A practical route through supplier visibility

After the course, you will be able to audit how AI systems describe an industrial supplier and identify whether the brand is cited, confused, weakened, or omitted. You will learn to inspect the source material behind those answers: product pages, application notes, distributor references, certification pages, comparison content, and trade references. The course shows how to structure technical content so a language model can quote it without flattening the product meaning. You will also learn how German and English content choices affect supplier discovery, how crawl and schema issues can restrict access, and how to maintain visibility as products, standards, competitors, and source pages change. The goal is a more reliable public evidence base, not a magic ranking position.

The program moves from foundations to execution. First, we define GEO for the German industrial market and examine how AI systems retrieve, cite, summarise, and compare supplier sources. Then we work through entity structure, product and application clarity, citation quality, multilingual decisions, technical access, and measurement. The final part brings these pieces into a complete campaign model and a maintenance routine.

You should know your company's product range, customer segments, and current website structure. Familiarity with product pages, technical documentation, distributor relationships, and search traffic will help, but no AI engineering background is required. I introduce technical terms only when they change what a supplier should write, mark up, cite, or maintain. Bring your own pages to mind as you read; the course works best when every concept has a real page attached to it.