GEO — Generative Engine Optimization
I optimize you to be selected in AI-generated answers (ChatGPT, Perplexity, Google AI Overviews, Copilot) — on both the retrieval and parametric channels.
What is GEO (Generative Engine Optimization)?
GEO is the discipline through which a brand gets selected and mentioned in the answers generated by AI engines such as ChatGPT, Perplexity, Google AI Overviews or Copilot.
It works on two distinct channels: retrieval (the engine searches in real time and cites sources) and parametric (what the model already "knows" from its training data). The two call for different interventions — the first is solved through technical access and extractable content, the second through entity presence and mentions in sources the models have learned from.
What's included
The retrieval channel
Content and structure that make you extractable in real time by bots that cite with attribution — the effect usually shows sooner than on the parametric channel.
The parametric channel
Brand and entity signals that influence what the model "knows" about you at the next retraining cycle.
AI bot access
Citation-first robots.txt (llms.txt optional): you allow search bots (OAI-SearchBot, PerplexityBot, Claude-SearchBot) instead of blocking them.
Brand entity
I position you as a clear, coherent entity, so models associate you with your commercial topics.
Why it matters
Search has moved from blue links to generated answers. GEO is the difference between being named or invisible in the client’s decision.
We work both channels (retrieval + parametric) — not just a one-off trick.
What a GEO engagement looks like in practice
A GEO engagement starts with a measurement, not an assumption: we run a set of questions relevant to your business across several AI engines, in clean sessions, and note whether the brand appears, how it is described and which sources are cited in your place. That produces a gap map: questions where you are absent, sources that replace you, outdated or wrong information about the brand.
The work itself splits across the two channels. On the retrieval channel we fix what stops a bot from picking you up: access in robots.txt, rendering, passage structure, pages that answer the question directly. On the parametric channel we work on brand coherence across the site, profiles and third-party sources — the places a model builds its picture of you from. Every change is documented, so we know what produced what.
What we measure, and how
There is no "Search Console" for ChatGPT or Perplexity, so measurement is a protocol, not an automatic report. We combine several sources: repeated runs of the same prompt set (the same question can produce different answers and sources, so a single run proves nothing), AI-source traffic in GA4 and, for Google, the "Generative AI" report in Search Console.
We report separately what moved quickly (access and structure, where the effect can show once the bot re-crawls the page) and what builds slowly (the brand entity, which depends on model retraining). That way we don't confuse a session fluctuation with a real improvement.
What we do not promise
We do not guarantee positions and we do not guarantee citations: AI engines are probabilistic systems, and no provider controls what a model picks. We guarantee the agreed deliverables, delivered in full and on time. What you get is a verifiable diagnosis, documented work and a measurement that shows the trend, including when a change did not work.
Nor do we sell shortcuts: an llms.txt file or a particular schema type is not a proven citation lever. We use them where they cost little and clarify structure, but the core work remains bot access, answer quality and entity coherence.
About GEO — Generative Engine Optimization
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