AI Visibility of Turkish Furniture Manufacturers in the German Market 2026

There Is No Single Ranking in AI: AI Visibility of Turkish Furniture Manufacturers in the German Market 2026

On September 3, 4 and 8, 2026, we compared five German-language commercial/B2B queries across ChatGPT, Gemini, Meta AI and Cerebras. We recorded 300 entity appearances and 77 unique names. The average candidate-set overlap between model pairs was 14.8%. ChatGPT produced the same results on all three dates for the five queries examined, while some answers also included entities that are not manufacturers, are foreign or are only indirectly related to the category.

Prepared by: Magna Dijital SEO & GEO Team · Editor: Mert Feke · Measured: September 3, 4 and 8, 2026 · Published: September 21, 2026 · Last updated: November 3, 2026

Research Question: Do AI Engines Recommend the Same Manufacturers for the Same Commercial Intent?

When a user asks ChatGPT, Gemini, Meta AI or another generative AI system about the same commercial need, it is not only the order of companies in the answer that can change. The set of companies and brands included in the answer can also differ from platform to platform.

In addition, not every entity in the answer necessarily matches the type of company the user described. An AI system may correctly recognize a company related to the furniture category, yet match it to the role of manufacturer, retailer, distributor or another commercial role.

This distinction matters especially for B2B and industrial companies: it is not enough for a company to be associated with its industry; its commercial role as a manufacturer, supplier, distributor or retailer also has to be understood correctly.

In this study, which Magna Dijital carried out with Magna AI Analyzer, we examined two core questions:

To what extent do different AI engines include the same companies and brands in their answers to queries with the same commercial intent?

To what extent does the entity included in the answer match the industry, country and commercial role the user described?

The sample consisted of five German-language commercial/B2B queries representing searches for Turkish furniture manufacturers aimed at the German market.

Scope of the Research

The main comparison set used measurements taken on September 3, 4 and 8, 2026. The five German queries were examined comparatively across four AI engines.

Perplexity and Google AI Overviews were kept out of the main comparison set: Perplexity because of quota-related gaps in its measurements, and Google AI Overviews because it did not produce a directly comparable list of companies or brands.

Because Meta AI did not produce a usable answer for two queries on September 8, the cells concerned were treated as missing data. These cells were not interpreted as zero visibility.

In the ChatGPT measurements, no deliberate repetition was created by reusing a previous answer, through conversation/session reuse or through a response cache.

Methodology and Measurement Approach

The core unit of analysis was the entity appearing in the answer for a given model × query × date combination.

A company or brand appearing in the answer was not treated directly as success, recommendation quality or a business outcome. The research primarily examined observed AI visibility and the structure of that visibility.

To measure the similarity of the entity sets produced by pairs of models, we used the Jaccard overlap rate. This rate divides the number of entities two models share by the total number of unique entities recommended by both models.

100% overlap means the two models produced the same set of entities; 0% overlap means they had no entities in common.

To assess stability over time, we compared the entity sets for the same query on different measurement dates. Cases where positions stayed exactly the same were noted separately.

Key Concepts and Metric Definitions Used in the Research

In assessing AI visibility, this research uses some concepts that look similar but mean different things. To interpret the findings correctly, it is important to distinguish concepts such as entity, mention, recommendation, position and citation.

Entity
Something an AI system treats as a distinct person, brand, company, institution, product or other identifiable thing. In this research, names such as Bellona, İstikbal or Modoko were each treated as an entity.

Intent
What the user is actually looking for with a question, or the need they are trying to meet. For example, "Turkish furniture manufacturers" and "Turkish furniture manufacturers looking for distributors in Germany" relate to the same category but carry different commercial intents.

AI Visibility
The overarching concept that examines to what extent a brand or entity appears in the answers generative AI systems give to relevant user queries. Appearing in a single query does not on its own represent a brand's overall AI visibility.

Presence
An entity appearing in an AI-generated answer. In this research, an "entity appearance" means the entity was present as a candidate in the answer for a given model, query and date.

Recommendation
An AI system presenting an entity as an option or candidate suited to the user's question, rather than merely mentioning it. A mention and a recommendation are not the same thing.

