AI visibility · an open course

The Course

Thirteen lessons on how AI answer engines decide what to say about a company, and how to measure it without fooling yourself. The course starts with how an answer gets built, works through the seven dimensions a reading scores, then covers the method: audit, correct, engineer, measure and monitor. The last lesson covers answers in German. Read it in order, or start with the lesson you need.

Free to read and to cite with attribution. New here? Start with the overview, or try the short version: audit your own AI visibility in an afternoon.

1. 01How AI Answers Are Actually BuiltYou can't change what AI says about you without knowing how it decides what to say. This is the machinery, in plain terms.
2. 02AEO, GEO, SEO: what's actually differentThree acronyms, one real question — are you in the answer? Here's how AI visibility differs from search, and how AEO and GEO fit together.
3. 03Recognition: does AI know you exist?Before an assistant can describe you well, it has to recognise you as a specific thing. Most visibility problems start here — and so does the fix.
4. 04Accuracy: is what AI says about you true?Once a model recognises you, the next question is whether it describes you correctly. Confident and wrong is the dangerous combination.
5. 05Competitor framing & share of voiceAI rarely describes you alone. It names a set — and where you sit in that set, and how often you're in it at all, is its own dimension.
6. 06Recommendation: the outcome you can't chase directlyBeing recommended is the goal — and the one dimension you can't work on head-on. You reach it sideways, through the others.
7. 07Documentation: writing so machines understand youThe outcome dimensions were what AI says about you. The machinery dimensions are how you change it — starting with the words a model actually reads.
8. 08Structured data: machine-true, and why schema isn't enoughSchema is the clearest signal you can hand an AI about what you are. It's also routinely oversold. Here's what it does — and what it can't.
9. 09Citation & authority: who AI trusts to describe youThe last dimension is the one least in your hands — the outside sources an AI trusts. It's also where the durable advantage is won.
10. 10The audit: measuring it without fooling yourselfSeven dimensions are only useful if you can measure them the same way twice. This is what separates an audit from a vibe.
11. 11Correct and Engineer: turning findings into fixesAn audit tells you what's wrong. These two stages turn that into a ranked list of changes — the record first, then the machinery.
12. 12Measure and Monitor: proof, then vigilanceA fix only counts if it moved the answer — and answers don't hold still. The last two stages are where the method earns its trust.
13. 13DACH: AI visibility in GermanMost AI-visibility advice is written for English. In German, the answer can be different — and far fewer companies are watching.

Between lessons, I post short Field Notes: quick observations from the field.