What is Generative Engine Optimization (GEO)?
Generative Engine Optimization (GEO) is the practice of making a brand, product or piece of content accurately represented, cited and recommended in the answers of AI assistants such as ChatGPT, Claude, Gemini and Perplexity, and in AI search features such as Google AI Overviews. It builds on SEO but measures answers, not rankings.
Why does GEO matter now?
People ask AI assistants questions they used to type into a search box: "best project management tool for a small team", "is this bank reliable?", "cheapest compostable coffee capsules". Instead of ten links, they get one composed answer, often with a short list of recommended options and a few citations. If a brand is not in that answer, it is not in the consideration set, and unlike a search results page there is no second page to scroll to.
The answer is also a description. It can say that a product is expensive, that a company had a data breach, or that a feature does not exist. Those descriptions come from somewhere, and they can be wrong or out of date. GEO is about both parts: being present in the answer, and being described correctly.
How is GEO different from SEO?
| SEO | GEO | |
|---|---|---|
| Unit of success | A ranking position for a query | A mention, a recommendation and a correct description inside an answer |
| Stability | Rankings are relatively stable day to day | Answers vary from one run to the next; results are rates over many runs |
| What is optimised | Your pages | Your pages and the third-party pages AI engines read about you |
| Language | Queries, usually short | Questions, often long and conversational |
| Measurement | Rank trackers, search console data | Repeated sampling of AI answers, with confidence intervals |
GEO does not replace SEO. Many AI assistants search the web before answering, so pages that rank well for the questions people ask are more likely to be read. But ranking is only the entry ticket: the engine then decides what to say, which sources to trust, and which brands to name.
What do AI engines draw on when they answer?
Two things shape an AI answer:
- What the model learned during training. A large language model absorbs a snapshot of the public web. What it "knows" about a brand without searching comes from that snapshot and changes only when the model is updated.
- What it retrieves at answer time. Assistants with web search or grounding run searches, read a handful of pages and compose the answer from them, often citing some. This part can change within days.
The pages that feed both are rarely just your own site. Encyclopedia entries, discussion forums, review sites, video descriptions, news articles and "best of" comparison lists all weigh heavily. Different engines lean on different kinds of sources, and their habits change over time. This is why GEO is as much about the wider web as about your pages.
What can a brand actually do?
Most GEO work is ordinary good practice, aimed at the right pages:
- Measure first. Write the questions your customers ask, in each language you sell in, and check what each engine answers, several times. Without a baseline you cannot tell a change from noise. See how to check what ChatGPT says about your brand.
- Make your facts easy to find and hard to misread. A current pricing page, a clear product page with specifications, an FAQ that answers the questions people ask AI, and dates on everything that changes. Outdated pages on your own site are a frequent source of wrong answers.
- Describe your entity consistently. Use the same one-sentence description of who you are across your site, profiles and directories. Structured data (schema.org Organization, Product, FAQPage) helps machines read it.
- Let the crawlers in. Check that your robots.txt does not block the crawlers that AI search features use, if you want to be retrieved by them.
- Be present where the engines read. Earn coverage in the comparison articles and reviews that rank for your category; participate genuinely in the discussions where your category is debated; keep public profiles accurate. Fake reviews and planted posts are against platform rules and tend to backfire.
- Correct the sources, not the answer. When an answer is wrong, find the pages that carry the wrong claim and fix or respond to them. See how to fix wrong information about your brand in AI answers.
- Re-measure. Answers change as engines update their models and retrieval habits. GEO is continuous, not a one-off project.
What are the common misconceptions about GEO?
- "One test is enough." One answer is an anecdote. The same question can produce different brand lists on different runs; you need rates over repeated samples.
- "Only my website matters." Third-party pages often matter more, because engines look for independent confirmation.
- "A special file will fix it." Proposals such as llms.txt make a site easier for language models to read, but there is no single file that makes an engine recommend you. Content and reputation still do the work.
- "Negative means hostile." The more common failure is quieter: a brand stops being recommended, or is described with a hedge.
How do you measure GEO?
The core metrics are the AI visibility rate (how often you are mentioned), the recommendation rate, share of voice in AI answers against competitors, the tone of what is said, and the accuracy of the claims made. Each should be reported per engine and per language, with a confidence interval. The methodology guide explains why.
Measuring presence tells you where you stand. Knowing which pages shaped the answer tells you what to do. That second part is what Tonecast was built for: it samples ChatGPT, Claude, Gemini and Perplexity every week, and builds the source graph behind the answers, with an influence weight and an inclusion reason for every page. You can see it on fictitious brands in the public demo.