AI Visibility of Turkish Automotive Suppliers in Germany 2026

AI Agrees on Europe's Automotive Giants but Splits on Turkish Suppliers

Across three measurements, we examined six German-language commercial queries on ChatGPT, Gemini, Meta AI, Cerebras and Google AI Overview. We recorded 328 company/institution appearances and 103 unique names. Average overlap between models was 40.9% in the Europe-wide supplier query but fell to about 3.5% in Türkiye-focused queries. 84.5% of the names appeared on only one platform; OEMs, Tier suppliers, material manufacturers and companies with other commercial roles could all end up in the same candidate pool.

Prepared by: Magna Dijital SEO & GEO Team · Editor: Mert Feke · Measured: September 10, 11 and 14, 2026 · Published: September 23, 2026 · Last updated: November 3, 2026

Is Türkiye on the AI Supplier Map of the German Automotive Industry?

When the purchasing team of a German car manufacturer or Tier-1 company asks an AI system today about reliable automotive suppliers in Europe, which companies does it see?

Do different AI systems produce similar candidate lists around established global companies such as Bosch, Continental or ZF?

And when the user adds the condition "Turkish automotive suppliers" to the query, does the same agreement hold?

And a more basic question:

Does AI really see the type of commercial company the user is looking for?

Because in the automotive ecosystem, "a related company" and "the type of supplier being sought" are not the same thing.

An OEM is a vehicle manufacturer.

A Tier-1 or Tier-2 company may supply components, systems or services at a different layer of the supply chain.

A battery manufacturer can be a highly relevant candidate for a specific EV purchasing need.

A composite or reinforcement material manufacturer can be highly relevant to the automotive supply chain, yet not be a direct component supplier.

A holding company may be active in entirely different fields.

That is why, in this study carried out by Magna Dijital with Magna AI Analyzer, we examined not only "which company appeared?" but also "on which platform, for which purchasing intent and in which commercial role did it appear?"

Scope and Method of the Research

The research used six German-language commercial queries across three separate measurements.

The first two queries focused on Europe-wide supplier discovery, while the next four were Türkiye-focused.

Total potential observation space:

5 platforms × 6 queries × 3 measurements = 90 cells

Usable results containing companies/institutions were obtained in 62 of them.

Overall data coverage was 68.9%.

Excluding Google AI Overview, 62 of the 72 cells that could produce company names were filled; coverage rises to 86.1%.

The platforms' data coverage is not equal:

The situation on the Google AI Overview side should not be read as "the platform gave no answer at all." According to the methodology notes in the working files, organic sources appeared in some results, but no concrete supplier lists comparable with the other platforms could be extracted.

Meta AI, for its part, is a partial sample.

Because the system used on the ChatGPT side has a four-day cache, answers that repeated exactly across different measurements were not interpreted as the model's stability over time.

How was overlap measured?

To measure the similarity of the company sets two platforms produced for the same query, we used the Jaccard overlap rate (Jaccard similarity).

If two platforms produce exactly the same set of companies, the rate is 100%.

If they have no names in common, the rate is 0%.

What is measured here is not the quality of the companies, but the extent to which different AI systems meet in the same candidate pool when answering the same purchasing need.

Key Concepts Used in the Research

AI visibility

An AI visibility analysis, which examines to what extent a company or brand appears in the answers relevant AI systems give to user queries, is not assessed on the basis of a single query or platform result.

Company/institution identity (Entity)

Something identifiable that the AI system treats as a distinct company, brand, institution or commercial structure. In the rest of the research, the terms company, institution, name or commercial structure are used for readability.

Purchasing intent

The commercial need the user is trying to meet with the query. General supplier discovery, quality and certification, EV and battery technologies or nearshore sourcing are different purchasing intents.

Company type fit (Entity Fit)

How well the company in the AI answer matches the type of company the user is really looking for. An automotive OEM, a component supplier, a material manufacturer and a holding company are not the same company type.

Key Findings

Europe Has a Shared Supplier Core; for Türkiye It Largely Falls Apart

The most important finding of the research is the difference between P2 and the Türkiye-focused queries.

In P2, when the user asked about reliable automotive suppliers in Europe without naming Türkiye or any other country, the average overlap between the models' company sets was:

40.9%

Looking at the three measurements together:

• ZF Friedrichshafen 10 times,
• Continental 9 times,
• Bosch 9 times,
• Schaeffler 6 times

appeared in the P2 results.

More importantly, Bosch, Continental and ZF entered the candidate pool of all four platforms that produced comparable company names for this query.

