Your brand now has a shelf position inside AI answers. The Agentic Shelf Audit is an AI brand visibility and model-belief audit that tests how major assistants describe your company, judge your claims, compare you with competitors and decide whether to recommend you — then traces those answers back to the sources and information gaps shaping them.
Every day, assistants answer questions about your ingredients, your health claims, your controversies — and decide whether you're the answer to your category's problems. They do it with confidence, at scale, from sources nobody in your building has audited. The Agentic Shelf Audit shows you exactly what models believe, where every belief comes from, and how to change it.
Get a free 3-belief teaser See scope & pricingBuyers use assistants in two ways that decide your revenue anyway: they ask for solutions to problems — and a brand is the answer. Or they name your brand and ask for a verdict — and the model delivers one. The audit measures both.
"Best breakfast for a diabetic." "What helps atopic skin in winter." "Which formula for a colicky baby." Nobody names a brand — but every answer contains brands. We measure your Share of Answer: how often you appear at your category's entry points, how favorably, and who owns the default answer instead.
"Is that immunity claim real?" "Does it contain palm oil?" "Is it ultra-processed?" Models judge your brand daily — synthesizing studies, regulator decisions, forums, and stale product data into confident verdicts. We inventory and grade every belief in the Model Belief Report.
Search gave you ten links and let the buyer decide. An assistant hands down a single confident judgment — on your claims, your ingredients, your controversies — and the buyer takes it as read. The agent is now the user, and it doesn't read marketing.
Every model belief has a supply chain: retailer product data, Wikipedia, health portals, forums, old press. Nobody at your company has ever audited it — which means nobody is managing it.
Beliefs models hold today feed the answers they give for years, and what they learn now hardens with every training cycle. Wrong beliefs are cheapest to fix while they're still forming.
A structured battery of real buyer queries — problem, interrogation, and comparison — run across the major assistants and agents, then traced back through the machine side of your presence to explain why the answers come out the way they do.
Problem and occasion queries built from your category's real entry points, run across leading assistants. We measure where you appear, how favorably, in whose company — and which competitors own the default answer.
Every factual and evaluative belief models hold about your brand, extracted into a graded ledger — each weighted by severity and how entrenched it is across models:
Your full claim register — on-pack, on-site, in-campaign — tested claim by claim against model verdicts: upheld, hedged, rejected, or reversed onto a competitor. Including where models over-claim on your behalf, a legal exposure most brands have never seen.
When an agent builds a comparison, someone decided which attributes matter. We test agent-constructed comparisons against named competitors and private label — and identify the missing proof points that cost you the verdict.
The full agentic path on your owned channels and key retail listings: find, parse, basket, checkout. Every stall, misread, and abandonment is a documented revenue leak — often rerouted straight to a competitor. This is the commerce edge of agentic experience design.
Priority beliefs traced to their sources — retailer data, knowledge bases, press, forums, your own inconsistent pages — each graded for authority, accuracy, and whether you can fix it. This is the map that turns findings into influence.
Your presence and favorability across the category's entry points, benchmarked against up to three competitors and private label — with the full evidence pack and a re-runnable query battery, yours to keep.
The graded belief ledger, the claim-by-claim adjudication, the controversy narratives, and a threat register ranking every damaging belief by severity, spread, and confidence — with your Belief Integrity Score as the number leadership tracks.
Every priority belief traced to the sources feeding it, graded by authority and addressability. Single points of failure, canon vacuums, and what a fix changes next week versus next training cycle.
Every fix ranked by impact and addressability — owned, influenceable, earned — cut into now / next / sustained. Delivered in a 60-minute session built for brand, e-commerce, legal, and comms in the same room.
Intake. A 45-minute call plus your claim register and substantiation file. We lock the market, entry points, competitor set, and query battery — then you go back to your day job.
Fieldwork. The full battery across assistants and agents — problem, interrogation, and comparison queries, plus agentic purchase attempts. Every transcript logged as evidence.
