These days, when people are looking for a product, they’re increasingly turning to an AI assistant rather than a search engine. Until now, it’s been nearly impossible to measure whether a brand is recommended, merely mentioned, or ignored in the response. To address this, Sistrix released three new analyses in its free AI Check in July 2026—and demonstrates in a tutorial how to turn them into a comprehensive brand gap analysis. We’ve reviewed both and summarized what online store owners can take away from them.
At a Glance
- What: With three new analyses, the Sistrix AI Check measures how AI systems talk about brands—sentiment, competitive perception, and recommendation map.
- Data source: real user questions and real AI responses; each statement is backed by the original quote and the prompt that triggered it.
- Cost: Free for all Sistrix packages during the beta phase, no separate project required.
- For online store owners: Using the 5-step workflow of the Brand Gap Analysis, you can identify gaps between your brand’s self-image and how AI perceives it, and address them specifically with content.
What is the Sistrix AI Check?
The AI Check (ai.sistrix.com) evaluates how AI systems like ChatGPT discuss brands in their responses. What sets it apart: The analyses are not based on individual test questions, but on real user questions and real AI responses. Brand mentions are automatically extracted and deterministically analyzed—every statement can be traced back to the original quote and the prompt that triggered it. Three new analyses have been available since July 21, 2026:
Sentiment Analysis
A score ranging from −100 to +100 indicates how positively or negatively AI systems view a brand. The topic breakdown details what the brand is praised for and what it is criticized for—supported by original quotes.
Competitive Perception
A matrix compares the attributed characteristics of all relevant competitors side by side. It reveals which attributes your own brand occupies—and in which areas the competition leads.
Recommendation Map
For each purchasing occasion, the map shows whether a brand is recommended as the first choice, as an option, or not at all. This reveals purchasing situations in which your brand simply does not appear.
How Does a Brand Gap Analysis Work?
A brand gap analysis compares a brand’s self-image with the external perception that AI systems paint of it. The Sistrix tutorial describes a five-step workflow for this:
Define the self-image
Document the positioning in one sentence, 5–10 key terms, the most important purchasing occasions, the product range with target audiences, as well as identified strengths and weaknesses.
Review the sentiment analysis
Evaluate the overall score and thematic analysis: What is the brand praised for, and what is it criticized for? The tutorial example, Lufthansa, scores +45—with strengths in its route network and significant criticism of its baggage policies.
Examine Competitor Perception
Which attributes does your own brand embody, and which do competitors embody? Of particular interest are attributes that are part of your brand’s self-image but are not perceived by anyone else.
Evaluate the recommendation map
In which purchasing situations is the brand recommended—and in what role? Lufthansa was mentioned in only 5 out of 12 relevant situations and never as the first choice.
Prioritize gaps and address them with content
Systematically collect discrepancies between the brand’s self-image and its external perception, prioritize them based on appeal and competitive strength, and derive concrete content and communication strategies from them.
What the Analysis Reveals—The Lufthansa Example
The tutorial walks through the workflow using Lufthansa as an example. What’s interesting isn’t so much the airline itself as the pattern behind it—the same gaps exist in every industry:
| The brand’s self-image | How AI systems describe it |
|---|---|
| Premium provider | The perception of “premium” applies only to the higher booking classes; when it comes to baggage policies, the topic score drops to −67. |
| Brand values: cosmopolitanism and sustainability | Do not appear in a single AI quote—campaign messages do not come across in the responses. |
| In-house frequent flyer program as an advantage | When it comes to the purchase motivation “earning frequent flyer miles,” a competitor is most often recommended—and in some cases, the company’s own program isn’t even associated with the corporation. |
| Modernized cabin, excellent design | The cabin is still described as somewhat outdated—the modernization has not yet been reflected in the responses. |
The lesson here: AI systems repeat what can be substantiated and cited. Advertising messages without a factual basis simply do not appear in the responses.
What specific steps can online retailers take now?
