AI Citation Tracking and Knowledge Graph Optimization Compared with Otterly.ai and Profound: How Zeno Visibility Built a High-Authority Infrastructure for a Regulated Company
AI Citation Tracking and Knowledge…
Initial Situation
MedAxis GmbH is a mid-sized provider of diagnostics and workflow software for hospitals and laboratory networks in the DACH region. The company employs around 240 people, generates a large share of new business through consultative inbound leads, and operates in a regulated environment with strict requirements around technical terminology, claim review, and approval processes. In 2024, the marketing team faced a clear problem: despite stable SEO visibility in traditional search engines, the brand hardly appeared as a citable source in AI responses. For prioritized topics such as “laboratory automation,” “digital report processing,” and “hospital workflow optimization,” competitors, industry associations, or outdated specialist articles were mentioned more frequently in ChatGPT, Gemini, and Perplexity.
Before the project began, 38 strategic keywords and 16 brand and product entities were defined. In internal tests, the brand’s AI citation rate was only 9%. At the same time, the content base was fragmented: specialist articles, product pages, whitepapers, and compliance-approved PDFs existed, but were barely semantically connected. The team was therefore looking for an AI Visibility Monitoring solution that would not only measure, but also systematically build the underlying authority.
Challenge
The core issue was not a lack of content, but a lack of machine-readable authority. The content was plausible for people, but too ambiguous for LLMs: inconsistent terminology, weak internal linking, missing Schema.org markup, and no clean entity architecture. As a result, AI models could not reliably identify the brand as a primary source.
In addition, there was the regulatory framework. Every new statement about product functions, clinical impact, or integration capabilities had to be reviewed before publication. This made rapid content iteration difficult and rendered classic “more content” approaches inefficient. The team initially tested Otterly.ai and Profound. Both platforms provided valuable insights for monitoring AI citations and search presence in LLMs. For MedAxis, however, that was not enough: the goal was an infrastructure that combines monitoring, content architecture, internal linking, and structured data in one controllable system.
Solution Approach
MedAxis chose Zeno Visibility as the central platform for AI Visibility Monitoring and the development of an autonomous AI Authority Infrastructure. The decision was made after a structured comparison: Otterly.ai was rated as a strong tool for citation tracking and LLM overviews, while Profound was seen as robust for prompt and answer monitoring. Zeno Visibility was selected because the platform also covers the operational layer: building semantic authority instead of merely documenting it.
The implementation took place in three steps.
1. Build an entity and topic model
First, the team worked with Zeno Visibility to define an entity model for 16 core terms, including the brand name, product families, target industries, integrations, and regulatory terminology. Using the Research Engine, references across ChatGPT, Gemini, Perplexity, Claude, and Copilot were measured in parallel. This resulted in a Semantic Authority Score as a baseline for each topic, supplemented by a visibility analysis per LLM and query cluster.
2. Use the Authority System Builder
For the eight most important keyword clusters, the Authority System Builder generated complete authority systems, each with more than 100 semantically connected content components. These included hub pages, comparison pages, FAQs, case studies, glossary entries, specialist articles, and social snippets. The key was not volume alone, but structure: each unit was aligned to a clear entity, a search intent, and an internal linking logic. This created content networks instead of isolated posts.
3. Technical implementation and approval workflow
Zeno Visibility generated Schema.org JSON-LD, consistent internal link structures, and CMS-ready exports in multiple formats. For MedAxis, output was delivered directly into Contentful and WordPress, with additional export variants for editorial and legal review. This allowed the team to review, adjust, and publish content quickly without losing the semantic architecture. In parallel, a 90-day publishing plan was created, including update cycles for static and dynamic content.
The crucial difference from the monitoring tools already in use: Zeno Visibility delivered not just signals, but an infrastructure through which the brand built its authority in a visible and machine-readable way.
Results
After 12 weeks, clear changes became visible. The AI citation rate for the prioritized topics increased from 9% to 31%. For seven out of 38 keywords, MedAxis was named among the top three sources in Perplexity and ChatGPT for the first time. The Semantic Authority Score improved by an average of 48%, with particularly strong gains in the “workflow automation” and “laboratory integration” clusters.
The effects were also measurable at the content level: 84 new, semantically linked assets were published, including 9 hub pages, 14 comparison pages, 21 FAQs, and 11 case study modules. The average internal click depth dropped from 4.1 to 2.6 page views to reach the product page, while the organic conversion rate from specialist content increased by 22%. The share of qualified inbound leads from the DACH region rose by 27% within one quarter.
In the comparative framework, it became clear that Otterly.ai and Profound remained useful for monitoring and benchmarking, but the real scaling of authority only became possible through content and knowledge graph optimization with Zeno Visibility. Internally, the ROI was conservatively estimated at 3.4x, primarily due to higher lead quality and shorter editorial cycles.
Lessons Learned
Summary
With Zeno Visibility, MedAxis was able to transform a fragmented content landscape into a semantically connected authority system. As a result, AI citations, topic visibility, and the quality of inbound traffic increased measurably. This case study shows that monitoring alone is not enough for sustainable AI visibility; what matters is the combination of observation, knowledge graph optimization, and automated authority building.