„Which business near me is any good?“ increasingly lands with an AI rather than with Google. Its answer is short, names two or three businesses, and one of them should be yours.
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Two local service businesses with measurable mentions in AI answers: 265 and 344 AI citations.
4,358 org. traffic | €6,440 value Local SEO for a Dresden funeral home. Built from scratch with 808 ranking keywords and €6,440 traffic value.
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7,392 Clicks | €2,246 Value Complete SEO buildup for a career service. Content strategy and technical optimization led to €2,246 traffic value and 344 AI Citations.
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First measure what the AI says today, then put the sources in order.
I ask the AI systems exactly the questions your customers would ask, with a location reference, and record who gets named and who does not.
Then I check what those answers rely on: Maps listings, review platforms, directories, editorial mentions.
Contradictory business data is the most common reason a model cannot confidently identify a business. That gets unified and made machine readable.
A model needs reasons to recommend you. Reviews, concrete service descriptions and mentions in the right places provide those reasons.
The same prompts, the same conditions, at fixed intervals. That shows whether your mentions actually change.
Wherever people look for a recommendation rather than a list.
With urgent needs people increasingly ask the AI, because it names a business directly instead of ten links. That moment decides who gets the call.
In health and law AI systems are especially careful and only name sources they are confident about. Completeness and consistency are the entry ticket here.
Recommendation questions are the norm here. Answers are built almost entirely from reviews and descriptions other people wrote about you.
Pick an area to see what it actually involves.
First we measure rather than guess. I test the questions people in your area really ask, across several AI systems, and document who gets named.
For local recommendations Maps is very often the first source a model reaches for. So your listing has to be unambiguously readable not only for Google but for an AI.
Different spellings of name, address and phone number are harmless to people but a reason for uncertainty to a model. Uncertain sources do not get recommended.
For local recommendations reviews are not only a trust signal but the text from which the AI draws its reasoning. What is written there shows up in the answer in substance.
A model has to understand that your business, your location and your service belong together. That needs explicit connections rather than implicit hints.
Location pages that only swap the place name give a model nothing to refer to. It needs statements that apply only to you and your area.
Local press, associations, partners and directories are sources models weigh comparatively heavily. A few good mentions do more here than many arbitrary ones.
AI answers change without anyone getting a notification. That is why the same prompts get repeated at fixed intervals and the development recorded.
Through the A/B tests and optimizations we increased our conversion rate by 23%. The collaboration was structured and results-driven.
Richard supported us strongly with SEO and A/B testing. For some of our products we achieved a 13% conversion uplift while making performance more stable.
Great work. Richard supported us with various SEO perspectives, including AI Search. I can recommend him without reservation.
Häufige Fragen
Local SEO aims at appearing in the map section and the local results of Google. Local GEO aims at an AI naming your business when someone asks for a recommendation in your region. The foundations overlap considerably, the measurement is entirely different.
Very often from Google Maps first, then from review platforms, directories, regional media and the business website. That is why a well-maintained Maps listing is the starting point for GEO as well, rather than something outdated.
Yes, though differently from rankings. I test fixed prompts with a location reference at regular intervals and record whether and how your business is named. That produces a development line, even without a position like in Google.
The number of local AI queries is still considerably smaller than in Google. The effort is also small, however, because much of it builds on the same basis as local SEO. Starting now puts you in a field where hardly any local business is doing anything.
In practice yes. An incomplete Maps profile and contradictory business data block both at the same time. If the local basis is not in place yet, we start there, see Local SEO.
That is harder to predict than with rankings, because models update at different speeds. Changes to Maps data and reviews often show within a few weeks, editorial work tends to take months.
GEO stands for Generative Engine Optimization: optimizing to be recommended in AI answers from ChatGPT, Perplexity and Google AI. Here is how it works.
GEO & AI SearchHow your brand gets recommended as a source in ChatGPT and Perplexity: clear facts, strong entities, trust signals and clean technology.
GEO & AI SearchA practical GEO checklist: in 8 steps, make your brand visible as a source in ChatGPT, Perplexity and Google AI Overviews. Ready to work through.
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Tell me where you are based and what you offer. I will test a few real prompts and tell you what comes out today.
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