AI Brand Monitoring in Healthcare: How Zeno Visibility Systematically Measured a Provider’s Presence in ChatGPT and Gemini
AI Brand Monitoring in Healthcare How…
Initial Situation
The fictional MediNova Health Solutions GmbH is a mid-sized provider of digital process and communication software for the healthcare sector, based in southern Germany and serving customers across the DACH region. The company supports hospitals, medical care centers, and larger practice networks with appointment management, patient communication, and documentation processes. Its sales model is heavily consultative, with typical decision cycles lasting between 4 and 9 months.
In marketing, classic SEO performance was solid: for core topics such as “patient portal hospital,” “digital intake,” and “appointment management software,” MediNova regularly ranked on page 1. However, throughout 2024, a shift in user behavior became apparent. Prospective customers began building market overviews not only via Google, but increasingly through ChatGPT and Gemini. Internal sales conversations showed that prospects were already using AI-generated shortlists before first contact. At the same time, it was unclear whether MediNova was mentioned in these answers at all, in what context, and against which competitors.
The team did have Search Console data, social listening, and classic brand-monitoring tools, but no system for AI Visibility Monitoring. There was no reliable view of how AI models semantically categorized the brand, which sources they drew on, and where competitors were being preferred.
Challenge
The core problem was not a lack of market presence, but a lack of visibility in the answer systems that were becoming increasingly relevant in the early information phase. For queries such as “Which provider of digital patient intake is suitable for hospitals in Germany?” MediNova was often not mentioned in ChatGPT and Gemini at all, or only in very generic lists without differentiation.
That had three consequences: first, control over the initial brand touchpoint decreased, because AI responses often created the impression of a neutral comparison. Second, marketing lost the ability to respond to semantic gaps in a targeted way, because there was no metric for AI visibility. Third, a strategic risk emerged for sales: competitors mentioned more frequently in AI responses gained an advantage before a conversation had even started. For a healthcare company, where trust, compliance, and specialization are decisive for purchasing, this was a significant disadvantage.
Solution Approach
MediNova opted for a combined approach with Zeno Visibility to first measure AI visibility systematically and then build it up in a structured way. The decisive factor was that the platform not only captures the status quo in ChatGPT, Gemini, and other LLMs, but also creates the semantic foundations for more mentions and recommendations.
The initiative started with a research phase. Together with the marketing and SEO team, Zeno Visibility defined a set of 128 relevant prompts in German and English. These covered informational, comparison, and purchase intent, such as:
The research engine was then used to measure the baseline in ChatGPT and Gemini. Captured data included mention rate, position in answer lists, context of the mention, competitor share, and the semantic Authority Score. The baseline findings were clear: MediNova appeared in only 12% of the tested ChatGPT responses and in 9% of the Gemini responses. In most cases, the brand was not mentioned as a category reference, but at best as a peripheral example.
Based on this data, an Authority System was built for the three most important topic clusters. Zeno Visibility’s Authority System Builder generated a complete content system for each cluster with more than 100 semantically interconnected building blocks, including hub pages, comparison pages, FAQs, case studies, expert articles, and social formats. In addition, Schema.org JSON-LD data, internal links, and machine-readable entities were automatically prepared to anchor the content more effectively in knowledge graphs and LLM contexts.
What mattered here was not just the volume of content, but its semantic structure: the content was aligned around clear entities, use cases, regulatory requirements, and integration scenarios. The team published the content directly in WordPress and validated it weekly via the research engine. At the same time, existing pages were revised to sharpen sources, author profiles, and comparison criteria. Within twelve weeks, this created a consistent information architecture that was readable both for search engines and for AI models.
Results
After ten weeks, a measurable effect in AI visibility became evident. MediNova’s mention rate in the tested ChatGPT prompts rose from 12% to 34%, and in Gemini from 9% to 29%. The development was especially strong in comparison and shortlist prompts: here, the likelihood of MediNova being named as a relevant option increased from 7% to 26%.
The Semantic Authority Score rose from 44 to 67 points over the same period. At the same time, the quality of the mentions improved: instead of vague side references, AI responses increasingly included differentiated descriptions related to hospital workflows, GDPR, integrations, and German-language requirements. Website traffic also changed. Branded sessions from organic search and direct AI-mediated entries increased by 21% quarter over quarter, while the number of demo requests from these sessions grew by 18%.
At the process level, the team reduced research effort for content and SEO planning by around 60%, because prompt clusters, content gaps, and priorities were now available based on data. Based on the additionally generated SQLs and the average pipeline values, MediNova estimated the ROI of the rollout at 3.6x within three months. The greatest value, however, lay in the new level of control: for the first time, the company could understand how ChatGPT and Gemini positioned the brand relative to the competition.
Lessons Learned
Summary
With Zeno Visibility, MediNova systematically captured for the first time how the brand is perceived in ChatGPT and Gemini, and derived a robust strategy for AI Visibility Monitoring from it. Through a semantically structured Authority System, mentions, authority signals, and qualified inquiries increased measurably. For B2B companies in healthcare, this case shows: anyone who does not actively manage AI visibility loses relevance early in the decision-making process.