Grading & tracing. Beliefs extracted, graded, and ranked; priority beliefs traced to their sources. Nothing enters the report without evidence and reproduction steps behind it.
Synthesis & readout. Scorecard, Belief Report, Source Graph, and roadmap assembled — then delivered live to your leadership. You keep everything, including the battery.
Fixed scope, fixed price, one signature — approvable from a marketing, e-commerce, or comms budget without a procurement cycle. If it turns out models believe only true, flattering things about your brand, you'll have the evidence to show your board. That has not happened yet.
+ Additional market or brand — €2,900 each. Localized battery, same instrument, same report.
+ Canon & Repair Sprint — €12,000. We implement the Now-tier fixes with your team in three weeks: your machine-readable brand canon, product data harmonization, schema, and the highest-value source repairs. The same instrument we run on ourselves — see our own AI info page and capabilities.json.
+ Quarterly belief tracking — €3,500/quarter. The battery re-run: which false beliefs died, which narratives moved, your Belief Integrity Score and Share of Answer over time. Brand tracking for the model layer.
CEE pricing shown · EU/UK and enterprise pricing on request
If your category is bought on trust in a claim — infant nutrition, supplements, functional foods and dairy, dermocosmetics, OTC, pet food, plant-based, bottled water — models are adjudicating those claims right now, and someone is winning the verdict. It's equally built for comparison-driven categories — appliances, baby gear, consumer electronics — where agent-built tables decide the purchase. Buyers: brand, e-commerce, and digital directors, plus the legal and comms leaders who discover mid-readout that this was their audit too.
Four adjacent things get confused with this audit. Each is useful; none answers the same question.
| Method | Primary question | Evidence | Boundary |
|---|---|---|---|
| SEO audit | How do pages rank in traditional search? | Technical, content and link factors | Does not directly test model beliefs or recommendations |
| AI visibility / GEO tool | Does the brand appear across prompts? | Automated monitoring and share-of-answer metrics | May not investigate claims, transcripts or source causality deeply |
| Brand tracking | What do humans think? | Survey and panel data | Does not show what AI systems assert |
| Physical shelf audit | Is the product available and correctly merchandised? | Stores, facings, planograms, compliance | A different shelf entirely |
| The Agentic Shelf Audit | How do AI systems represent and judge the brand? | Queries, transcripts, beliefs, claims, competitors and source tracing | A focused, senior-led diagnostic and repair plan |
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The closest neighbour is the GEO monitoring tool, and the distinction is worth being precise about: a tool tells you your share of answer moved. This audit tells you what models believe, why they believe it, and which of those beliefs is worth the effort to change.
This audit is diagnostic. The repair work it recommends is about correcting the public record, not gaming it.
We do not create or coordinate inauthentic third-party content.
Ever, in any form.
We do not attempt to inject instructions into model behavior or exploit retrieval systems.
Corrections we recommend must be true and evidenced. If a model's unflattering claim is accurate, we will tell you that instead.
The durable position comes from being accurately represented, not from temporarily gaming a retrieval system that will change next quarter.
The major assistants your buyers actually use. The exact set is agreed at market definition and recorded in the report, because coverage changes and comparability depends on knowing exactly what was tested.
Models answer differently by language and region — often materially. Scope is agreed up front, and one audit covers one brand in one market.
Yes, deliberately. You receive the full query battery, so the audit can be re-run and compared over time rather than being a one-off snapshot.
Partly. You can ask a model about your brand today. What is harder is designing an unbiased battery, running it consistently across models, capturing findings in a form that survives scrutiny, and tracing assertions back to sources.
The belief ledger records what the model said, verbatim, with prompt and date. It is not our opinion of your brand — it is a record of what systems assert. Where a model is wrong, that is the finding.
No. We flag claims that models contradict or hedge; your legal and regulatory review stays with you.
Ten working days, €7,500 fixed, one brand in one market. Fixed scope and fixed price, agreed before we start — no scoping theatre.