AI Shelf Audit for Consumer Brands
An AI Shelf Audit shows whether your brand appears in AI-generated product discovery answers — or whether AI recommends competitors, retailers, private labels or substitute categories instead.
What an AI Shelf Audit answers
Consumers are no longer only searching, scrolling and clicking. They are asking AI assistants what to buy, compare, eat, use and trust. LLM Shelf audits those answers across real buying situations, category contexts and competitor comparisons.
- Does AI recommend our product when users do not name our brand?
- Does AI recognise and defend our brand when users ask about it directly?
- Which competitors, retailers or private labels appear instead?
- Which buying situations activate our brand?
- Where does AI solve the consumer need through substitute categories?
- Are our claims visible, credible and supported by sources?
- Does our brand recover after a natural follow-up question?
What we measure
Unbranded AI Discovery, Branded Visibility, Competitor Comparison Visibility, Brand Defence and Overall Target Visibility.
Recommendation Rate, Strong Recommendation Rate, Sentiment, Position and Rationale Quality.
Follow-up Recovery, Second-Turn Discovery, Use Case Visibility, Category-to-Brand Activation and Substitution Risk.
Competitor Presence, Private Label Pressure, Claim Visibility, Claim Support, Source Coverage and Source Authority.
What you receive
- Executive KPI snapshot
- Prompt battery and methodology
- Use-case performance matrix
- Competitor and private-label map
- Follow-up recovery analysis
- Claim and source recommendations
- Practical action plan for marketing, content and ecommerce teams
Who it is for
LLM Shelf is built for FMCG, beauty, wellness, retail, ecommerce and consumer brands that depend on product discovery. It is especially useful for brands that want to understand whether AI assistants are already shaping consideration, recommendation and substitution in their category.
Find out what AI says when consumers ask about your category.
Your brand is already being interpreted by AI systems. The question is whether it is being recommended, ignored or replaced by someone else.