Position
The relative order of an entity in the AI answer. Being first can be a strong position signal, but it does not mean the entity was classified correctly or is commercially better.

Citation
The source, page or reference supporting a piece of information or a claim in the AI answer. A brand appearing in the answer does not mean that the brand's website or any other source was used as a citation.

Entity Fit
How well the entity in the answer matches the industry, company type, geographic origin, product category and commercial role the user asked for. A company may be related to the furniture industry but not fully fit the intent "Turkish furniture manufacturer."

Entity Resolution
Correctly determining which person, company or institution a name or brand expression actually refers to. Similar names or insufficient context can cause AI systems to mix up different entities.

This distinction is especially important. Presence, citation, recommendation, position and referral are not the same metric. This research does not measure citation sources, AI referral traffic or downstream business outcomes.

Key Findings

Average overlap between model pairs was 14.8%

One of the clearest findings was that different AI engines produced largely different sets of entities for the same commercial queries.

In this sample, on average only about one-seventh of the candidate sets two AI engines produced for the same commercial query were shared.

So in assessing AI visibility, the question "What position are we in?" may not be enough. Before that, which platforms' answer sets the brand is included in is a separate dimension of observation.

This result shows that the assumption of a single, fixed "AI ranking" covering all AI systems falls short of explaining the data in the scope of this research.

In none of the 15 tests did all usable AI engines agree on the same first place

Taking the three main measurement dates and five queries together gave 15 query × date combinations.

In none of these 15 combinations did all the AI engines that produced a usable answer put the same entity in first place.

Result: 0 / 15.

This finding suggests that "number one in AI" type generalizations based on a single platform or a single measurement should be treated with caution from a research methodology perspective.

57% of the 77 entities appeared only once

The 300 total entity appearances were spread across 77 unique names. Of these 77 entities, 44 appeared in only one answer.

In other words, about 57% of the unique names identified in the research were single appearances.

By contrast, a more limited group of brands recurred across different dates, queries and models.

These six brands accounted for 148 of the 300 entity appearances, or 49.3%.

The data thus reveals two different behaviors: a relatively narrow core that recurs across measurements, and a wide long tail that mostly appears only once.

So a brand appearing in a single answer and a brand appearing again and again across dates, models and query intents are not the same observation.

The same total appearances do not mean the same visibility profile

Total entity appearances on their own can hide some important differences.

With 35 appearances, Bellona reached the highest total entity appearances in the research and appeared in all four models and all five intents. Yet it was never in first place in any observation.

Although İstikbal appeared 28 times, it was in first place in 14 of those appearances.

Çilek Mobilya, by contrast, had only eight appearances, but six of them were in first place, and its visibility was concentrated in the children's and youth room query intent.

These examples suggest that measures such as presence breadth, position and intent concentration need to be kept separate.

ChatGPT was more stable than the other platforms in this sample

When the same five queries were repeated on September 3, 4 and 8, the platforms behaved differently over time.

In ChatGPT, the entity sets and positions for all five queries stayed the same on all three measurement dates.

However, this stability was not the same as correct entity classification.

For example, in the distribution/wholesale query, ChatGPT kept the order Yuvam GmbH, ARGA Handels GmbH and MUFE-X on all three dates. In the research's entity-fit review, these results were not considered a full match for the role of a Turkish furniture manufacturer.

Similarly, Modoko was in first place in all three measurements for the multi-criteria manufacturer query; yet its entity type was not classified as a manufacturer.

For this sample, the key distinction is clear: Stability ≠ Correctness.

This result does not show that ChatGPT is more stable than other models in every industry, country or usage condition. It only describes the behavior observed for the five queries used and the three main measurement dates.

Results changed markedly by query intent

Entity-set overlap between models also changed depending on the commercial query intent.

The lowest model overlap was observed in the children's and youth room query: 3.3%.

The same query also produced a notable split in entity fit. Çilek Mobilya was in first place for ChatGPT and Gemini on three separate dates, while Meta AI put "IKEA (made in Türkiye)" in first place in the same three measurements.

In the research's classification, Çilek Mobilya was considered a match for the query's manufacturer and subcategory intent, while the IKEA example was treated as an entity-fit case showing that "a production or sourcing relationship in Türkiye" and "a Turkish furniture manufacturer" identity need to be kept apart.

