Technical communication before AI visibility
I teach GEO from the floorboards of industrial communication: catalogues, manuals, application notes, category pages, certification references, and supplier descriptions that have to stand up to technical reading. My work begins with observed retrieval failures, then turns them into practical publishing exercises for manufacturers whose product meaning must remain precise across websites, catalogues, distributor listings, and AI-generated answers.
Elias Vormann
GEO teacher for industrial suppliers
If a model repeats your product identity badly, the first question is evidence, wording, and category fit.
A composite teaching case begins with a maintenance team asking an AI system for sealing components for a factory line. The answer looked tidy at first glance: a few large marketplaces, some general suppliers, and a confident paragraph about availability. Then the details began to wobble. Two specialist manufacturers were missing. One actual manufacturer was described as a distributor. A narrow component category had been widened until it no longer matched the engineering problem. For a human buyer with enough domain knowledge, the mistake was visible. For a rushed buyer collecting names, it could easily become the shortlist.
I am from northern Germany, from a town where workshops, catalogues, and export invoices felt more familiar than advertising slogans. I came into industrial communication through manuals, product sheets, and application notes. In that kind of writing, a misplaced term changes how an engineer reads a product, which applications seem safe, and which supplier appears competent. Over 19 years I wrote and edited documentation for component manufacturers, built category pages for specialist suppliers, and helped export-oriented companies make technical content more legible to buyers outside their immediate network.
When AI systems began turning source material into supplier recommendations, I recognised the old documentation problems in a harsher light. A vague product page could become the reason a model erased the company, borrowed authority from a nearby distributor, or quoted an outdated catalogue instead of a current technical page. That is why I moved into AI visibility work for industrial supplier discovery. My position is fairly plain: GEO is the discipline of making product identity, category fit, evidence, and practical use cases clear enough that people and models can repeat them without distortion. I opened this course for German industrial suppliers because many of them already have the substance. What is often missing is the source discipline that lets AI systems recognise it.
How I teach
I start with a concrete failure before I define the term behind it. A supplier is missing from a generated shortlist. A precision component maker is widened into a general manufacturer. A distributor page carries more authority than the manufacturer's own documentation. An old catalogue becomes the citation for a current product line. From there, we slow down and ask what the model could safely infer from the available sources. Each lecture separates what a human buyer understands from what a language model can support with evidence. Then we turn the diagnosis into a publishing task: rewrite a category page, clarify an application note, improve a comparison page, expose certification evidence, or repair a crawl and schema problem. The method is strict because the market is strict. Industrial buyers need product meaning that holds still under pressure.
Learn GEO through the mistakes industrial suppliers actually face.
Start with the program, then move through the lectures as practical audits of your own material.