Generative Engine Optimization: how to get your brand recommended by AI

Generative Engine Optimization: how to get your brand recommended by AI, that is, present in the answers generated by Google AI Mode, ChatGPT, Perplexity, Gemini and Copilot, where the user receives a synthesised answer with a few sources instead of ten links. In 2026, part of purchasing decisions passes through these answers; Kantar estimates that about a quarter of AI users already use a shopping assistant. A brand missing from the answer does not exist for that user, whatever its position in classic search.

The guide explains what differs from SEO, how engines choose their sources, which factors increase the chance of being cited, how to build the brand entity and how to measure, with what we have observed on the clients we track monthly in the three main assistants.

Generative Engine Optimization: how to get your brand recommended by AI

What differs from SEO

Classic SEO targets a position in a list; GEO targets being cited or recommended in an answer. They do not exclude each other: generative engines take their sources from search indexes (Google for AI Mode and Gemini, Bing for Copilot and partly ChatGPT, its own index for Perplexity), so without good rankings there is no citation. What is added is the selection of the passage and of the brand: from among the ranked pages, the system picks those from which it can extract a clear answer and the brands it recognises as trusted entities. The practical difference: in SEO you can be in position 3 and get clicks; in GEO, you can be in position 3 and not exist, if the passage is vague or the brand is unknown.

How engines decide what to cite

• Brand authority: sources recognised in the field, with mentions in publications, reviews, a consistent presence; a known brand is cited ahead of an unknown one with better content.
• Accuracy of information: statements with figures, dates, thresholds; "the conversion rate rose by 23% after optimisation" is citable, "results improved" is not.
• Structure: clear subheadings, lists, tables, the answer in the first sentences under the subheading.
• Sources cited on the page: content that backs up its claims with references is preferred; the GEO study (KDD 2024) found that adding quotations, statistics and sources increases visibility in answers by up to 40%, while keyword stuffing lowers it.
• Freshness: for fast-moving topics, the update date and up-to-date content.
• Accessibility for AI bots: blocked pages (from robots.txt or by protection services) cannot be cited by engines that do not use the Google index; the check is in the article on how to build a site indexed by AI.

The brand entity: what AI knows about you

Models build a representation of the brand from everything they read: the website, the Google Business profile, reviews, press articles, directories, social networks, Wikipedia and Wikidata (where they exist). If these sources say different things (a different name, a different field of activity, different contact details), the entity is unclear and the brand is not recommended. What we build for clients:

1. An about-the-company page with real data (tax ID, address, founding, team, what you do and for whom) and Organization structured data with sameAs pointing to all the profiles.
2. Real authors with author pages and Person structured data, linked to Organization.
3. The same information (name, description, contact) in the Google Business profile, on LinkedIn, in directories and in the press.
4. Mentions in sources that models read a lot: industry press, associations, review platforms, supplier lists; interviews and quotes as an expert.
5. An llms.txt file with the company description and the core pages, so that models that read it have your version.

The check: ask each assistant "what is company X" and "who offers service Y in city Z"; the answer shows what the model has understood and what is missing.

The content that gets cited

For every commercial page and for the main articles: the direct answer under each subheading (40-80 words), your own figures and data, short definitions, lists and tables for comparisons, a frequently asked questions section with answers of 40-60 words, cited sources, author and date. On service pages, explicit answers to "how much does it cost", "how long does it take", "how do you work", "who is it for": these are the questions users put to assistants and that assistants look for in pages. The model for rewriting passages is in the article on adapting content for AI Overviews, and the GEO and AEO framework in the one on GEO and AEO.

