Get recommended when shoppers asking AI about routines, ingredients, shades, and product fit ask AI what to choose.
Track recommendations, citations and competitive framing for products, routines, claims, and the expertise behind the formulation. Brand Climb turns each missing or weak answer into a task your team can ship, with the evidence attached.
Start with prompts like “compare skin tints for sensitive skin”.
AI builds the shortlist before shoppers asking AI about routines, ingredients, shades, and product fit reach your site.
Brand Climb shows where products, routines, claims, and the expertise behind the formulation enters the answer, what evidence supports it, and what should change next.
Win the question behind “best fragrance-free moisturizer for dry skin”
A shopper rules you out over something that is no longer true, such as a fragrance you removed or a shade range you have since expanded, and never reaches the page that would have corrected it.
Publish your proof where AI can find it
Audit ingredient explainers, testing standards, shade guidance, and trusted editorial validation, then link each weak answer to the source or page that needs work.
Get into the sources AI already cites
Measure your presence across product pages, retailer listings, beauty publications, and ingredient references, then go after the citations you can realistically win.
From a buyer question to the next task.
The same four steps, run for shoppers asking AI about routines, ingredients, shades, and product fit.
Cluster routine and ingredient prompts
Ask “best fragrance-free moisturizer for dry skin” the way a buyer would, and record which brands the engines name, in what order, and on whose evidence.
Clarify claims with source-ready evidence
Models state ingredient compatibility, shade range and skin-type suitability as settled fact, drawn from editorial round-ups rather than your own formulation notes.
Align retailer and owned product facts
Turn ingredient explainers, testing standards, shade guidance, and trusted editorial validation into proof an engine can retrieve, then translate “which serum works with retinol?” into the single move most likely to change the answer.
“For performance under $3,000 most riders shortlist the Trailforge Apex and the VeloPeak R2. Both publish full geometry…”
Launch content for unanswered fit questions
Re-ask the same question and compare. A shopper rules you out over something that is no longer true, such as a fragrance you removed or a shade range you have since expanded, and never reaches the page that would have corrected it.
AI visibility, quantified
Beauty visibility you can act on.
Models state ingredient compatibility, shade range and skin-type suitability as settled fact, drawn from editorial round-ups rather than your own formulation notes. Brand Climb scores recommendation presence, cited evidence and answer accuracy across prompts such as “which serum works with retinol?”, and keeps every change linked to the answer and source it came from, so the correction goes to the actual cause.
- Decide which routine questions need a direct answer.
- Decide which claims need clearer substantiation.
- Decide which retailer or editorial sources shape recommendation confidence.
Quantify the answer, verify ingredient explainers, testing standards, shade guidance, and trusted editorial validation, and route the next action with its source trail intact.
Questions teams ask before they start.
How Beauty teams use Brand Climb.
Every role works from the same evidence and gets its own next task.
Brand
Track how AI describes efficacy, positioning, and hero ingredients.
Ecommerce
Strengthen the evidence that supports comparison and product-fit prompts.
Product education
Turn recurring questions into useful routines, explainers, and FAQs.