Sprawdź, czy AI poleca Twoją markę — czy podsuwa konkurencję.
Konsumenci nie tylko szukają, scrollują i klikają. Coraz częściej pytają AI, co kupić, zjeść, porównać i komu zaufać. LLM Shelf pokazuje, czy Twoja marka jest w tych odpowiedziach rekomendowana, zastępowana przez konkurencję, czy po prostu pomijana.
Zbudowane dla FMCG, retailu, ecommerce, beauty, wellness i marek konsumenckich, które zależą od product discovery.
Następna półka nie jest wynikiem wyszukiwania. Jest odpowiedzią.
Zanim konsumenci wejdą do Google, na marketplace, stronę retailera albo do sklepu fizycznego, mogą zapytać ChatGPT, Gemini, Perplexity lub inny system AI, co kupić. Odpowiedź, którą dostają, może ułożyć consideration set, zanim Twoja marka zdąży zabrać głos.
To brak odpowiedzi o Twojej marce.
Marka nie musi przegrać dlatego, że AI ją krytykuje. Może przegrać dlatego, że AI nigdy jej nie wymienia. LLM Shelf mierzy, czy marka trafia do generowanego przez AI consideration set — i co dzieje się, kiedy tam nie trafia.
Pełny system pomiaru widoczności AI — nie tylko licznik wzmianek.
LLM Shelf mierzy całe answer space: discovery, brand defence, siłę rekomendacji, konkurentów, private labels, substytuty, źródła, claims, follow-upy i performance w konkretnych use case’ach.
Czy marka wchodzi do odpowiedzi AI?
Overall Target Visibility
How often the target brand or product appears across all tested answers.
Unbranded AI Discovery
Does the brand appear when the user does not name it?
Branded Prompt Visibility
Does AI correctly recognise the brand when the user explicitly asks about it?
Competitor Comparison Visibility
Does the brand appear when users compare it with named alternatives?
Brand Defence
When the brand is named, does AI defend it, explain it and keep it in the recommendation set?
Prompt-Type Split
Visibility separated into unbranded discovery, branded prompts and competitor-comparison prompts.
Czy marka jest tylko wspomniana, czy realnie rekomendowana?
Recommendation Rate
How often AI explicitly recommends the brand, not just lists it.
Strong Recommendation Rate
How often the brand receives a clear, positive, commercially useful recommendation.
Recommendation Position
Where the brand appears in the answer and whether it is framed as a leading option.
Positive / Neutral / Negative Sentiment
Whether the brand is described positively, neutrally, cautiously or negatively.
Rationale Quality
Whether AI gives a convincing reason to choose the brand.
Commercial Intent Quality
Whether the answer helps a user move closer to purchase, comparison or trial.
Jakie potrzeby konsumenta aktywują markę?
Use Case Visibility
Visibility by buying situation: lunchbox, work snack, sport, cooking, premium, value, health and more.
Use Case Family Performance
Normalized reporting by use-case families, not only granular prompt scenarios.
Persona / Context Visibility
How the answer changes by consumer role, need, context, market and situation.
Category Mention Rate
Does AI reach the right category even when it does not name the brand?
Category-to-Brand Activation
When the category appears, how often does AI activate the target brand?
Substytucja Risk
Which adjacent categories solve the user need instead of the target product?
Kto przejmuje przestrzeń odpowiedzi?
Competitor Presence
Which named competitors appear, how often, and in which use cases.
Producer Brand Pressure
Traditional category competitors separated from other market entities.
Retailer Pressure
How often retailers such as marketplaces or grocery chains enter the answer.
Private Label Pressure
How often own brands and private labels appear instead of producer brands.
Competitor Without Target
Cases where competitors appear while the target brand is absent.
Competitive Recommendation Gap
Where competitors are not only mentioned, but recommended more strongly.
Co dzieje się po pierwszej odpowiedzi?
Follow-up Eligibility
How often a first answer invites or enables natural continuation.
Follow-up Recovery
If the brand is missing in the first answer, does it appear after a neutral follow-up?
Second-Turn Discovery
Brand discovery measured in the next conversational turn, without inserting the brand name.
Recovery by Use Case
Which buying situations recover the brand after continuation — and which stay cold.
Same-Model Follow-up Control
Whether follow-up answers are generated under the same model setup as the parent response.
Conversation Path Risk
Where the conversation drifts into substitutes, retailers or generic advice instead of the brand.
Czy AI ma dość dowodów, żeby polecić markę?
Claim Visibility
Which product claims are visible and repeated in AI answers.
Claim Support
Which claims are supported, weakly supported, missing or risky.
Source Coverage
Which owned, earned, retailer, expert and third-party sources influence the answer.
Source Authority
Whether the answer is backed by credible and commercially relevant sources.
Owned vs Earned vs Retail Signals
Where the answer seems to draw its evidence from: brand site, retailers, media, reviews or external databases.
Fact Accuracy & Risk
Whether AI misstates product facts, overclaims benefits or creates avoidable brand risk.
Czy sygnał jest wystarczająco stabilny, żeby nim zarządzać?
Prompt Stability
How consistent answers are across repetitions of the same prompt.
