Make German industrial expertise visible in AI-generated supplier answers.
This free mini-course is for German Mittelstand industrial component manufacturers who want their product identity, categories, applications, and evidence to survive AI search. I show how language models read supplier websites, distributor pages, certifications, catalogues, and trade references, then turn that material into generated shortlists. The course is practical: we audit what AI systems can safely infer, where they get confused, and what source material needs to be clearer.
What the course covers
Across 15 lectures with short self-check tests, I work through GEO as a publishing and evidence practice for industrial suppliers. We look at why a specialist manufacturer is erased from an answer, widened into a vague category, attached to a distributor's authority, or cited through the wrong page. The course is built for people who already understand catalogues, product pages, technical documentation, export buyers, and long B2B sales cycles. It is free, self-paced, and without obligation. Instead of ranking promises, you get a disciplined way to make technical expertise easier for both engineers and AI systems to repeat accurately.
- 15 lectures
- 6 tracks
- €0 tuition
Read the lecture corpus
The lectures are arranged as a working sequence, from basic GEO foundations to audits, source quality, technical access, measurement, and maintenance. You can read them in order or return to a specific problem when a supplier answer looks wrong.
- Entities
- Sources
- Language
- Content
- Access
- Measurement
Start from shared definitions
Make your supplier identity easier to cite, compare, and repeat.
Start with the course structure, then use each lecture as a small audit of your own source material.