would be referenced more often and more consistently in LLM responses.
In the first step, the team used Zeno Visibility’s research engine to measure brand presence across ChatGPT, Gemini, Perplexity, Claude, and Copilot in parallel. To do this, 36 core prompts from 8 topic clusters were defined, including product comparisons, use cases, buying criteria, and vendor lists. The monitoring delivered a baseline Semantic Authority Score as well as a visibility matrix by model and intent. Particularly noticeable were gaps in comparison and alternative queries, as well as explanatory questions about the category itself.
In the second step, the Authority System Builder was used to create a complete content system for 12 prioritized keywords. For each keyword, the platform generated a semantically connected set of more than 100 building blocks: hub pages, FAQ blocks, comparison pages, use-case pages, case study formats, glossary elements, and social assets. The advantage was not the volume, but the structure: content was organized around clear entities, questions, and decision patterns so that LLMs could more easily identify the brand as a relevant source.
At the same time, Zeno Visibility generated Schema.org JSON-LD and an internal linking structure designed for knowledge-graph anchoring and improved machine readability. The content was published directly in WordPress and Contentful; only the technically critical parts were reviewed editorially. This reduced production time per topic cluster from an average of 8 to 10 working days to around 2.5 days. Within six weeks, 94 new or revised content assets were published, including 14 comparison pages and 23 FAQ modules.
Operationally, the key was coupling monitoring and content creation: the research engine not only showed where Astera Systems was invisible, but also which semantic terms and sources the models preferred in their answers. These insights fed directly into the content architecture. That was the key difference from classic SEO audits: not just rankings, but generative answer patterns became the control mechanism.
Results
After 90 days, clear effects were visible in AI Visibility Monitoring. The Semantic Authority Score rose from 31 to 67 points. Across the 36 monitored prompts, Astera Systems had previously been mentioned in 9% of responses; after implementation, that figure reached 41%. The strongest improvement was in comparison and alternative queries, where the mention rate increased from 4% to 29%. In Perplexity and Gemini, there was also a rise in how often Astera Systems was positioned as “suitable for the mid-market” or as “one of the relevant providers.”
There were also visible effects on traffic and lead quality. Clicks to the newly structured pages increased by 27% compared with the previous quarter, while demo requests from organic and AI-adjacent entry points rose by 18%. The share of content that received its first generative mentions within 30 days of publication was 62%. At the same time, the editorial and SEO team reduced manual production effort by around 45%. Based on saved agency and internal costs, this resulted in an estimated positive ROI within four months.
Lessons Learned
Summary
With AI Visibility Monitoring, Astera Systems was able for the first time to understand how its brand appears in generative answers and where specific semantic gaps exist. By using Zeno Visibility, these gaps were not only analyzed but systematically translated into a scalable content and authority structure. The result was a measurably stronger presence in LLM answers, better organic performance, and a significantly more efficient content process.