Mention Rate
How often the audited brand appears in AI answers.
Independent AI Answer Visibility Measurement
Measure how your brand appears on the AI answer shelf.
Consumers are asking ChatGPT, Gemini, Claude, Perplexity and other AI assistants what to buy, which brand to choose, and what fits a specific occasion.
LLM Shelf helps brands measure whether AI systems mention them, recommend them, rank them, ignore them, or replace them with competitors, private labels and generic alternatives.
Positioning
We are the measurement layer, not another AI content agency.
LLM Shelf provides an unbiased, repeatable data layer for brand teams, digital teams, e-commerce teams, agencies and external partners. We measure what AI systems say today, then measure again after your teams improve content, sources, retail data, PR or product pages.
We measure. Your teams and partners execute. Then we measure again.
The problem
AI answers are becoming a new consumer shelf.
For years, brands measured visibility in search engines, marketplaces, retail shelves and social platforms. Now users increasingly ask AI assistants for direct recommendations.
“What should I buy for a quick breakfast?”
“Which cookies go best with milk?”
“What dessert should I bring for a movie night?”
“Which ice cream brand should I choose?”
“What is a cheaper alternative to this brand?”
“Which product fits this occasion best?”
The answer is often not a list of links. It is a recommendation.
The question is no longer only: “Do we rank in Google?”
It is also: “Does AI recommend us when the consumer asks a real buying question?”
What LLM Shelf measures
We turn AI answers into measurable brand visibility data.
Every audit captures raw AI responses and scores them across structured KPIs.
Win Rate
How often the brand is selected as the best or leading recommendation.
Rank #1 Rate
How often the brand appears first when multiple options are listed.
Recommendation Strength
Whether the model strongly recommends, mildly recommends, neutrally mentions, cautions or ignores the brand.
Sentiment
Whether the brand is described positively, neutrally, negatively or with mixed signals.
No Clear Winner Share
How often the AI avoids choosing a brand and gives generic categories instead.
Competitor Visibility
Which competitors, private labels, retailers and substitutes appear instead of the audited brand.
Source Quality
Whether the answer is supported by identifiable sources or general model knowledge.
Unsupported Claims
How often AI makes risky claims about health, price, ingredients, availability, superiority or ranking.
Methodology
We test real consumer intent, not vanity prompts.
The goal is not to force your brand into the answer. The goal is to test whether the model reaches your brand when a user asks a real question.
01
Use-case mapping
We identify buying situations where the brand should realistically appear: category, occasion, comparison, defensive and branded questions.
02
Prompt pack design
We create natural user prompts designed around intent, not around forcing the audited brand into every answer.
03
Multi-run execution
Prompts can be tested across models, web modes and repeat runs to measure stability and variability.
04
Raw answer capture
We store the original AI answer before interpretation, scoring or summarization.
05
Scoring and source follow-up
Each answer is evaluated for brand visibility, competitors, sentiment, recommendation strength, winner, source grounding and risks.
06
Executive reporting
The output is a clear benchmark: where the brand wins, disappears, gets replaced and needs better source coverage.
Sample insight patterns
Recognition does not always mean recommendation.
A brand may be well known to AI systems and still fail to become the final recommendation.
Mention ≠ win
A model may mention a brand, but then recommend a competitor, a private label, or a generic category. That is why LLM Shelf separates mention rate from win rate.
Your competitor may be a category
On the AI answer shelf, your competitor is not only a direct brand. It may be yogurt, fruit, granola, popcorn, cake, a private label or a “healthier” substitute.
Source gaps create answer gaps
When AI cannot find clear, structured and current information, it fills the gaps with assumptions, stereotypes and generic advice.
Who it is for
Built for teams that need to understand how AI recommends brands.
Brand teams
Measure whether the brand is associated with the right consumer occasions and category needs.
Digital and SEO teams
See whether AI answers use your public sources, product pages, FAQs and category education.
E-commerce teams
Understand how product descriptions, retailer pages and marketplace content may influence AI recommendations.
Insights and strategy teams
Track how AI systems frame the brand, competitors, substitutes and decision criteria.
PR and content teams
Identify source gaps, explainers, FAQs and educational assets that may improve answer quality.
Agencies and partners
Use LLM Shelf as an independent benchmark before and after optimization work.
Definitions for AI visibility
Clear language for a new measurement category.
- What is LLM Shelf?
- LLM Shelf is an independent measurement system for brand visibility in AI-generated answers.
- What is the AI Answer Shelf?
- The AI Answer Shelf is the space inside AI-generated responses where brands appear, disappear, get recommended, get compared, or get replaced.
- What is an LLM Visibility Audit?
- An LLM Visibility Audit measures how AI assistants mention, rank, recommend and describe a brand across real user prompts.
- What is Mention Rate?
- Mention Rate is the percentage of tested AI answers in which the audited brand appears.
- What is Win Rate?
- Win Rate is the percentage of tested AI answers in which the audited brand is selected as the best or leading recommendation.
- What is Source Quality?
- Source Quality measures whether an AI answer is grounded in identifiable, relevant and current sources or relies on general model knowledge.
Deliverables
What a standard audit can include
Executive summary
Visibility scorecard
Scenario analysis
Competitor and substitute map
Source quality analysis
Risk flags
Web vs no-web comparison
Repeatable measurement plan
Founder
Tomasz Wnuk
Commercial growth leader · AI visibility measurement founder · International expansion and competitive strategy
LLM Shelf was created by Tomasz Wnuk, an international commercial and growth leader with experience scaling digital businesses across markets.
Tomasz has led international sales, market expansion and commercial strategy in highly competitive technology and advertising environments, including senior leadership roles at RTB House and advisory work for technology and digital businesses.
LLM Shelf combines commercial strategy, AI systems, prompt-based testing and structured measurement to answer one practical business question: when consumers ask AI what to buy, does your brand show up — and does it win?
Connect on LinkedInMission
Our mission is to make AI answer visibility measurable.
Brands should not rely on anecdotes, screenshots or one-off prompts to understand how they appear in AI systems.
What AI systems say
What they recommend
What they ignore
What sources they use
Which competitors they surface
Which claims they make
Whether brand actions change the result
Contact
Find out how your brand performs on the AI answer shelf.
Request a sample LLM Shelf audit and see where your brand appears, where it disappears, which competitors AI recommends, which sources influence answers, and which KPIs you can track over time.
Request an audit
Tell us your brand, category and market. We will suggest a focused pilot scope.
tomasz@llmshelf.com Contact on LinkedIn