How LLM Shelf Measures AI Product Visibility

1. Product and category definition

We start by defining the audited product, category context, market, claims, positioning and relevant competitors. This includes producer brands, retailers, private labels and adjacent categories that may solve the same consumer need.

2. Use-case discovery

AI product discovery depends on the buying situation. A brand may appear when the user asks directly about the product category, but disappear when the user starts from a broader need.

• Everyday convenience
• Work or school snack
• Post-workout need
• Price-sensitive purchase
• Premium or health positioning
• Ingredient or recipe usage
• Sensitive skin or ingredient-driven skincare
• Product substitution

3. Prompt battery generation

We build a controlled set of consumer prompts across neutral discovery, branded questions, competitor comparisons, contextual buying prompts and follow-up eligible prompts. Each prompt is designed to test a specific commercial question.

4. AI response collection

We collect answers from selected AI models and answer engines using consistent scenarios and, where needed, repeated runs. This allows us to measure not only whether the brand appears, but also how stable and repeatable the answer is.

5. Follow-up testing

Many AI conversations do not end after the first answer. LLM Shelf measures whether a brand appears after a natural continuation, such as a user asking for more detail or accepting a suggested follow-up.

6. Scoring and analysis

We score each answer for visibility, recommendation strength, sentiment, competitor presence, substitution risk, claim visibility and commercial usefulness. The result is a structured view of how AI represents the brand.

7. Action plan

The audit translates findings into practical recommendations for content, product pages, source coverage, claims, category associations and recurring monitoring.