Brand Mentions in LLMs for a Service Provider in the Energy Industry: Zeno Visibility Optimizes GEO for Citable Answers
Brand Mentions in LLMs for a Service…
Situation
A German energy services provider with around 420 employees and revenue in the mid-double-digit millions wanted to expand its visibility beyond traditional search engines. The company advises industrial and commercial clients on energy procurement, load management, energy efficiency, and CO₂ reporting in Germany, Austria, and Switzerland. SEO was already established, rankings for transactional core terms were solid, but the brand appeared only rarely in AI answer systems.
At the start of the project, brand mentions in a standardized prompt set of 50 purchase-relevant search queries stood at just 6 percent. The company was cited as a source in 2 percent of the answers. At the same time, the share of queries in which decision-makers no longer used Google, but instead relied on ChatGPT, Perplexity, or Gemini to generate initial vendor lists, was increasing. The content team consisted of two people, worked with external freelancers, and produced content mostly in isolation for each landing page. There was no systematic GEO Generative Engine Optimization.
Challenge
The core problem was not a lack of content, but a lack of semantic authority. The company’s content covered individual topics, but not the chains of questions that LLMs need for citation-worthy answers: What is the difference between energy purchasing and energy procurement? Which providers are suitable for mid-sized manufacturing businesses? Which criteria point to a service provider in energy management?
This created three effects. First, LLMs could not easily recognize the brand as a clearly categorized provider. Second, the company hardly appeared in comparison and shortlist answers. Third, the sales funnel shifted: prospects came into contact later, were better informed in advance, and more often named competitors that were more present in AI answers. For marketing, SEO, and sales, this turned a visibility problem into a pipeline problem.
Solution
For the project, Zeno Visibility was used as a platform for GEO, not just as a monitoring tool, but as infrastructure for building AI Authority. The goal was to establish the brand as a reliable source in the major LLMs and increase the likelihood that models would mention or cite it in response texts.
The starting point was the research engine. Zeno Visibility monitored brand presence in parallel across ChatGPT, Gemini, Perplexity, Claude, and Copilot and assessed the status with a Semantic Authority Score. The initial score was 34 out of 100 points. In addition, a prompt cluster of 32 prioritized search intents was defined, divided into six topic areas: energy procurement, load management, efficiency consulting, CO₂ reporting, tender support, and provider comparison.
On this basis, the Authority System Builder generated a complete authority system with 114 semantically interconnected assets. These included hub pages, comparison pages, FAQ articles, case studies, glossary content, and short social assets. All content was exported CMS-ready into WordPress and additionally enriched with Schema.org JSON-LD, an internal linking structure, and distinct entities. The focus was deliberately on citation-worthy answers: clear definitions, structured comparisons, verifiable statements, and consistent terminology.
An editorial workflow ensured that the specialist department, legal team, and sales approved the content. Within six weeks, the first content system was live. After that, the content was iteratively adjusted whenever the research engine identified gaps in LLM answers or inconsistent brand associations.
Results
After 90 days, a measurable effect was visible across all observed LLMs. Brand mention in the same prompt set rose from 6 to 39 percent. Mention as a cited or referenced source increased from 2 to 18 percent. The Semantic Authority Score improved from 34 to 67 points.
The effect was particularly strong in Perplexity, where brand mention in the prioritized comparison and shortlist prompts increased from 8 to 44 percent. In ChatGPT, mentions in the defined queries rose from 4 to 29 percent. At the same time, classic reach KPIs also improved: the new GEO content generated 31 percent more organic impressions and 24 percent more clicks to thematically adjacent pages.
The pipeline was also affected. During the observation period, seven qualified leads with a connection to AI-assisted research were recorded, including two opportunities with an expected annual contract value of around EUR 240,000 in total. With project costs of EUR 68,000, this resulted in a pipeline-to-cost ratio of 3.5:1. A purely content-driven approach would not have achieved this effect; the decisive factor was the combination of monitoring, semantic structuring, and machine readability.
Lessons Learned
Summary
With Zeno Visibility, the energy services provider was able to significantly increase its visibility in LLMs within 90 days and was mentioned more frequently in citation-worthy answers. The key was building semantic authority through a complete content system rather than individual SEO pages. For B2B companies in the DACH region, this case shows how GEO Generative Engine Optimization can be operationalized and measured.