AI Visibility (Baseline)
7,2 %
Case Study
How De Martin AG makes its expertise in B2B surface technology discoverable for modern AI search systems.
By establishing an AI Visibility Architecture (AIVA)
Executive Summary
An established Swiss industrial company in the surface coating sector faced a situation that is still underestimated in many B2B markets.
Despite eighty years of technical expertise, a functional website, and technically sound content, the company had virtually no presence in AI systems.
Purchasers and engineers are increasingly researching suppliers in systems such as ChatGPT, Perplexity, and Google AI Overview. Companies not mentioned there are excluded from consideration before the first contact is even made.
7,2 % → 17,2 % AI Visibility
21,7 % → 32,9 % Share of Voice (Rank 3 to Rank 1)
5,3 → 3,4 Ø Answer Position
4,1 → 36,7 Visibility in Google AI Overview
Measured using Peec AI, across models including ChatGPT, Perplexity, and Google AI Overview. Baseline: January 2026 compared to a rolling 30-day window through July 7, 2026.
The Initial Situation:
The “Ghost Entity” in the Surface Coating Segment
The company had:
- an established brand and a long-standing market position
- a well-structured website with factually accurate content
- a functional digital presence in the traditional sense
And yet:
- In ChatGPT, Perplexity, and Google AI Overview: hardly any mentions
- For provider-related questions that influence decision-making: no presence or placement behind generic portals
- Even when it came to technically relevant inquiries, the brand was overlooked
From the perspective of traditional search engine logic, the ranking was solid. That was precisely the problem. AI systems do not evaluate individual pages or generate rankings. Instead, they compile answers from a network of references, drawing on the entity that is most clearly confirmed by external sources. Under this logic, a well-maintained website is not proof of authority.
In the AIVA model, this means:
No architecture → no visibility
The source data (Peec AI Analysis)
Share of Voice (Baseline)
21.7%, 3rd place in the category
Ø Answer Position
5,3
AIVA Maturity (Classification)
Level 1 bis 2 (Invisible / Fragmented)
Core of the problem
The brand was interpreted as an isolated entity without sufficient external validation.
The real cause:
An architectural problem, not a content problem
The technical foundation was sound. The website was accessible and readable by AI crawlers. The assessment based on the AIVA Framework revealed three structural shortcomings that were external to the website.
Missing Entity Authority
The brand was not clearly established as an entity in the global knowledge graph. There was a lack of consistent signals that clearly linked the company to its core technologies—such as hard chrome, PVD, and DLC—and to specific application areas such as textile machinery, medical technology, and defense technology.
For AI systems, the brand’s identity remained unclear. An entity that is not clearly defined is mentioned less frequently.
No External Validation Layer
AI systems do not rely primarily on company websites. They draw on third-party sources, reference platforms, and structured knowledge sources. The brand had virtually no presence on the few platforms that these systems classify as highly authoritative.
Without sufficient external validation, the likelihood of a recommendation decreases.
Missing Co-Occurrence Patterns
Established competitors consistently appeared in the context of relevant technical terms and use cases.
This connection was missing from the brand. For AI systems, this means there is a low probability of the brand being mentioned in the correct professional context.
The Solution: Implementing AIVA (AI Visibility Architecture)
Instead of scaling operational measures, visibility was rebuilt—not as a content problem, but as an architectural problem.
The process began with a focused AIVA Architecture Sprint with a clearly defined goal: to understand why AI systems bypass the organization and to design the architecture that would change that.
The implementation took place in four steps.
Prompt Landscape Mapping
Step 1- Analysis of Relevant AI Demand in the Surface Coating Segment
- Identification of the core intents critical to decision-making (25 tracked intents)
- Prioritization Based on Relevance to Revenue and Positioning
AI Mention Mapping
Step 2- Analysis of Which Providers Dominate AI Responses and Why
- Identification of the sources on which the models in this segment are based
- Identifying Co-Occurrence Gaps and Systematically Assessing Competitive Position
Architecture Refactoring (Core AIVA Lever)
Step 3- The critical change was made in the reference network outside the website
- Clear positioning of the brand as an entity in the Knowledge Graph
- Machine-readable linking of brand, technology, and use case on platforms that treat the models as the ground truth
- Processing external content as facts and reference points for citation
Authority Amplification
Step 4- Strategic reference content that AI systems use as a source
- External validation signals on relevant industry platforms
- Targeted coverage of long-tail questions relevant to decision-making
- Not through more content, but through more precise architectural signals
The Results: From Invisibility to Structured AI Visibility
During the observation period, the brand’s position shifted structurally. It evolved from a minor player to become the most frequently mentioned brand in the category.
| Before | After | |
|---|---|---|
| AI Visibility: | 7,2 % | 17,2 % |
| Share of Voice | 21.7% (3rd place in the category) | 32.9% (Ranked 1st in the category) |
| AIVA Maturity: | Levels 1 through 2 (Invisible / Fragmented) | Toward Level 4 (Strategic) |
| Ø Answer position: | 5,3 | 3,4 |
7,2 % → 17,2 %AI Visibility
21,7 % → 32,9 %Share of Voice (Rank 3 to Rank 1)
5,3 → 3,4Ø Answer Position
21 / 25Core Intents with Presence
Why This Strategy Works
AI systems work differently from search engines. They rely on reference structures and semantic relationships, on externally validated knowledge sources, and on content that provides answers—not on pages that can be ranked.
Companies that actively shape this architecture are systematically mentioned more often, are regarded as trustworthy sources, and are already present before the first contact with a customer takes place.
They are based on
- Reference Structures and Semantic Relationships
- Externally validated sources of knowledge
- Content that generates responses—not just pages that rank well
Companies that are actively shaping this architecture
- Are mentioned more frequently on a systematic basis
- Are classified as a trusted source
- We’re there before the first meeting with a client takes place
The Strategic Impact
The key benefit isn’t traffic, but authority within AI systems: AI Authority.
In the industrial B2B sector, this means a higher likelihood of being mentioned in purchasing processes and when switching providers, greater recognition as a relevant provider, and a structural advantage as long as competitors continue to rely solely on traditional SEO to manage their visibility. This edge is based on architecture. It cannot be offset in the short term through budget or content volume.
Our Conclusion
This case illustrates a classification that is relevant for many companies in the DACH region. AI Visibility is not a content or SEO issue. It is a structural issue.
Before investing in AI visibility, it’s important to clearly understand the current situation and why AI systems are bypassing an organization. That’s exactly what the AIVA Architecture Sprint does. Companies that actively build their AI visibility architecture are mentioned more often, found more quickly, and ranked as more relevant. All others remain invisible to AI systems.