This finding shows that AI visibility can change not only by platform but also by query intent.

AI can find the right industry but choose the wrong commercial role

A preliminary entity-fit review of the 77 unique entities revealed different types of fit problems.

Some results were related to the furniture ecosystem but did not fully match the "manufacturer" role the query asked for.

These results indicate that an AI system recognizing the industry correctly does not mean it also classifies the entity's role in the value chain correctly.

Company Type Fit: Being Visible Does Not Always Mean Being Represented Correctly

Two notable examples in the research were Vakko and Beymen.

In the main three-day sample, Vakko appeared eight times, and all eight appearances came from Meta AI. Vakko was included in Meta AI results at least once in all five intents.

Beymen appeared three times, and all three appearances again came from Meta AI.

In the same measurement set, ChatGPT, Gemini and Cerebras did not include these two entities in their answers.

This observation suggests that in this sample Meta AI may have matched a product/category relationship to the manufacturer role more broadly. However, the research does not measure the algorithmic cause of this behavior.

A similar distinction appeared in the Yuvam GmbH and Modoko examples. Both entities could stay in first place for certain queries on all three main measurement dates, yet in the research's entity-fit assessment they did not fully match the role of "Turkish furniture manufacturer."

That is why the research's measurement discipline keeps this distinction:

Presence ≠ Relevance ≠ Correctness.

Likewise:

Mention ≠ Citation ≠ Recommendation ≠ Position ≠ Referral.

What Do the Findings Suggest for Measuring AI Visibility?

The data in this research suggests that explaining AI visibility with a single aggregate score can hide some important differences.

In this sample, at least five separate dimensions of observation can be distinguished:

We use these five dimensions to explain the research data; we do not present them as a new commercial score, a product or a validated universal AI visibility model.

In particular, no normalized "Entity Fit Rate" covering the whole research was calculated for entity fit. Such a metric would require a separate methodology and the systematic classification of all observations in the future.

What Can Be Concluded for GEO and Entity Information

This study does not measure the impact of GEO work or whether specific optimizations cause visibility in AI answers.

On the other hand, the entity-role errors observed make it worthwhile to examine separately how clearly and consistently brands are defined online.

Whether a company is a manufacturer, distributor or retailer, which product categories it works in, which geographic markets it serves and which brand/entity it should be associated with are separate entity dimensions that can be researched from an AI visibility perspective.

The data in this research does not support a direct conclusion such as "this source increases AI visibility." However, the wrong commercial role and origin matches observed give a reason to examine how entity information is represented on the website and on verifiable third-party surfaces.

The measurement approach and methodological scope for GEO, AEO and AI visibility can be explored further in Magna Dijital's GEO methodology.

Limitations of the Research

The research is limited to five German-language commercial queries, three main measurement dates, one industry and one target market.

The September 2, 2026 measurement was treated as pilot data and was not included in the longitudinal comparison because it did not have the same structure as the later measurements.

Perplexity and Google AI Overviews are not part of the main model comparison set. The results should therefore not be generalized to these platforms.

Meta AI not producing a usable answer for two queries on September 8 was recorded as missing data. Missing observations were not treated as zero visibility.

In the entity-fit review, companies or names that could not be clearly verified were not automatically classified as "wrong" or "hallucination"; they were treated as unclear or requiring verification.

The research does not have an experimental causal design that would explain why platforms choose certain entities or the algorithmic mechanisms they use.

What This Research Does Not Show

This study is not a ranking of "the best Turkish furniture manufacturers in Germany."

It does not measure the brands' product quality, production capacity, export volume, financial size, price competitiveness or actual market share in Germany.

The study is not a general accuracy and quality benchmark of ChatGPT, Gemini, Meta AI or Cerebras.

An entity appearance in this research:

• is not a citation count,
• is not an AI referral traffic metric,
• is not a sales or lead metric,
• is not a guarantee of future visibility,
• does not on its own mean a correct recommendation.

Likewise, the platform differences observed in the research do not definitively explain how these platforms' algorithms work.

The aim of the study is not to put specific brands into a final success ranking, but to compare the overlap, stability over time, change by query intent and entity fit of the entity sets AI systems produce for the same commercial intent.

