Turkey AI Visibility Report 2026: which companies does Google AI Mode recommend, and why?

Six findings

1. Answers give a shortlist. An answer recommended 7.5 companies on average (median 8, min 3, max 17). In 4 of the 7 questions asking for a single winner (“which is best?”) the answer said “there is no single best”, yet all 7 still named at least three.
2. Most sources are company websites. Of 313 links, 56.5% are the recommended companies’ own sites and 15.7% other companies’ sites and blogs. News is 4.8%, independent blogs and guides 3.8%, forums 3.5%.
3. In software and manufacturing, recommendation and own site come together. The share of recommendations whose own site was cited in the same answer: 82% in software, 65% in manufacturing, 25% in retail, 2% in hotels.
4. In hotels, the Google business listing decides. 8 of 10 hotel answers showed business cards with rating, review count and nightly price; 6 had no external source link.
5. In manufacturing, directories and foreign makers fill the gap. 11.9% of manufacturing sources are company and export directories. In 3 of 10 manufacturing answers, Turkish-language “top 10 manufacturers in Turkey” lists written by foreign manufacturers were cited.
6. Answers keep the conversation going. 39 of 40 answers ended by asking about budget, size, region or use case. 60% of software answers included a comparison table.

How many companies does an answer recommend?

Manufacturing answers gave the longest lists; hotel and retail answers stayed shorter. The share of foreign brands is notable in retail and software and very low in hotels and manufacturing.

The 301 recommendations belong to 288 different companies; only 13 appeared in more than one answer. Each question builds its own shortlist: being named in one category doesn’t carry over to the neighbouring one.

What sources do the answers rely on?

To answer a question, AI Mode issues multiple related searches across subtopics and data sources (Google calls this “query fan-out”)^[1]. We sorted the links attached to the answers by type:

27% of software sources are blog posts by other software companies that weren’t recommended. For example, a “best accounting software” list written by an e-commerce platform company was cited for an accounting question. Judging by the URL, 21% of source links are list or guide articles (“best”, “which”, “comparison” and similar). 91% of the 246 domains appeared in only one answer.

Is the recommended company’s own site a source?

For each recommendation we checked whether the company’s own domain was among the same answer’s sources. Overall it was in 48.2% of recommendations, but industries differ sharply:

In hotels, the Google business listing decides

Hotel answers were built more from business cards than from text. In 8 of 10 answers each hotel came with rating, review count, star class, nightly price and a “Website” button. 6 answers had no external source link at all; the information came from Google’s own business listings. In the 4 answers with external sources, half of the links were booking and travel-agency sites.

Google’s local ranking guidance lists review count and positive ratings among the factors behind prominence^[2]. The practical takeaway for hotels: a complete, current Google Business Profile is a precondition for visibility in AI answers.

Who fills the gap in manufacturing?

In manufacturing answers the maker’s own site is the main source (72.5%). Where that isn’t enough, the second layer is company and export directories (11.9%). The striking finding: in 3 of 10 manufacturing answers, Turkish-language “top 10 manufacturers in Turkey” lists published by foreign machinery and mould makers were cited. When Turkish manufacturers don’t describe their category in Turkish, their competitors write that story.

2 manufacturing answers also showed rated business cards, each based on fewer than 100 reviews. Even for a maker selling to corporate buyers, the Google Business Profile can be the visible face of the answer.

Answer format: follow-up, tables, location

• Follow-up: almost every answer asks for the next step: budget, headcount, region, use case. The shortlist narrows in the second turn, where information on who a brand is “right for” decides.
• Tables: 60% of software answers compare companies in columns such as “key feature”, “best for” and “free trial”. What fills those cells is what companies state plainly on their own pages.
• Location: in 2 of the 10 retail questions that named no city, the answer added shops in the user’s own district next to national chains. Google lists distance from the searcher among local ranking factors^[2].

How current are the sources?

82 source cards showed a publication date. Of these, 55% were published in the last 12 months and 26% in the last 90 days; 51% are dated 2026. On the other hand, 3 cards were news items from 2005–2006. Current content has an edge, but an old article can still be cited if it’s the only source on its topic.

What it means for brands

These are practical inferences from this data; since they rest on one day and one engine, test them in your own industry.

1. 01

   Answer the category question on your own site

   In software and manufacturing, the recommendation and the own site come together. State what you make, who you serve, where you are and which standards you meet, on one page, in plain sentences (citable content guide).

2. 02

   Local business? The Business Profile comes first

   For hotels the basis is Google’s business listing. Rating, review count, price and website link should be complete and current (hotels and travel).

3. 03

   Be listed correctly in directories

   In manufacturing, the second layer is company and export directories. If you aren’t there, foreign competitors’ Turkish-language lists describe your category (manufacturing).

4. 04

   Write the “right for” yourself

   Answers build comparison tables and end by asking about budget, size and region. If you don’t state which customers you suit and where you serve, someone else fills that cell (software).

5. 05

   In retail your site isn’t enough

   In retail, the recommended chains’ own sites make up only 40% of sources; news (12.7%) and forums (11.1%) weigh far more than in other industries. Corporate news and customer experience are part of the answer too (retail chains).

6. 06

   Dated, current content

   More than half of dated sources are from the last year. Show the update date on category pages and update them regularly.

To run the same measurement in your industry, our method is in how to measure AI visibility; for a measurement of your brand, see the AI visibility audit.

Method and limits

• Questions: 10 Turkish questions per industry, phrased as a real user might and without brand names (e.g. “Can you recommend a POS and order-ticket system for restaurants?”). The 40 questions in this issue are in the CSV file.
• Collection: asked on 7 October 2026 from Istanbul, with one signed-in Google account, at human pace in a browser. Each question was asked once. We saved the answer text, the visible source cards and the links in the “Show all” list.
• Company: a business or brand the answer recommends. Price-comparison and marketplace sites mentioned only as tools were counted as sources, not companies.
• Own site: a source link whose domain belongs to a company recommended in the same answer.
• Source types: domains were classified by hand. Google’s own domains (maps, business listings) were not counted as external sources. The “list article” share is based on the link address only; page content was not read.
• Limits: a 40-answer sample; one day, one engine, one account. AI answers vary by person, location and time. The accuracy of information in the answers wasn’t scored in this issue. Findings are descriptive; generalising needs repeated measurement.
• Data: Aggregate per-answer data (CSV). No company names or source links; counts and flags only. Licence CC BY 4.0. Cite as: “Vextoo Geo Agency, Turkey AI Visibility Report 2026, Issue 1”.

Next issue

Issue 2 adds the remaining five industries (private education, e-commerce, corporate B2B, healthcare, legal); two questions per industry will also be asked in ChatGPT and Perplexity to measure overlap between engines; 10 random questions will be repeated to measure how much answers change. In healthcare and legal, the aim is to measure how answers handle recommendations.