Ask real shopping questions at scale
We query AI surfaces with thousands of category-specific prompts like “best magnesium for sleep” or “omega-3 with high EPA.”
The new storefront
AI assistants are becoming the shelf. Supplement brands can’t see it.
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.
Supplement brands feel this first: paid channels are constrained by health-claim policies, comparison queries are common, and shoppers need attribute-heavy recommendations.
What ShelfSignal does
Measurement + attribution + remediation for AI recommendations.
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 · Google · Perplexity
RankCompetitor A: 34% · Your brand: 11%
FixClaims, schema, PDP, feeds, citations
4visibility gaps found
48hfirst report
Why supplements first
A painful, measurable wedge.
Paid ads are restricted.Health claims and platform policies limit Meta/Google performance, leaving budget searching for a new channel.
Intent is comparison-heavy.“Best for sleep,” “low sugar,” “third-party tested,” and “vegan” are exactly the questions AI assistants answer.
Attributes are structured.Form, dose, certification, allergens, use case, and claims can be diagnosed and remediated with category-specific precision.
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 design partners
Deliver founder-led Agent Shelf 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.
Positioning
Not another horizontal AI visibility dashboard.
Horizontal tools won the category.Share-of-voice and rank tracking are becoming table stakes. That is not where ShelfSignal tries to win.
Vertical depth is the wedge.Supplement-specific attributes — form, dose, certification, allergens, claims — make diagnosis and remediation sharper.
Attribution is the moat.The goal is to connect “AI recommended us” to revenue and prove which product-data changes caused lift.
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.
45%Consumers use AI somewhere in purchase discovery
5–10Design partners wanted for sleep/recovery and supplement categories
48hTarget turnaround for the first Agent Shelf Report
DTCStarting with Shopify supplement brands
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.
Design partner offer
See where your brand appears, who beats you, and what to fix first.
Looking for 5–10 design partners
For one narrow supplement category, we will produce a founder-led Agent Shelf Report: recommendation share across AI assistants, competitor ranking, missing attributes, and the first remediation roadmap.
Become a design partner →MVP in progress, attribution-led
The MVP starts with manual + automated reports, then connects recommendation position to Shopify or subscription revenue and validates whether attribute fixes create measurable lift.
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