Queries Used

Q1 — Distribution / wholesale

Welche türkischen Möbelhersteller eignen sich für eine Vertriebs- oder Großhandelspartnerschaft in Deutschland?
(Which Turkish furniture manufacturers are suitable for a distribution or wholesale partnership in Germany?)

Q2 — Sofas and home furniture

Welche türkischen Hersteller von Sofas, Polstermöbeln sowie Wohn- und Schlafzimmermöbeln würden Sie deutschen Möbelhändlern empfehlen?
(Which Turkish manufacturers of sofas, upholstered furniture and living and bedroom furniture would you recommend to German furniture retailers?)

Q3 — Children's and youth rooms

Welche türkischen Hersteller von Kinder- und Jugendzimmermöbeln sind für Importeure und Händler in Deutschland interessant?
(Which Turkish manufacturers of children's and youth room furniture are of interest to importers and retailers in Germany?)

Q4 — Hotel and contract furniture

Welche türkischen Möbelhersteller eignen sich für Hotels, Wohnprojekte und andere Objekteinrichtungen in Deutschland?
(Which Turkish furniture manufacturers are suitable for hotels, residential projects and other contract furnishing in Germany?)

Q5 — Multi-criteria manufacturer comparison

Vergleiche empfehlenswerte türkische Möbelhersteller für den deutschen Markt nach Produktkategorie, Exporterfahrung, Produktionskapazität, Zertifikaten, Individualisierungsmöglichkeiten und Händlerangebot.
(Compare recommended Turkish furniture manufacturers for the German market by product category, export experience, production capacity, certifications, customization options and offer for retailers.)

Conclusion: AI Visibility Is More Than a Ranking Problem

In this research sample, different AI engines produced largely different sets of entities for the same commercial queries.

Average entity-set overlap between model pairs stayed at 14.8%.

About 57% of the 77 unique entities appeared only once, while six brands accounted for 49.3% of the 300 total appearances.

ChatGPT produced the same entity sets and positions for the five queries on all three main measurement dates, while the other platforms showed more variation over time. However, entity-role mismatches also recurred in some stable results.

Brand-level examples also showed that visibility is not one-dimensional. Bellona had the highest total entity appearances but was never in first place in any observation. İstikbal reached first place in 14 of its 28 appearances. Çilek Mobilya, in a narrower set of queries, was in first place in six of its eight appearances.

One of the most critical findings was that appearing in an answer is not the same as being represented as the correct entity.

A system can choose a company from the right industry but with a different commercial role, include a foreign brand in a Turkish manufacturer context, or add an entity that cannot be adequately resolved to its result set.

So within the scope of this research, the more meaningful AI visibility question is not just:

"What position is our brand in the AI answer?"

but rather:

"How consistently, how widely and how accurately is our brand represented as an entity in the answers different AI engines give to different commercial intents?"

About This Research

We prepared this research at Magna Dijital, based on multi-platform AI visibility measurements we took on September 3, 4 and 8, 2026 with Magna AI Analyzer, a tool we developed ourselves.

Magna Dijital is an integrated, data-driven digital marketing agency founded in 2014; our SEO & GEO team runs our GEO and AI visibility work. The aim of this research is not to rank brands by commercial success or product quality, but to compare the overlap, stability over time and entity fit of the entity sets different AI systems produce for the same commercial intent.

How Can You Use This Research for Your Brand?

If you are a furniture manufacturer, exporter or contract furniture supplier, these findings show that you need to check separately which AI platforms your brand appears on in German-language purchasing queries, and in which commercial role.

If you want to see the same measurement for your own brand and competitors, choose our AI Visibility Analysis; if you want us to improve how your brand appears in AI answers based on the findings, choose our GEO Services. We explain how we design measurements and interpret findings on our GEO Methodology page.

Related research

• AI Visibility of Turkish Automotive Suppliers in Germany 2026 — Platform overlap of 40.9% in the Europe-wide supplier query fell to 3.5% in Türkiye-focused queries.
• AI Visibility of Turkish Apparel Manufacturers in Germany 2026 — Turkish manufacturer visibility stayed very limited when Türkiye was not specified; platform overlap was 2.1%.

You can find all of our research on our Magna Research page.