This shows that AI systems can form a shared supplier core, at least around certain established global automotive companies.

When the Türkiye condition is added, however, the picture changes sharply.

Taking all usable platform-pair observations in P3–P6 together, average overlap is about 3.5%.

In other words, when the same German buyer asks different AI systems about the same Türkiye-focused need, they largely see different supplier candidate lists.

This difference forms the core contrast of the research:

AI systems can agree on Europe's well-known global automotive companies; in the Turkish supplier universe, this common ground largely disappears.

84.5% of the 103 Unique Names Appeared on Only One Platform

Across the three measurements, a total of 103 unique companies/institutions were identified.

Of these, 87 appeared on only one AI platform.

The rate:

84.5%

Only 16 names entered the candidate sets of more than one platform.

Six companies/institutions appeared at least once on all four platforms that produced comparable company names:

• Bosch
• Continental
• ZF Friedrichshafen
• Schaeffler
• Valeo
• Magna

What stands out about this list is that it consists mainly of companies that have long been strongly defined in the international automotive ecosystem.

In the Türkiye-focused P3–P6 subsample, the fragmentation is even more visible.

These four queries contain 77 unique names, 67 of which appear on only one platform.

The rate is about:

87.0%

So visibility is not a one-dimensional property that can be read simply as "present or absent in AI."

A company may be in ChatGPT's candidate pool but not in Gemini's; a company Cerebras considers may never make it onto the other platforms' lists.

74.8% of the Names Appeared in Only One Purchasing Intent

Alongside platform fragmentation, a strong intent dependency was also observed.

77 of the 103 unique names appeared in only one query.

This rate:

74.8%

The number of names seen at least once in all three measurements was 40.

In other words, only 38.8% of the unique names spread across all three measurements.

This structure suggests that an AI visibility score measured through a single general query may not represent the whole of B2B purchasing behavior.

A company may be invisible in a general automotive supplier query yet appear in an EV and battery-focused purchasing need.

Another company may be visible in the quality and certification query but not make it onto any AI system's candidate list in the nearshore sourcing and fast production query.

Without Türkiye in the Query, Turkish Suppliers Struggle to Make the Shared Candidate List

In P2, the user asked about reliable automotive suppliers in Europe without naming any country.

The shared core of the results contains established global companies such as Bosch, Continental, ZF and Schaeffler.

By contrast, no similar core formed in which Türkiye-based component suppliers entered the candidate list spontaneously and jointly across models.

When the Türkiye condition was added explicitly in the P3–P6 queries, a much wider universe of Türkiye-linked companies/institutions emerged.

This finding does not measure Türkiye's real-world automotive production capacity or the strength of its supplier industry.

What is measured is only which companies AI systems take into the candidate pool during spontaneous supplier discovery.

So the more limited but important conclusion the data supports is this:

AI systems can form a wide universe of Türkiye-linked companies when asked directly about Türkiye; but when Türkiye is not specified, Turkish suppliers are not observed to the same extent in the spontaneous Europe-wide supplier candidate list.

Company Type and Commercial Role Analysis

Being Visible Does Not Mean Being the Right Supplier

The second main axis of the research emerged independently of platform fragmentation:

A company appearing and a company appearing in the right commercial role are different things.

Being a strong company in the automotive industry and fitting the specific supplier role a German OEM or Tier-1 buyer is looking for are not the same.

When assessing the results, at least the following commercial roles need to be kept apart:

1. OEM / vehicle manufacturer,
2. Tier-1 / Tier-2 supplier,
3. parts / component manufacturer,
4. material or input manufacturer,
5. battery / energy solutions company,
6. the Türkiye operation of a global supplier,
7. holding company / conglomerate,
8. a company outside the industry or not matching the commercial role,
9. an unclear company/institution requiring verification.

In raw AI answers, these roles are not always distinguished.

OEMs Can End Up on Supplier Lists

Ford Otosan appeared 11 times in the Türkiye-focused P3–P6 results, on three AI platforms and in three different purchasing intents.

Ford Otosan's official corporate description shows that the company can design and produce complete vehicles, engines and powertrains, and that it is one of Ford Europe's important commercial vehicle production centers. So although its appearance is a strong automotive company signal, it would not be right to automatically place the company in the classic Tier-1 component supplier category.

The same applies to Tofaş. Tofaş appeared 10 times in the research, in four different Türkiye-focused purchasing intents.

Tofaş's official corporate sources describe the company as an automotive manufacturer that produces vehicles and has complete vehicle development capabilities.