What each engine looks for

Engine	Sources	What carries weight	What you do in addition
Google AI Mode and AI Overviews	the Google index	classic ranking, structure, entities	classic SEO plus citable passages
ChatGPT with search	Bing plus its own crawler	clarity of the passage, authority of the source	indexing in Bing, access for OAI-SearchBot
Perplexity	its own index	freshness, structure, citation	access for PerplexityBot, recent data
Gemini	the Google index plus the Knowledge Graph	entities, structured data	complete Organization and Person
Copilot	the Bing index	domain authority, recent content	Bing Webmaster Tools, IndexNow

How to measure

There is no official citation report. What we do monthly, for each client: a fixed set of 15-30 questions (informational and commercial, with and without a city), put to ChatGPT, Gemini and Claude, noting the brand's presence (mentioned, cited with a link, recommended first) and the competitors that appear; one visibility score per quarter, because monthly variation is large. In Analytics, the referral traffic from chatgpt.com, perplexity.ai, gemini.google.com and copilot.microsoft.com, with its conversions (usually above the organic average). In Search Console, informational queries with stable impressions and falling clicks (the sign of a generated answer above) and those with the brand name (the sign that recommendations are turning into direct searches). We built an internal dashboard for this monitoring; what it shows after three months on a services client: presence in commercial answers up from 2 in 20 to 9 in 20, after completing the entity and rewriting the service pages.

An example answer and what decided the citation

The question "which SEO agency do you recommend for a small business in Alba Iulia" put to three assistants on the same day. Two recommended three or four companies, with one-sentence descriptions taken from the "about us" pages and from Google reviews; the third answered in general terms, with no names. The recommended companies had in common: a Google Business profile with over 20 reviews, an about page with the year of founding and the city in the text, mentions in at least two external sources (local press, supplier lists) and service pages with prices or a described process. The absent companies had comparable websites, but no Google reviews and no external mention. We could not verify the weight of each factor; the pattern did, however, repeat across ten similar questions, in three cities.

Reviews and third-party platforms

Assistants use reviews (Google, industry platforms) as a source of description and trust: the text of the reviews becomes the description of the company in the answer ("customers appreciate the communication and the clear reports"). For GEO, real, detailed reviews on the platforms that models read are worth more than any text on the site; requesting them systematically, with a reply to each, is described in the article on Google Maps and local SEO. On our own site we replaced the fictitious reviews inherited from the old site with the real ones from Google, for exactly this reason: models check.

What does not work

• "Company X is a leader" type text on the site: models ignore self-assessments and look for external confirmation.
• Mentions bought on sites with no traffic: models read them no more than people do.
• Blocking AI bots and then asking "why don't we appear".
• Mass-generated content "for AI": models prefer sources with experience, and Google treats it as scaled content abuse.
• Waiting for a "trick": there is none; GEO is SEO with an emphasis on the entity and on clarity.

Frequently asked questions

1. Does GEO replace SEO? No; it is built on it. Without rankings in the search indexes there is no citation.
2. How long until results appear? The entity and the rewritten pages are reflected in answers with real-time search within weeks; in training knowledge, within months or years.
3. Can I pay to be recommended? There is no paid placement in assistants' organic answers at the time of writing; ads appear separately, labelled.

The 90-day plan

1. Month 1: AI bot access checked and fixed; the entity (about us, authors, structured data, aligned profiles); the question set and the first measurement.
2. Month 2: rewriting the service pages with direct answers on price, duration, process, who it is for; real frequently asked questions; llms.txt.
3. Month 3: mentions in two or three industry sources (interview, article, supplier list); new reviews on Google; the second measurement and adjustment.

GEO does not replace SEO; it complements it with what sites were missing before as well: a clear identity and direct answers. Companies that have them are recommended; the others are summarised by someone else. The initial diagnosis is part of the SEO audit, and the general context of the change is in the article on Google AI and the impact on SEO.

Sources

• Google Search Central, AI features in Search: how sources are chosen for AI Overviews and AI Mode.
• Google Search Central, helpful, reliable, people-first content: the quality criteria also applied in generated answers.

Source references

• Google Search Central, funcțiile AI din Căutare (AI Overviews, AI Mode)
• Google Search Central, conținut util, de încredere, scris pentru oameni