case-studyJune 18, 2026 ZENO Team 6 min read

LLM Brand Monitoring for an International Trading Company: Zeno Visibility Boosts the Semantic Authority Score in ChatGPT and Perplexity

LLM Brand Monitoring for an…

Initial Situation

The fictional Müller & Kahl International Trading GmbH is a mid-sized trading company with locations in Germany, the Netherlands, and Poland. The company distributes technical components and industrial supplies to B2B customers across Europe and North America. Annual online revenue stood at approximately €18 million, with around 42% generated through organically initiated leads. The content and SEO teams operated a well-developed hub-and-spoke setup, multiple product landing pages, and an active thought leadership section.

Despite stable rankings in traditional search engines, a new problem emerged: when users submitted product-related or advisory queries to LLMs such as ChatGPT and Perplexity, the brand was rarely mentioned. When it did appear, it was often without precise context — or with outdated information about the product range, target markets, and delivery capabilities. An initial benchmark placed the Semantic Authority Score for the core brand at 31/100. The brand appeared in relevant LLM responses in only 18% of tested prompts — 22% on Perplexity and 15% on ChatGPT.

For a company selling products that require explanation, this was strategically significant: a growing number of buyers were using LLMs not just for research, but for pre-selecting vendors, comparing markets, and building shortlists. Traditional SEO visibility was no longer sufficient.

Challenge

The core challenge was not a lack of traffic, but a lack of semantic authority within generative search systems. Müller & Kahl was well known within its industry, but information about the brand was fragmented across the website, PDFs, trade articles, product data, and external mentions. As a result, LLMs were unable to reliably establish the brand as a trustworthy source.

There was also a structural issue: content was published regularly, but not systematically optimized for LLM readability, entity recognition, internal semantic linking, or source coherence. This meant that individual pages might rank well, but no consistent brand image was being formed for models like ChatGPT, Perplexity, or Claude. The impact was tangible in sales: prospects were increasingly asking about competitors because they simply didn't encounter the brand in AI-generated responses.

The company therefore needed more than LLM visibility monitoring — it needed a mechanism to actively build the brand's semantic authority.

Solution Approach

For the pilot, Müller & Kahl chose Zeno Visibility because the platform addresses two requirements within a single system: LLM Brand Monitoring across multiple models and the development of measurable semantic authority at the content level. The goal was not only to measure current visibility, but to address the structural causes behind low LLM presence.

The implementation ran in three phases. The first phase involved a baseline audit using the Research Engine. A set of 64 relevant prompts was defined, organized into categories such as "comparing industrial components," "reliable suppliers in DACH," "alternatives to vendor X," and "brand relevance in procurement contexts." These prompts were tested in parallel across ChatGPT, Perplexity, Claude, Gemini, and Copilot. The team also captured entities, source patterns, and the frequency with which the brand appeared in responses, citations, and comparison lists.

In the second phase, the team used Zeno Visibility's Authority System Builder to generate a complete authority system for each keyword cluster. Across four core clusters, 116 semantically interconnected pieces of content were created within just a few days: hub pages, 18 FAQ articles, 12 comparison pages, 9 case study formats, 24 blog articles, 21 social assets, and additional glossary and use-case pages. All content was exported CMS-ready and deployed directly into WordPress and Contentful. At the same time, the platform generated Schema.org JSON-LD, internal link structures, and consistent entity references to improve machine readability.

In the third phase, content was iteratively refined based on LLM response patterns. Pages with a high likelihood of being cited received additional supporting references, clearer definitions, and stronger links to product, industry, and application pages. This process built a coherent semantic network within a single model — one that positioned the brand as a reliable source for AI systems.

Results

After 12 weeks, measurable improvements were visible across LLMs. The Semantic Authority Score rose from 31 to 68 points. On Perplexity, Müller & Kahl now appeared in relevant response patterns in 61% of tested prompts — up from 22%. On ChatGPT, the figure reached 54%, and on Claude 47%. The most significant gains were seen in advisory-style queries with a comparative or evaluative character.

Brand perception also shifted: rather than being mentioned solely as a supplier, the company increasingly appeared as an explanatory reference or a valid option on shortlists. Over the same period, the share of leads initiated through AI-assisted research grew by 28%. The sales team also reported a shorter pre-qualification phase, as prospects were entering conversations with a more precise understanding of the brand.

From a business perspective, the impact was equally relevant. Internal calculations estimated the combined value — from reduced content production effort and additional pipeline contribution — at approximately €74,000 over the pilot period. Content production was accelerated by around 35% through CMS export and structured templates.

Lessons Learned

LLM Brand Monitoring is not a reporting tool — it's a management instrument. Only by monitoring multiple models in parallel did it become clear where the brand was actually absent and in which contexts it appeared incorrectly or incompletely.

Semantic authority is not built through individual pieces of content, but through interconnected content systems. Hub pages, FAQs, comparisons, and case studies must be logically related to one another.

Structured data is not an add-on — it's a foundation. JSON-LD, clear entity definitions, and internal linking increase the likelihood that models will interpret content correctly.

Generative visibility must be operationally embedded in CMS workflows. Only then does insight become a repeatable process rather than a one-off campaign.

Success shows up early in response quality, not just in traffic. When LLMs cite a brand more precisely, lead quality typically improves first — traffic follows later.

Summary

With Zeno Visibility, Müller & Kahl did more than measure LLM visibility — the company systematically built the brand's semantic authority. Within 12 weeks, the Semantic Authority Score increased significantly, while brand presence in ChatGPT and Perplexity grew in measurable terms. For companies in the DACH region, this case makes one thing clear: in the age of AI, being recommended requires more than SEO rankings — it requires a robust authority structure.

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*This content was created with AI assistance and reviewed by a human editor.*

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