Shoppers ask recommendation-style questions
“Best magnesium for sleep” or “which creatine is safe for women?” are exactly the prompts AI assistants answer with named products.
Example report
From “AI doesn’t mention us” to a ranked fix list.
Recommendation shareLast 7 days
Your brand appears in 11% of relevant sleep/recovery prompts, behind two competitors with clearer claims and certification data.
Why competitors winAttribute gaps
01Product form is ambiguous across PDP and schema
02Third-party testing is not machine-readable
03Sleep use case appears in blog content, not product feeds
First remediation sprint48h report → fix → re-measure
Update product data around form, dose, certification, allergens, and claims. Then re-query the AI shelf and measure recommendation movement.
The new storefront
AI assistants are becoming the shelf. Supplement brands need to see it first.
Search used to show brands a ranked page. AI assistants answer with a few named products. If you are not in that answer, you are invisible at the exact moment purchase intent is highest.
We start with supplements because AI answers matter unusually early here: shoppers ask comparison questions, trust depends on detailed attributes, health-claim rules limit paid acquisition, and small wording/data gaps can decide which brand an assistant recommends.
Why supplements first
Because AI recommendations are already a buying shortcut in this category.
Trust depends on structured attributes
Form, dose, certifications, allergens, reviews, and claims need to be clear enough for both humans and models to understand.
Paid growth is constrained
Health-claim rules make acquisition harder. If AI becomes a discovery channel, brands need visibility, attribution, and fixes early.
What ShelfSignal does
Measurement + attribution + remediation for AI recommendations.
Test real supplement shopping questions
We query ChatGPT, Claude, Perplexity, and Google AI with category prompts like “best magnesium for sleep” or “omega-3 with high EPA.”
Measure who wins the AI shelf
We extract recommended products, rank, cited sources, competitor mentions, and the attributes that influenced the answer.
Fix the inputs agents rely on
We turn gaps into concrete feed, schema, PDP, and content changes — then re-measure whether recommendation share improves.
How the signal works
From shopper questions to fixes that move AI recommendations.
ShelfSignal runs real buying prompts through AI assistants, turns answers into a shelf ranking, then shows which product-data gaps are costing the brand visibility.
Live category scan
Sleep magnesium
Ask“best magnesium for sleep”
CompareChatGPT · Claude · Perplexity · Google AI
RankCompetitor A: 34% · Your brand: 11%
FixClaims, schema, PDP, feeds, citations
4visibility gaps found
48hfirst report
Proof plan
What we are proving now.
The market for AI visibility is already real. ShelfSignal is not trying to prove that dashboards can exist — we are proving that one vertical can turn AI recommendation position into attributable revenue.
Narrow category
Start with sleep/recovery supplements where prompts, competitors, and attributes are concrete.
5–10 pilot brands
Deliver founder-led Example AI Visibility Reports and collect real objections, data gaps, and buying triggers.
First attribution case
Connect AI shelf position to Shopify/subscription data and show one attribute fix that moves recommendations.
Convert to paid
Turn reports into a lightweight SaaS + remediation subscription for ad-restricted DTC brands.
Why now
Share of voice is becoming commodity. Revenue attribution is the prize.
Horizontal AI-visibility tools have proved the market. ShelfSignal is the vertical wedge: supplements first, revenue attribution first, remediation built around the exact attributes agents use to recommend products.
AIChatGPT, Claude, Perplexity, and Google AI now influence product discovery
5–10Pilot supplement brands wanted for sleep, recovery, and wellness categories
48hTarget turnaround for the first Example AI Visibility Report
DTCStarting with Shopify supplement brands
Founders
Vertical depth, data infrastructure, and fast execution.
Vladimir Fedorov
Founder · 20 years full-stack. Former Bright Data ecosystem experience, high-load web data, ML classification pipelines, and technical leadership.
vfedorov.com →
Makhrova Alexandra
Co-founder · Business and wellness. Focused on category insight, customer discovery, and turning supplement-brand pain into a sharp go-to-market wedge.
LinkedIn →For supplement brands