How do ChatGPT and AI assistants cite a company?

Ask ChatGPT, in French, whether Migros's Cumulus card is worth as much as Coop's Supercard. Before answering, it runs several searches on the web. One looks like this: site:migros.ch Cumulus points 500 points 5 francs 2026. Another: Migros Cumulus points value CHF 2026 Cumulus official.

You did not write "2026", "official" or "CHF". It added them itself. And it will answer you with the pages these searches find, not with its memory.

We talked about this in SEO or GEO: ranking well on Google is no longer enough to be recommended by an AI. Here, we look at how it works from the inside. How LLMs learn about your brand, how ChatGPT decides which companies to mention, and what we saw when we measured its searches on Swiss questions.

Training data: what does ChatGPT know about you before it searches?

ChatGPT, Gemini, Claude and Perplexity rest on a large language model, an LLM. That model was trained on text: articles, websites, forums. A slice of the web, frozen at a given date. That is its training data.

If your company was in that slice, the model knows it, with what people said about it at the time. If it was not, the model does not know it.

And what you publish today will only get in at the next training run, months from now. This memory matters, but you have almost no hold on it.

It matters most when the question is general. In our automotive study, the buying questions named no city and no canton: "What are the best SUVs?", "Which city car should I buy?". Between 16 August and 8 September 2026, ChatGPT answered them without citing any source in 29% of cases, 508 answers out of 1,776. On a question that broad, it sometimes answers from memory, with what its training left it about the brands.

Grounding: what does it search for when it answers?

That is the second route, and this is where most of it happens. When you ask a question, the assistant queries a search engine, reads the pages it finds, and answers from them. This is called grounding, or RAG, for retrieval-augmented generation. The idea was described as early as 2020 in a research paper (Lewis et al.). It is what consumer assistants do today.

This mechanism moves all the time. A page published this morning can be cited this afternoon.

We checked it on the Swiss questions our observatory tracks. Answers that cite no source are rare, whatever the engine.

Answers recorded between 8 September and 8 October 2026, on the questions of ten Swiss brands in the observatory: banking, health insurance, retail, telecoms, transport, real estate, travel, education, watchmaking.

On these precise questions, which name a canton, a city or a Swiss service, the gap with the automotive study is clear. More than nine answers in ten rest on pages found in the moment. That is good news for you. What the AI says about your company depends mostly on what is published and findable today. One caveat all the same: an answer with no source may also come from a link we could not capture. These percentages are therefore ceilings.

Fan-out queries: why does one question become several searches?

The assistant does not run one search, it runs several. OpenAI says so in its documentation: ChatGPT generally rewrites the question into one or more targeted searches, which it sends to engines such as Bing (OpenAI). Google describes the same thing for AI Overviews and AI Mode. They can run "multiple related searches" across subtopics (Google). These searches are called fan-out queries.

Here is what ChatGPT searched for on 1 October 2026, on questions from our observatory (asked in French).

Look at where it goes. The federal law on Fedlex, the Fédération romande des consommateurs, the banking ombudsman, the canton of Vaud, the operator's website. When the question is Swiss, the assistant looks for Swiss institutions, official texts and Swiss players. For a company, these sources are neighbours worth knowing. They are the pages that will be read next to yours.

Across the whole set, we recorded 1,136 searches run by ChatGPT between July and October 2026, on questions asked in French.

It adds the year. In 30% of searches, ChatGPT writes "2026" when the question mentioned no date. A page with no date, or dated 2023, is less likely to come up.

It adds Switzerland. OpenAI passes the user's approximate location on to the engines. When the question did not mention Switzerland, ChatGPT added it in about half of the searches. The sample is small, 104 searches, but the example speaks for itself. "Does Ebookers offer the best prices for low-cost flights?", asked in French, becomes ebookers low cost flights comparison Switzerland.

It switches to English. One search in ten goes out in English, even though the conversation was in French. "Is Proton Mail worth leaving Gmail for in 2026?", asked in French, gives Proton Mail vs Gmail 2026 features privacy. It then reads English-language sources, and answers you in French with them.

It goes for official websites. For Cumulus, it searches directly on migros.ch. Google does the same: on the same question, the AI Overview relies on the Migros and Supercard pages.

Google screenshot, October 2026.

It checks prices to the cent. Most of the time, ChatGPT makes do with one or two searches: half of the answers we recorded have only one. On a comparison, it can go much further. On 15 September 2026, "Salt Home vs Swisscom Internet, comparison 2026." triggered fifteen searches. Here are some of them:

Salt Home Internet Suisse prix 2026 Salt Home Fibre 10 Gbit/s
Swisscom blue Internet prix 2026 Suisse 10 Gbit/s
site:salt.ch "49.95" "Salt Home" 2026
site:swisscom.ch "49.90" "blue Internet L" "2026" fibre
site:salt.ch/fr/internet-tv/home-plus "CHF 59.95" "Home+" 2026
site:swisscom.ch/fr/clients-prives "blue TV S" "39.90" 2026
Swisscom fibre coverage households Switzerland 2026 FTTH 10 Gbit/s
nPerf 2025 fixed internet Switzerland Salt Swisscom 2026

The quotation marks are not there for decoration. They ask the engine for pages that contain exactly "49.95" or "CHF 59.95". The assistant is checking a precise price, on the operator's page. A price shown in an image, or one that is out of date, does not pass this check.