Document your brand image
Summarize your positioning in one sentence, identify key terms, and note the most important purchase triggers—such as “birthday gift,” “B2B bulk order,” or “fast spare parts delivery.” Without a documented self-image, there’s no benchmark for identifying gaps.
Try the AI Check for free
Review your own brand and key competitors in the AI Check. The beta version is free and doesn’t require a dedicated project—the effort needed for an initial assessment is manageable.
Facts, Not Marketing Jargon
AI systems cite verifiable statements: delivery times, warranties, certifications, material specifications, and numbers. Superlatives without evidence, on the other hand, are rarely accepted. Brand values need to be translated into quotable, factual content.
Answer Questions Visibly
FAQ sections and guides with question-based headings align with what users actually ask AI assistants—and can be clearly parsed as question-answer pairs.
Target Unoccupied Purchase Occasions
For purchase occasions where your own brand does not appear in AI responses, create targeted guide content—preferably where the occasion is attractive and competition is weak.
Address points of criticism transparently
The topic with the highest negative sentiment score—in this example, baggage policies—is the best candidate for a dedicated transparency page with clear, comprehensive information.
What Your XONIC Store Brings to the Table
There is no guarantee of appearing in AI responses—the AI providers formulate the answers. However, machine-readable, verifiable content is essential for AI systems to be able to evaluate a shop correctly in the first place. The XONIC shop system comes equipped with several features right out of the box:
- Structured data: Products, news articles, and FAQ content are automatically delivered with Schema.org markup (JSON-LD and Microdata)—including author, date, and publisher for news articles.
- Author profiles: The news section supports managed author profiles with a photo, job title, and biography, complete with a Person markup—a key component for verifiable authorship (E-E-A-T).
- Controllable AI crawler access: Starting with XONIC 4.8.2, you can configure which AI providers are allowed to access your content—and you can control access separately for AI search and AI training.
- GEO-compatible content modules: Fact boxes, visible FAQs, and HTML infographics can be maintained directly in the store—as text that AI systems can read, rather than as images they ignore.
Frequently Asked Questions About the Sistrix AI Check
What is the Sistrix AI Check?
An analysis tool from Sistrix that uses real user questions and real AI answers to evaluate how AI systems talk about brands. Since July 2026, it has included sentiment analysis, a competitive perception matrix, and a recommendation map.
How much does the AI Check cost?
During the beta phase, the AI Check is available free of charge with all Sistrix packages—no separate project is required.
What is a Brand Gap Analysis?
It is the systematic comparison between a brand’s self-image and the external image that AI systems paint of it. Discrepancies—the “gaps”—are prioritized and addressed with targeted content.
Why is perception by AI systems relevant for online stores?
An increasing portion of purchasing advice is provided by AI assistants. Whether a store is recommended, merely mentioned, or overlooked by these assistants shapes the perception of potential customers—and until now, this has been virtually impossible to measure.
How can store content be made more analyzable for AI systems?
Verifiable facts instead of promotional language, visible question-and-answer blocks, structured data following Schema.org, and clearly defined purchase triggers are among the factors that improve evaluability.
Sources & Further Information
- Sistrix Changelog: “Three New Analyses: How AI Evaluates Your Brand” (July 21, 2026) – sistrix.de
- Sistrix Tutorial: “Brand Gap Analysis with the AI Check” – sistrix.de
As of August 2026. Sistrix is a trademark of SISTRIX GmbH; use of the AI Check requires a Sistrix account.
Optimizing Content for AI Search
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Stephan Dunger
Stephan Dunger is one of the brains behind the XONIC store system. He has been developing the platform together with the team since 2012 - from the database to the interfaces to the checkout.
A passionate programmer, technical mind and consultant at the same time: with his in-depth knowledge of store systems and e-commerce, Stephan combines the depth of a developer with an eye for the big picture. Together with the XONIC team, he shapes the technical direction, consistently thinks about functions from the retailer's perspective and advises on customized solutions.
The result is software with a face: customers don't get an anonymous provider, but a direct line to the people who develop XONIC. Pragmatic, fast and at eye level.