Otokar appeared 11 times, on three platforms and in four different purchasing intents. Otokar's official sources clearly show its identity as a vehicle manufacturer. So high AI visibility does not automatically mean the company is a component supplier.

The problem here is not that these companies are unrelated to automotive.

Quite the opposite: they are strong automotive companies.

The problem is that the AI system does not always draw the same distinction between the signal "an important company in the automotive industry" and "the specific supplier role of supplying components to an OEM."

SIRO: Narrow Purchasing Intent, Strong Technical Fit

SIRO shows a different visibility profile.

Across the three measurements combined, it appeared with:

• 5 appearances
• 2 platforms
• 1 query

All five appearances came in the P5 — EV, batteries and new mobility query.

This narrow visibility matters for the research.

SIRO's official sources show that the company develops energy storage solutions for automotive and non-automotive uses and works on battery modules and packs.

So SIRO not appearing in every query does not have to be read as a lack of visibility.

This example shows:

that broad visibility and visibility in the right technical purchasing need are not the same thing.

In this research, SIRO is not an example of "the most visible company"; it is an example of a company that appears in a narrow but technically more meaningful purchasing intent.

Kordsa: Strong Link to the Supply Chain, a Different Supplier Role

In the Türkiye-focused results, Kordsa appeared with:

• 6 appearances
• 1 platform
• 4 different purchasing intents

Kordsa's official sources show that the company develops tire reinforcement technologies and composite technologies used in various industries, including automotive.

So Kordsa is strongly linked to the automotive supply chain.

But that link does not mean it should always be placed in the same category as a classic Tier-1 component supplier.

One of the research's key distinctions can be stated here as follows:

Being related to the supply chain ≠ having the same supplier role.

Tüprag: A Candidate for Commercial Role Mismatch

In all three measurements, ChatGPT showed Tüprag in third place in the same quality/certification query.

In the raw results, the company was included in the candidate pool in an automotive supplier context.

However, Tüprag's official company sources define its field of activity as metal mining exploration and operation; the company operates the Kışladağ and Efemçukuru gold mines.

That makes Tüprag one of the clear commercial role mismatch candidates in the research.

What matters here is not only that the match looks weak for the query.

The same company recurring in the same query in all three measurements also shows that repeated appearance on its own does not mean correct classification.

Consistency ≠ correctness.

Yıldız Holding: A Notable Example of an Entity Resolution Problem

Yıldız Holding appeared four times, only in Meta AI results and in three different Türkiye-focused queries.

In the raw AI records, the company was given roles related to the automotive supply chain.

By contrast, Yıldız Holding's official corporate description explains the group's activities mainly in terms of food, snack production and retail.

So the Yıldız Holding example should be treated as a candidate for a more basic entity resolution problem or an unsupported association.

Examples like this should not lead to a general conclusion such as:

"AI is getting it wrong."

The more controlled question is:

Through which relationship or context was this company included in the automotive supplier candidate pool?

This study does not explain why; it only shows the observed matching problem.

Notable Company and Institution Examples

The table below is not a ranking. It consists of examples chosen to show how different AI visibility profiles and commercial role problems differ.

Note: Visibility counts are not a ranking of company quality, production capacity, technical competence or commercial suitability.

How Should the Findings Be Read from a B2B GEO Perspective?

Measuring AI Visibility Only by "Did the Company Appear in the Answer?" Is Not Enough

The automotive data reveals an important methodological problem showing that, in GEO work, B2B supplier visibility in particular should not be read through mentions or position alone.

For B2B and industrial companies in particular, this distinction makes it important to define clearly not only which industry the company operates in, but also its role in the supply chain, its production capabilities, its target markets and its commercial position.

A company can appear in an AI answer.

It can recur on different dates.

It can appear in more than one query.

It can even be recommended in first place.

But none of these on its own shows that the company is represented in the right commercial role.

To explain the data in this research, it seems more meaningful to separate the following measurement dimensions.

Appearance

Does the company appear in the relevant AI answer?

Consistency

Does it reappear in different measurements?

Model Coverage

How many different AI engines' candidate pools does it enter?

Intent Coverage

In how many different purchasing needs does it appear?

Company Type Fit

Does the company really match the type of company the user is looking for?

Supply Chain Role Fit

Is it an OEM, a Tier-1/Tier-2 supplier, a component manufacturer, a material manufacturer or a battery company?

Capability Fit

Can the required attribute, such as EV, batteries, IATF 16949, nearshore sourcing or flexible production, really be verified?

We do not present these seven dimensions as a new product or a universal AI visibility framework.