So your page does not answer a question. It answers the searches the assistant builds from that question. And those searches talk about Switzerland, francs, the year, what is "official". These are words a Swiss page can write in black and white.

When the question says Switzerland, the sources turn Swiss

The language of the question already changes the sources. Our automotive study asked exactly the same question in the three national languages. Here is how ChatGPT answered "What are the best SUVs?", over four rounds of measurement between 16 August and 8 September 2026.

Same question, three presses, three brands in the lead. In French, the assistant reads Que Choisir and L'Argus. In German, Auto Bild and the ADAC. Italian is the exception, with Comparis in Italian and Topten. Swiss content in Italian exists, and the assistant finds it.

Writing Switzerland into the question changes everything too. Compare two searches made from Switzerland, in English, on 8 October 2026.

The first does not mention Switzerland: "which robot vacuum to buy for an apartment". The AI Overview recommends the Roomba Mini Combo at CHF 229.00, via iRobot Schweiz. But its sources are YouTube videos and a Google listing, not Swiss editorial sites.

Google screenshot, October 2026.

The second names Lausanne: "best health insurance for students in Lausanne". This time, the AI Overview points to Swisscare, Assurance Genevoise and SWICA. All three are Swiss.

Google screenshot, October 2026.

This is not a fluke of two screenshots. The figures show the same thing.

On buying questions that do not name Switzerland, those in our automotive study (in French), 51% of the sites cited in French are aimed first at the French market. Sites built for Switzerland stay between 13% and 16%. The same goes for German with sites from Germany, and for Italian with sites from Italy.

On the questions in our observatory for French-speaking Switzerland, which nearly all mention Switzerland, a canton or a city, the picture reverses.

Citations recorded between 8 September and 8 October 2026, on the questions of ten Swiss brands in the observatory. The rest goes mostly to .com and .org websites.

The two studies do not ask the same questions, and they do not classify sites the same way. So we do not compare the figures one by one. But the direction is clear. The more Switzerland is written, in the question or in the rewritten search, the more Swiss the sources.

And these Swiss sources differ from one sector to the next. Here are the ones the assistants cite most, sector by sector, in the latest edition of the observatory.

Swiss Atlas, Repliq observatory, October 2026 edition. Ten Swiss-French verticals, questions asked daily.

The assistant adds Switzerland or francs to part of its searches by itself. So a page that writes them in black and white stands a better chance than the best-ranked French page.

Why isn't ranking first on Google enough?

Because each assistant searches a different index. ChatGPT goes through Bing, among others. AI Overviews and AI Mode use Google. Perplexity has its own index. Claude reportedly goes through Brave, according to several observers, although Anthropic has not confirmed it (TechCrunch).

Nor do they read the same number of pages. On the questions in our automotive study, the average number of sources per answer varies ninefold.

Repliq automotive study, 1,776 answers per engine, from 16 August to 8 September 2026.

At Perplexity, an answer rests on about twenty pages: there are many places to take, but each one weighs little. At Grok or Gemini, two or three pages make the answer: being among them changes everything.

You do not need to work on each engine separately. But none of them should be blocked by mistake. A robots.txt file that is too strict, or a page that only shows its text with JavaScript, and some assistants stop reading you.

At OpenAI, the two mechanisms in this article even have a crawler each (OpenAI). GPTBot collects content for model training. OAI-SearchBot is what makes websites appear in ChatGPT's search answers. You can refuse the first without losing the second. Blocking OAI-SearchBot, on the other hand, takes you out of the answers.

# Stay in ChatGPT's answers without feeding model training
User-agent: GPTBot
Disallow: /
 
User-agent: OAI-SearchBot
Allow: /
Disallow: /espace-client/

One technical detail costs a lot here. A crawler only applies the group that names it, and ignores the rest of the file. The rules written under User-agent: *, your private pages for example, must therefore be copied into each named group. That is the Disallow: /espace-client/ line in the example. We made this mistake on our own site, before fixing it.

What can you do with this?

→ look at the searches, not just the questions. The words that come back, the year, "price", "official", "reviews", tell you what your pages need to contain

→ write Switzerland in black and white: prices in francs, cantons, local conditions, references to Swiss law where they matter. What goes without saying for a client in French-speaking Switzerland has to be written for an assistant that searches

→ date your pages and keep them up to date, to match the searches that add "2026"

→ know your neighbours: the Swiss institutions, comparison sites and associations the assistant will look for in your sector, and how they talk about you

→ check your crawlers: OAI-SearchBot, PerplexityBot, Googlebot and the others must be able to read your pages, with text visible without JavaScript

→ be present where the assistant searches: comparison sites, media, trade associations. Our ranking of the most cited pages in Switzerland is a good starting point

At Repliq, we record every day what the assistants answer to Swiss questions, in French, German and Italian, with the searches they run and the sources they cite.

Further reading

→ Aggarwal et al., GEO: Generative Engine Optimization, 2023 (arXiv): the research paper that gave GEO its name

→ Lewis et al., Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, 2020 (arXiv): the founding paper on RAG

→ OpenAI, ChatGPT search (OpenAI help) and Overview of OpenAI crawlers (documentation)

→ Google, AI features and your website (Google Search Central)