AI Visibility (Baseline)
about 20%
Case Study
How a Swiss rail and excursion provider became a fixed reference point for its category in AI answers
By establishing an AI Visibility Architecture (AIVA)
Executive Summary
An established Swiss rail and tour operator was no stranger to the AI responses. It was mentioned in connection with its own products, but within the category as a whole, it was on par with the competition.
Travelers are increasingly planning trips using systems such as ChatGPT, Perplexity, and Google AI Overview. There, the company shared visibility with established competitors, travel portals, and booking platforms.
Since implementing the AI Visibility Architecture, the company has become a clear leader in its category. Its share of voice rose from around 40 to over 50 percent, and what began as a presence in individual product-related queries has evolved into a clear lead in its own user experiences.
about 11% → about 68% Visibility in the “Drive in the Cab” Intent
about 40% → over 50% Share of Voice in the category
about 88% Share of Voice in the Core Tourism Product
over 60% Share of Voice in Perplexity
Measured using Peec AI, across models including ChatGPT, Perplexity, and Google AI Overview. Comparison of an early baseline against a rolling 30-day window. Intent values for each tracked prompt in the same comparison. Values are rounded.
The starting point:
Well-established but not consolidated in the rail travel and day-trip tourism segment
The company had:
- A strong brand with its own clearly defined products and experiential offerings
- a fully functional website and established traditional channels
- First mentions in AI responses, especially in response to direct product questions
And yet:
- When it comes to general questions about inspiration and day trips: hardly any direct presence, often hidden behind portals and booking platforms
- with a category share of voice of around 40 percent, shared with established competitors and travel platforms
- has not yet become the obvious choice when it comes to open-ended questions about excursions and inspiration
The ranking was solid but unevenly distributed. AI systems mentioned the company when the product name was already clear, but ignored it when the question was open-ended. Visibility for a single product is not the same as authority for a category.
In the AIVA model, this means that without a consolidated architecture, visibility remains tied to individual products and does not extend across the entire category.
The source data (Peec AI Analysis)
Share of Voice (Baseline)
about 40%, shared with the competition
Ø Answer Position
1,7
AIVA Maturity (Classification)
Levels 2 to 3 (Fragmented / Emerging)
Core of the problem
Visibility focused on individual product intents and did not contribute to broader category and inspiration queries.
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 diagnosis conducted using the AIVA Framework revealed three structural shortcomings that limited visibility to individual products.
Mismatched Entity Authority
The company was clearly recognizable as the provider of its products, but was not consistently established as a benchmark for the “day trips and train travel” category.
Where the product name was missing, the brand was missing. An entity that is known only through its products is rarely mentioned when questions arise.
Missing External Validation Layer
When it comes to broad questions about travel and inspiration, the models rely on travel portals, aggregators, and tourism platforms. The company was underrepresented on these sources.
If a topic is not present in the sources that AI systems consider authoritative, the likelihood of a recommendation decreases.
Weak Co-Occurrence in Inspirational Intents
Competitors and travel sites consistently appeared in connection with topics such as day trips, the most scenic train routes, or train excursions. This association was missing for the company outside of its product names.
For AI systems, this meant that when asked an open-ended question about a day trip, the brand was less likely to come to mind.
The Solution: Implementing AIVA (AI Visibility Architecture)
Instead of producing more content, the brand’s reference architecture was reorganized. Not as a content problem, but as an architectural problem.
The project began with a focused AIVA Architecture Sprint with a clear goal: to extend the existing product visibility across the entire category. Implementation took place in four steps.
Prompt Landscape Mapping
Step 1- Analysis of Relevant AI Demand in the Rail Travel and Day-Trip Tourism Segment
- Mapping of 25 tracked intents, ranging from product-related questions to open-ended questions seeking inspiration
- Prioritization Based on Relevance to Demand and Positioning
AI Mention Mapping
Step 2- Analysis of Which Providers and Portals Dominate AI Responses and Why
- Identification of the sources on which the models rely when answering questions about field trips
- Identification of co-occurrence gaps relative to established competitors, portals, and booking platforms
Architecture Rebuild (Core AIVA Lever)
Step 3- The key change was made in the reference network outside the website
- Clearly establishing the company as a distinct entity for its products and for the category
- Machine-readable links between brand, product, and occasion, such as tourist train routes and experiential offerings like a ride in the driver’s cab
- Processing external content as facts and reference points for citation
Authority Amplification
Step 4- Reference content that AI systems use as a source for questions about excursions and products
- External validation signals on relevant travel and industry platforms
- Targeted coverage of decision-related questions regarding the occasion, proceedings, and offerings
- Not through more content, but through more precise architectural signals
The Results: From a Scattered Presence to a Clear Lead
During the observation period, the company’s position shifted from shared visibility to a clear lead. The most significant change was seen in areas where the company offers its own experiences.
| Before | After | |
|---|---|---|
| AI Visibility: | about 20% | a good 21% |
| Share of Voice | about 40% (shared with the competition) | over 50% (a clear lead in the category) |
| AIVA Maturity: | Levels 2 to 3 (Fragmented / Emerging) | Toward Level 4 (Strategic) |
| Purpose of the Driver's Cab Ride: | about 11% visibility | About 68% visibility, about 80% share of voice |
about 37%Visibility in Google AI Overview
about 74% → about 88%SoV in the Core Product
about 11% → about 68%Intent: Ride in the Driver's Cab
about 40% → over 50%Share of Voice
Why This Strategy Works
AI systems work differently from search engines. They construct answers based on references and semantic relationships, from externally validated sources and content capable of providing answers, not from pages that can be ranked.
A brand that is clearly associated with specific occasions and its category is mentioned more often, cited as a reference, and is top of mind before a trip is booked.
The Strategic Impact
The main effect isn’t traffic, but rather the role it plays as an obvious recommendation when making travel decisions.
In the travel and leisure market, this means a higher likelihood of being mentioned in response to an open-ended question about an experience, greater recognition as the obvious choice, and a competitive edge as long as competitors rely solely on traditional SEO and directories to manage their visibility.
This lead is based on architecture. It cannot be offset in the short term by budget or reach.
Our Conclusion
This case illustrates a pattern that applies to many established brands in the DACH region.
AI visibility isn’t just about individual pages or campaigns.
She bases her decision on a brand based on the design of its product examples.
Existing visibility for individual products is a good starting point, but it doesn’t yet give you a competitive edge in the category. The path to that edge is structural and begins with a clear assessment of where you stand. That’s exactly what the AIVA Architecture Sprint does.
Brands that actively build their AI Visibility Architecture are mentioned more often, found more quickly, and perceived as the obvious choice. Those that focus solely on individual products remain invisible when it comes to open-ended questions.