They are separate measurement needs that this research revealed.

The last three dimensions are especially critical in a B2B purchasing context.

Because:

Appearing in an AI answer and appearing in the right purchasing context are not the same thing.

If all 103 companies/institutions were verified manually and with sources in a later study, a separate supplier fit rate could be calculated.

However, because not all companies were verified to that level in this study, we do not produce such a percentage.

A Critical Need for B2B GEO: Defining the Company and Its Commercial Role Clearly

This study does not test whether a specific GEO practice causes a specific visibility result.

So the data does not support a conclusion such as:

"If this optimization is done, AI will recommend the company."

On the other hand, OEMs, Tier suppliers, material companies and even companies outside the industry being mixed up in the same candidate pools makes it worthwhile to examine separately in which commercial role, product category and geographic context a company is defined online.

For a B2B manufacturer or supplier, it matters that AI systems can understand the answers to these questions from verifiable sources:

• What does the company produce?
• Which layer of the supply chain is it in?
• Is it an OEM, a Tier-1/Tier-2 supplier or a material manufacturer?
• Which components and systems is it competent in?
• Which markets does it produce for?
• Which certifications does it actually hold?
• How does it relate to specific purchasing needs such as EV, batteries, lightweighting, power electronics or nearshore sourcing?

The research findings suggest that this information is worth examining separately from an AI visibility perspective.

However, this data set alone does not show which source or optimization causes which visibility result.

Limitations of the Research

The data is based on three measurements. A longer-term study may show different behavior.

The platforms' data coverage is not equal. Comparable company/institution results exist in 62 of the 90 potential cells.

Meta AI is a partial sample. Company/institution results exist in 9 of its 18 potential cells.

Google AI Overview did not produce data in the same structure as the other models. Because no comparable concrete supplier lists formed for this query set, it cannot be used in company-set comparisons.

The measurement infrastructure used for ChatGPT has a four-day cache. So answers repeated across the three measurements are not interpreted as the model's independent stability over time.

The raw country lists for the P1 country query were not kept in the workbooks. So this data cannot be used to calculate Türkiye's country-level sourcing visibility for Germany or its rank against countries such as Portugal, Poland or Italy.

Certification and technical claims stated by AI are not independently verified research data. Claims such as IATF 16949, ISO 9001, OEM relationships, production capacity, EV capability, exports or delivery times should be checked separately against official sources for each company.

The research is set in one market and one query language. The six German-language queries used were designed to represent the purchasing needs of German OEMs and Tier-1 companies; different countries, languages and query sets may produce different results.

What This Research Shows

AI Supplier Discovery Depends Strongly on the Platform

84.5% of the 103 unique names appeared on only one platform.

Purchasing Intent Strongly Changes the Candidate Pool

74.8% of the 103 unique names appeared in only one query.

There Is Higher Model Agreement Around the Established Europe-Wide Supplier Core

In P2, the average company-set overlap between models was 40.9%.

Türkiye-Focused Supplier Discovery Is Much More Fragmented

The average overlap of usable model-pair observations in P3–P6 was about 3.5%.

A Company Appearing Does Not Mean the Right Commercial Role

In the research, OEMs, material manufacturers, upstream supply chain players and companies that do not match the automotive supplier role appeared in the same recommendation pools.

What This Research Does Not Show

This study:

• is not a ranking of "Türkiye's best automotive suppliers."
• does not measure the companies' product quality.
• does not compare production capacity.
• does not show the companies' actual OEM sales volume or market share.
• does not verify, based on AI answers, that they hold IATF 16949 or other certifications.
• does not rank AI systems by general accuracy or quality.
• does not show whether Türkiye is the best sourcing country in Europe.
• does not measure whether AI visibility creates traffic, leads or sales.
• does not prove that a specific GEO effort increases visibility.
• does not treat a high position in a single query as overall AI visibility performance.

In short, what the research measures is:

which companies and institutions AI systems take into the candidate pool when looking for suppliers for German OEMs and Tier-1 companies, how much these candidate sets overlap across platforms and purchasing intents, and how well the companies that appear match the commercial role the user is looking for.

The 6 Queries Used

P1 — Country Preference Query

Welche Länder in Europa sind für deutsche Automobilhersteller und Tier-1-Zulieferer besonders attraktive Beschaffungs- und Produktionsstandorte für Automobilkomponenten?
(Which countries in Europe are especially attractive sourcing and production locations for automotive components for German car manufacturers and Tier-1 suppliers?)

Aim: to observe how AI systems assess country options in Europe for sourcing automotive components.

