LLM Visibility Monitoring in Mechanical Engineering: How Zeno Visibility Made Brand Mentions Measurable in Large Language Models
LLM Visibility Monitoring in…
Situation
Hansa Maschinenbau GmbH is a mid-sized manufacturer of custom machinery for the packaging, food, and chemical industries, with around 820 employees and annual revenue of just under €140 million. The company sells complex, explanation-intensive solutions through a network of direct sales, system integrators, and specialist dealers in Germany, Austria, and Switzerland. In classic search engine marketing, Hansa was well positioned: for several product and problem-related keywords, the domain ranked on page 1, and individual expert articles generated steady organic traffic.
Despite this visibility, a new problem emerged: in the marketing team’s internal tests, the brand appeared only rarely as a recommended solution in large language models. Out of 50 standardized queries on topics such as “custom machine packaging line” or “supplier for hygienic conveyor system,” Hansa was mentioned in just 14% of responses across ChatGPT, Gemini, Perplexity, Claude, and Copilot. Competitors with weaker traditional SEO performance appeared more often because they were semantically linked more clearly to the relevant use cases. For a company with long sales cycles and a high level of consultative selling, this was strategically important: more and more decision-makers were using LLMs in the early research phase, before filling out a contact form or requesting a sales call.
Challenge
The real issue was not a lack of content, but a lack of machine-readable authority. Hansa regularly produced expert articles, product pages, and trade fair reports, but the content was spread across multiple subdomains and CMS structures. In addition, there was no systematic build-up of semantic clusters, FAQ structures, or linked reference content.
As a result, LLMs could recognize the brand in isolated instances, but could not reliably classify it as a trusted source for a specific use case. This led to three effects: first, brand mentions in LLM responses declined, even though organic reach remained stable. Second, competitors were more frequently named as the “best option” or “leading provider.” Third, there was no reliable monitoring in place to measure this development. The marketing team could report traffic and rankings, but could not answer how visible Hansa actually was in ChatGPT, Perplexity, or Gemini. This exact gap made professional AI visibility monitoring necessary.
Solution
Hansa chose Zeno Visibility because the platform does not just measure visibility, but also systematically supports the development of semantic authority. The rollout took place in three steps over a twelve-week period.
First, the team used the Research Engine to set up monitoring across five LLMs: ChatGPT, Gemini, Perplexity, Claude, and Copilot. To do this, 60 prioritized prompts were defined that reflected real search and buying intent, such as conveyor technology, hygienic design, retrofit, OEE optimization, and system availability. Zeno Visibility captured for each model whether Hansa was mentioned, recommended, or linked to a specific source. This resulted in a Semantic Authority Score as the core KPI for AI visibility.
In the second step, Hansa used the Authority System Builder. For eight core keywords, the platform generated a complete authority system with more than 100 interconnected assets per topic area: hub pages, comparison pages, case studies, FAQ pages, blog articles, social posts, and use-case-driven deep dives. The key factor was not quantity alone, but semantic structure. Zeno Visibility automatically created internal linking logic, Schema.org JSON-LD, and CMS-ready exports for WordPress and Contentful. This made it possible to integrate the system into the existing website without extensive manual editorial work.
In the third step, the content was rolled out with a clear GEO focus: problem-oriented entry points, precise terminology, solid technical arguments, and references from mechanical engineering. The team first published the topics with the greatest market potential and connected them with product, industry, and comparison pages. This created a closed authority network that was readable for both search engines and LLMs. At the same time, results were reviewed weekly in the monitoring board and adjusted whenever a model still misclassified certain topics.
Results
After twelve weeks, a clear before-and-after effect became visible. The brand mention rate in the tested LLMs rose from 14% to 39%. The trend was particularly strong in Perplexity and Gemini, where Hansa was not only mentioned more often in relevant queries, but was directly classified as a recommended provider in 27% of cases. The Semantic Authority Score increased by 46 points over the same period.
Marketing KPIs also improved measurably: qualified inbound inquiries from organic and AI-adjacent research sources increased by 31% compared with the previous quarter. The newly built authority system pages had an average dwell time 28% higher than the existing expert articles. In sales, 19 additional sales-qualified leads were documented that explicitly referenced research carried out with ChatGPT or Perplexity during initial conversations.
Based on the additional pipeline, Hansa estimated the direct return on investment of the initiative at 3.4x after three months, relative to content and implementation costs. The most important effect, however, was strategic: for the first time, the company was able to manage AI visibility monitoring as a distinct control area alongside SEO, paid media, and PR.
Lessons Learned
Summary
With Zeno Visibility, Hansa Maschinenbau made its previously invisible presence in large language models measurable and significantly increased brand mentions within twelve weeks. The decisive lever was not more content in general, but a systematic build-up of semantic authority through monitoring, structure, and markup. For B2B companies in mechanical engineering, this case shows that AI visibility monitoring is not a reporting topic, but an operational component of modern demand generation.