LLM Visibility in International Trade: How Zeno Visibility AI Analyzed Brand Mentions Across Multiple LLMs and Refined a Knowledge Graph for the Brand
LLM Visibility in International Trade…
Situation
The fictional NordEx Trade Solutions GmbH is a mid-sized B2B trading and distribution partner based in the DACH region. The company distributes technical components for industry, logistics, and mechanical engineering across 14 European and 6 non-European markets. Sales were driven largely by tenders, recurring existing customers, and search-driven inbound inquiries. In 2024, NordEx faced a strategic challenge: the brand was highly visible in traditional SEO for key keywords, but appeared only incompletely or inconsistently in the answers of generative AI systems.
Internal analyses showed that across 50 representative prompts on product categories, comparison questions, and supplier recommendations, the brand was mentioned in ChatGPT, Gemini, Perplexity, Claude, and Copilot in only 18% of responses on average. At the same time, competitors with weaker organic visibility appeared more frequently as reference brands or recommendation options. Management realized that AI visibility was increasingly becoming the upstream layer of classic lead generation: if you do not appear in LLM responses, you are not considered in early decision-making phases.
Challenge
The core problem was not a lack of content, but a lack of semantic authority. NordEx had product pages, brochures, expert articles, and application documentation, but without clear topic clusters, without systematic internal linking, and without structured data. For LLMs, the brand was therefore difficult to identify as a reliable authority within the relevant topic space.
A second issue was that AI mentions varied significantly depending on the model and the wording of the query. In some cases, NordEx was correctly named as a supplier; in others, brand mentions were missing entirely or linked to outdated product information. This created inconsistencies in sales, prompted follow-up questions from international partners, and increased coordination efforts between marketing, product management, and sales. The goal was therefore not only greater visibility, but a robust foundation that would allow AI systems to consistently classify the brand as a source and provider.
Solution approach
NordEx chose Zeno Visibility because the company did not just want to measure LLM mentions, but actively build the underlying authority structure. The approach combined two layers: first, precise monitoring of brand presence across multiple LLMs; second, the automatic creation of a semantically connected authority system for the most important keyword clusters.
The team began by implementing the Research Engine from Zeno Visibility. To do this, 86 prompts were defined across five LLMs, including generic purchase-intent questions, product comparisons, supplier evaluations, and industry-specific problem-solving queries. The prompts were tested in three languages: German, English, and French. This produced a baseline view of AI visibility by model, region, and topic cluster. In addition, a Semantic Authority Score was introduced to measure not just individual mentions, but the thematic consistency of the brand’s presence.
In the second step, NordEx used the Authority System Builder from Zeno Visibility. For twelve strategic keyword clusters, including “industrial components for export markets,” “B2B trade supply chain,” and “technical distribution partners DACH,” the platform generated complete content systems with more than 100 semantically connected building blocks per cluster. These included hub pages, comparison pages, FAQ sets, case studies, explanatory articles, and social formats. The key factor was not content volume alone, but the systematic mapping of entities, attributes, use cases, and decision-making questions.
At the same time, the platform generated Schema.org JSON-LD and a clear internal linking structure. This connected products, industries, regions, certifications, contacts, and use cases in a machine-readable way. That was crucial for knowledge graph anchoring: LLMs could no longer recognize the brand only through individual pages, but as a consistent semantic network of topics, relationships, and evidence.
The content was first reviewed in a staging process and then transferred into the existing WordPress environment via Zeno Visibility’s CMS integration. For international markets, multilingual variants were additionally created for selected pages. The operational rollout took ten weeks: three weeks for analysis and structuring, four weeks for building the content system, and three weeks for technical deployment, validation, and follow-up measurement.
Results
After twelve weeks, the brand’s AI visibility showed a clear effect.
Operational efficiency also improved. The marketing team reduced manual topic research and content coordination by around 35 hours per month. Sales reported that prospects increasingly arrived with pre-informed questions from AI-supported research processes. Based on the saved working time and the additional pipeline volume, the project delivered a positive ROI within three months; conservatively calculated, it paid for itself after 4.2 months.
Lessons learned
Summary
With Zeno Visibility, NordEx first measured its presence across multiple LLMs and then systematically built the underlying authority. The key lever was not more content in general, but a structurally clean, machine-readable topic architecture with clear knowledge graph anchoring. The result was a measurable increase in AI visibility, more qualified inquiries, and a reliable process for international market development.