Note: Because the raw country answers to this query were not kept in the workbooks, no country-level ranking is made in this research.

P2 — Spontaneous Supplier Discovery

Welche Automobilzulieferer in Europa würden Sie einem deutschen Fahrzeughersteller oder Tier-1-Unternehmen als zuverlässige Produktionspartner für Automobilkomponenten empfehlen?
(Which automotive suppliers in Europe would you recommend to a German vehicle manufacturer or Tier-1 company as reliable production partners for automotive components?)

Aim: to examine which suppliers AI systems spontaneously take into the candidate pool for Europe when Türkiye or any other country is not specified.

P3 — Turkish Automotive Suppliers

Welche türkischen Automobilzulieferer würden Sie deutschen OEMs oder Tier-1-Unternehmen als zuverlässige Produktionspartner für Automobilkomponenten empfehlen?
(Which Turkish automotive suppliers would you recommend to German OEMs or Tier-1 companies as reliable production partners for automotive components?)

Aim: to examine which companies emerge as core supplier candidates when Türkiye is specified explicitly.

P4 — Quality and Certification

Welche türkischen Automobilzulieferer eignen sich besonders für deutsche OEMs und Tier-1-Kunden, die hohe Qualitätsanforderungen sowie relevante Zertifizierungen wie IATF 16949 und ISO 9001 erwarten?
(Which Turkish automotive suppliers are especially suitable for German OEMs and Tier-1 customers that expect high quality standards and relevant certifications such as IATF 16949 and ISO 9001?)

Aim: to observe how a quality and certification requirement changes the candidate pool.

P5 — EV, Batteries and New Mobility

Welche türkischen Automobilzulieferer eignen sich für deutsche Unternehmen, die Produktionspartner für Komponenten von Elektrofahrzeugen, Batteriesystemen, Leistungselektronik oder anderen Lösungen der neuen Mobilität suchen?
(Which Turkish automotive suppliers are suitable for German companies looking for production partners for electric vehicle components, battery systems, power electronics or other new mobility solutions?)

Aim: to examine more technical and niche supplier visibility in the context of EV, batteries, power electronics and new mobility.

P6 — Nearshore Sourcing, Speed and Flexibility

Welche türkischen Automobilzulieferer eignen sich für deutsche Unternehmen, die kurze Lieferzeiten, flexible Produktionsmengen, schnelle Reaktionsfähigkeit und eine europanahe Lieferkette benötigen?
(Which Turkish automotive suppliers are suitable for German companies that need short delivery times, flexible production volumes, fast responsiveness and a supply chain close to Europe?)

Aim: to examine which companies enter the candidate pool for nearshoring, short delivery time and flexible production criteria.

About This Research

We prepared this research at Magna Dijital, based on a comparative analysis of three separate multi-platform AI visibility measurements we took on September 10, 11 and 14, 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 research examined six German-language B2B purchasing queries on ChatGPT, Gemini, Meta AI, Cerebras and Google AI Overview. The main source of analysis is the raw query-level results tables in the three measurement files; the overall score tables were not used to rank companies as "leaders" or "the best."

The aim of the research is not to rank Turkish automotive companies by quality, size or commercial success, but to examine the overlap, platform dependency, change by purchasing intent and company type/commercial role fit of the supplier candidate sets different AI systems produce in the context of German OEM and Tier-1 purchasing.

The main finding of the study suggests that B2B AI visibility has a more fragmented structure than a classic search engine ranking.

For the same purchasing need, different AI systems can produce different universes of companies.

A company can be visible on one platform and completely absent on another.

And perhaps more importantly, even if AI recognizes a company in the right industry, its commercial role in the supply chain may not be resolved correctly.

That is why, in B2B supplier visibility, the question is no longer just:

"Does our company appear in the AI answer?"

The more meaningful question is:

"How consistently, how widely and how accurately is our company represented as a supplier across the purchasing intents a German buyer brings to different AI systems?"

In B2B AI visibility, the issue is not only whether the company appears in the answer, but whether it is represented in the right purchasing need, in the right commercial role and consistently enough across different AI systems.

How Can You Use This Research for Your Brand?

If you are an automotive supplier who wants to reach OEM and Tier-1 buyers in Germany, you need to track not only whether you appear in AI candidate lists, but also whether you are identified in the right commercial role (Tier supplier, component or material manufacturer).

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 Furniture Manufacturers in the German Market 2026 — Average overlap between platforms stayed at 14.8% across 4 AI platforms; 57% of the 77 names appeared only once.
• 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.