Model Comparison
How visibility changes across ChatGPT, Gemini, Claude, Perplexity, Copilot and other answer engines when included in scope.
Market / Language Differences
How the answer space changes by country, language and local category context.
SKU / Product-Level Visibility
Whether AI recognises the brand at category, product line and SKU level.
Monthly Movement
How visibility, competitors and recommendation quality change over time.
Action Priority Map
Which use cases, claims, sources and content gaps should be fixed first.
Rozpoznawana, gdy jest nazwana. Niewidoczna, gdy konsument pyta neutralnie.
In one FMCG audit, the brand was recognised perfectly when users asked about it directly. But in neutral AI discovery prompts, it did not appear at all.
Demo numbers based on an anonymised single-category proof-of-concept audit.
AI może rozumieć kategorię i nadal nie polecać Twojej marki.
In product discovery, category visibility is not the same as brand visibility. The consumer need can be solved while your brand is left out.
Jak działa audyt AI Shelf
We start with consumer questions, not keywords.
Definiujemy produkt i kontekst kategorii
We map the product, category role, claims, positioning, competitors, private labels and relevant buying situations.
Budujemy realne prompty konsumenckie
We create structured prompts across neutral discovery, branded questions, competitor comparisons and contextual use cases.
Zbieramy odpowiedzi AI
We query selected AI models and answer engines using controlled scenarios, repetitions and follow-up logic where relevant.
Scorujemy widoczność i jakość rekomendacji
We measure whether the brand appears, how it is framed, whether it is recommended, which competitors appear and whether the answer supports commercial intent.
Przekładamy wyniki na działania
We identify the use cases, sources, claims and content areas that can improve future AI visibility.
Widoczność w AI zależy od sytuacji zakupowej.
A product may perform well when the user asks directly about the category, but disappear when the user starts from a broader need.
Twoi konkurenci w AI mogą nie być tymi samymi markami, które śledzisz dziś.
AI answers do not always follow retail shelf logic.
Marki producenckie
- Traditional category competitors
- Brands with stronger source signals
- Brands recommended in comparison prompts
Retailerzy i marki własne
- Retailer-owned brands
- Store ecosystems
- Value-led recommendations
Substytuty kategorii
- Adjacent products
- Alternative need-solutions
- Categories that absorb demand
W gospodarce odpowiedzi claims potrzebują dowodów.
AI systems do not only repeat brand messaging. They synthesize signals from product pages, retailers, reviews, rankings, expert content, media and public sources.
If a brand wants to be recommended for health, convenience, quality, value, sustainability or premium positioning, those associations need to be visible, credible and consistent across the sources AI can access.
Co dostajesz z audytu AI Shelf
Executive KPI Snapshot
A clear summary of your brand’s AI visibility, recommendation and competitive position.
Prompt Battery & Metodologia
A documented set of consumer questions by use case and query type.
Use Case Performance Matrix
Visibility and recommendation quality by buying situation.
Competitor & Private Label Map
Which brands, retailers and substitutes appear in AI answers.
Follow-up Recovery Analysis
Whether your brand appears when the consumer naturally continues the conversation.
Claim & Source Recommendations
Practical next steps for marketing, content, ecommerce and brand teams.
Zacznij od audytu. Kontynuuj monitoringiem.
AI Shelf Audit
Best for brands that want to understand their current position in AI answers.
- One brand / product category
- One market
- Competitor and private label mapping
- Prompt battery and AI response collection
- KPI dashboard and executive report
- Recommendations
Monthly AI Shelf Monitoring
Best for brands that want to track AI visibility over time.
- Recurring visibility tracking
- Competitor movement
- Model comparison
- Use-case performance
- Source and citation monitoring
- Monthly executive summary
Zbudowane dla marek, które zależą od product discovery.
Tworzone przez commercial operatora, nie teoretyka SEO.
Founded by Tomasz Wnuk, a data-driven commercial leader with deep experience in business growth, market analysis and revenue scaling, backed by doctoral studies at the SGH Collegium of Economic Analysis.
LLM Shelf was created to help commercial teams understand how AI changes product discovery, recommendation and category competition.
The methodology combines go-to-market strategy, competitive intelligence, AI response testing, prompt design and digital shelf thinking.
We show brands whether AI recommends their products — and what to do when it does not.
Budowane jako mierzalny audyt, nie ogólne hasło o AI visibility.
These pages explain what LLM Shelf measures, how the audit works and where AI visibility matters across FMCG, beauty, retail and ecommerce categories.
AI Shelf Audit
What the audit answers, what it measures and what the client receives.
Metodologia
Prompt testing, use-case mapping, follow-up recovery, scoring and action plan.
Słownik KPI
Definitions for discovery, brand defence, category-to-brand activation and more.
FMCG AI visibility
How AI answers shape snack, dairy, lunchbox, value and recipe decisions.
Beauty & skincare
How AI recommends products across skin type, ingredients, routines and trust signals.
FAQ
Direct answers to common questions about AI brand visibility and LLM Shelf.
Sprawdź, co AI mówi, gdy konsumenci pytają o Twoją kategorię.
Twoja marka jest już interpretowana przez systemy AI. Pytanie brzmi, czy jest rekomendowana, pomijana, czy zastępowana przez kogoś innego.