Close citation gaps
Top competitors are cited more often by review and evidence-led supplement sources.
50 buyer-intent questions × 10 runs each × 5 model families = 2,500 measured responses
Every response is stored with model version, prompt, run index, and timestamp. Raw responses are available to clients on request.
Imagine
A shopper asks AI — and your supplement brand is missing.
Imagine a shopper asks ChatGPT: “What's the best creatine for athletes?”
The assistant gives a list of recommendations — but your brand isn't on it.
The shopper doesn't even know you exist and buys from a competitor. Your brand loses a customer without ever knowing it.
We saw this problem firsthand. That's why we built ShelfSignal.
Example report
[Brand] creatine: #4 of 115 brands, 25% share of voice.
Overview · SoV leaderboard
1Competitor A34%
2Competitor B30%
3Competitor C27%
4[Brand]You25%
AcrossChatGPTClaudeGeminiPerplexityGrok
Prompts · Discovery vs reputation
Discovery — core set
- ?best creatine for athletes
- ?which creatine should I buy
- ?best third-party tested creatine
- ?best affordable creatine monohydrate
Brand reputation — separate layer
- ?is [Brand] any good
- ?[Brand] vs [Competitor]
Sources · Most-cited domains
healthline.com41%
examine.com33%
garagegymreviews.com28%
consumerlab.com19%
amazon.com16%
Recommendations · Moves to climb
Clarify testing and value claims
AI explanations repeatedly mention testing, serving economics, and creatine form.
How it works
Measure the AI shelf, find the gaps, fix the inputs.
One AI answer is not a ranking
Ask the same question twice and the brand order changes. We test 20–50 buyer-intent questions in your category and run each one 10 times across 5 model families — so what you see is a stable pattern, not one lucky response.
AI assistants explain their picks
We aggregate those stated reasons across thousands of runs — ingredient form, dose, third-party testing, allergens, reviews, claims — and show which ones consistently appear around top-ranked brands but not around yours.
Fix the inputs, then re-measure
Product data, PDP and FAQ content, brand messaging, and the external sources AI actually cites. Then we run the measurement again — models change and categories shift, and re-measuring is the only honest way to know whether something worked.
Why ShelfSignal
We're not building a tracker. We're building a reason model.
It explains why AI picks one supplement over another — and shows what to fix in your PDP, feeds, FAQ, and citations.
20 years of engineering
Vladimir built high-load web data infrastructure at Bright Data with 10M+ daily users.
4 million students
He led the Typetastic platform used by millions of students worldwide.
Global brand experience
Worked with Citroën, Paramount, Seagate, L’Oréal. We know how data and content drive consumer choice.
Working technology
The system analyzes 5 live assistant families and delivers attribute-level reports.
Why now
Shoppers are asking assistants what to buy — and nobody is measuring the answer.
Search gave brands a ranked page they could audit. AI assistants give a shopper three named products and no explanation. ShelfSignal makes that answer measurable: which brands appear, how often, and on what grounds.
1,000–2,500measured AI responses per category report
115brands tracked in creatine
48hfrom brand name to first report
0integrations, pixels, or data access required
Who is it for
Built for DTC supplement brands with an established catalog and Amazon presence.
Supplements with choice & comparison
Shoppers ask “which one?” and AI already influences the decision.
Categories where trust and attributes matter
Ingredient form, dose, certifications, allergens decide which brand gets recommended.
Ad-restricted categories
Health-claim rules limit paid acquisition. AI recommendations become the primary discovery channel.
Works for DTC, marketplaces, subscription boxes, and B2B supplement suppliers.
Recommendation drivers
The L4 layer: criteria AI states when explaining top picks.
Visibility snapshotRun 2026-08-14 · ChatGPT, Claude, Gemini, Perplexity, Grok
25%
[Brand] appears in 25% of measured creatine recommendation responses, ranking #4 of 115 tracked brands.
Measured gapsCreatine category
1Third-party testing is stated more often around top-ranked competitors.
2Value per serving appears as a recurring reason in winning explanations.
3Creatine form and purity language varies across cited sources.
Stated recommendation criteriaBased on 48 sampled top-3 explanations
We compare how often AI mentions each buying criterion when explaining top-ranked competitors versus your brand.
Third-party testedCompetitors: 34 / 48 explanationsYour brand: 7 / 48High gap
High bioavailabilityCompetitors: 30 / 48 explanationsYour brand: 11 / 48High gap
Good valueCompetitors: 15 / 48 explanationsYour brand: 33 / 48Strength
Example: third-party testing appears in most explanations of top-ranked competitors, but rarely in explanations mentioning your brand.
Measure → act → re-measureFirst remediation sprint
The report turns observed gaps into a prioritized action list, then re-runs measurement after updates. We report what models say and do; we do not claim access to model internals or guarantee ranking changes.
Founders
Built by operators with engineering, product, and wellness experience.
Vladimir Fedorov
CTO and co-founder · 20 years in engineering. High-load systems, web data, ML pipelines, and technical leadership.
vfedorov.com →
Makhrova Alexandra
Co-founder · Business and wellness. Focused on category insight, customer discovery, and go-to-market. She personally experienced the problem of invisible brands in AI.
LinkedIn →FAQ
Short answers to common questions.
Is this SEO?
No. SEO optimizes for search engines; we measure what AI assistants actually answer, and the inputs they cite.
How do you ensure accuracy?
We use non-branded buyer-intent questions per category, each run 10 times across 5 model families through official APIs: OpenAI, Anthropic, Google, xAI, and Perplexity models. Every response is stored with model version, prompt, run index, and timestamp. Raw data is available on request.
What do I get?
Category ranking and share of voice, the questions you win and lose, the sources feeding competitors, and the criteria AI states when explaining top picks — with a prioritized action list.
What's next on the roadmap?
Ongoing monthly re-measurement, trend tracking, and revenue attribution linking AI position to store data. Revenue attribution is a roadmap item, not a current shipped feature.
No integration required
No pixel, no tag, no data access, no engineering time.
We need your brand name and your category. First report in 48 hours.
Free category audit
See how your supplement brand is seen by ChatGPT, Claude, Gemini, Perplexity, Grok, and others.
Get a free category auditThe first 10 brands get a full category audit for free — report co-developed for your niche and personal support from founders. No obligation — we'll show a sample report and give initial recommendations within 48 hours.