AI models have formed beliefs about your brand. We built the instrument that reads them.
Today we're launching the Agentic Shelf Audit: a fixed-price, ten-day audit of everything AI models assert about a brand — graded, traced to its sources, and turned into a plan to change it.
The topic
Models don't just recommend. They adjudicate.
Every day, millions of consumers ask AI assistants questions that used to be answered by packaging, advertising, and shelf placement. Very few of those questions are "recommend me a brand." They sound like this: Is that immunity claim actually real? Does it contain palm oil? Is it ultra-processed? What should I buy for a diabetic breakfast?
And the assistant answers. Confidently. With a verdict on your claims, a summary of your controversies, a judgment about your ingredients — synthesized from retailer product pages, Wikipedia, health portals, old press, and forum threads that nobody in your building has ever audited. Sometimes the verdict is fair. Sometimes it's stale. Sometimes it's flatly invented. In every case it's delivered at scale, upstream of every channel you pay for. And then it does this:
We call each of these assertions a belief. Models don't have opinions; they have beliefs synthesized from sources. And that's the good news.
Beliefs can be inventoried, graded, traced — and influenced.
Why now
Three clocks are running.
Answers replaced links
Search gave you ten blue links to fight over; an assistant hands down one synthesized judgment, and the buyer takes it as read.
Advice became action
With agentic checkout, the same system that judges your claim can complete the purchase — or route the basket to whoever it trusts more. The agents got jobs, and one of them is buying.
Beliefs compound
What models learn about your brand this year feeds the answers they give for years, and hardens with every training cycle. Every quarter of inattention is training data for your competitor's advantage.
The practice
An audit built like an instrument, not a stack of screenshots.
The Agentic Shelf Audit is our answer. A versioned battery of 45–60 real-buyer queries runs across four leading assistants, in two modes — what models remember versus what they find when searching — with every high-value query repeated across separate days and reported as a probability, never a lucky output. Every belief lands in a graded ledger — Accurate, Outdated, Contested, Distorted, False, or Fabricated — weighted by severity and by how entrenched it is across models. Then the part almost nobody does: every priority belief is traced to the sources feeding it, each graded by whether you can fix it and how fast a fix propagates.
Two commitments make it credible:
Beliefs are judged against your substantiation file, not your marketing preference — if the model is right and the brand is wrong, the report says so.
Levers are split into documented effects (crawl access, feeds, structured data, harmonized product data), sound-but-variable (canonical content, independent evidence), and experimental — the "AI-optimization" folk remedies we'll test but never sell as fact.
The output: a Share of Answer scorecard benchmarked against three competitors, the Model Belief Report with its threat register, the source graph, and a prioritized influence roadmap — delivered in ten working days, fixed price, one signature. We audit and repair the information environment; we don't astroturf it.
The story
What one battery run looks like.
To build the instrument, we ran a full battery against a composite functional-dairy brand profile on the Polish market. The findings below come from that sample fieldwork — a composite, not a client; the full sample report is available on request — and they're representative of what we now see in category after category.
A false allergen claim, delivered as safety advice. Two models told "parents" the product contained an allergen it doesn't — and recommended a competitor as the safe option. Traced to a single erroneous retailer product page, syndicated to two aggregators. Fixable in days; feeding four beliefs across three models until then.
A study that doesn't exist. One model repeatedly cited a "2024 German consumer study" as evidence against the brand. No such study exists. Fabrications like this appear in most batteries we run — and legal teams have never seen them.
A category ruling pinned on one brand. A 2010 regulator decision that applied to an entire claim category was consistently attributed to the brand specifically — a kernel of truth, framed as a brand-specific rejection, repeated as fact.
The pattern behind the pattern: most damage traces to a handful of upstream sources — often a single product page — and to canon vacuums, topics where the brand publishes nothing citable, so models fill the silence with forums. It's the brand-side mirror of the argument we made about design principles: an agent either finds structured, checkable evidence, or it discounts you. Which means most of it is fixable. That's the entire point of the audit: not a scare, a map.
Who this is for
Four desks, one instrument.
Brand tracking for the model layer
You already track awareness, consideration, and image among humans. The Belief Integrity Score and Share of Answer extend the same discipline to the systems increasingly answering your consumers' questions — and the quarterly re-run turns it into trend data, not a snapshot.
Get your free 3-belief teaser →The revenue leaks are documented, not theoretical
The battery includes agentic purchase attempts on your channels and key listings: every point where an agent stalls, misreads your product data, or reroutes the basket to a marketplace or competitor is logged with reproduction steps — and ranked by fix value.
See what an agent does on your PDPs →The claims being made in your name
Models over-claim on brands' behalf, repeat regulated-claim violations, and invent studies and recalls — exposure your team has never had visibility into. The fabrications section of the report catalogues every one, with sources and reproduction steps.
Request the sample report →The shortlist you're not on
The same instrument runs on the B2B buying process: the shortlists agents build, the risk narratives they attach to your company, and the quiet killer we call positioning compression — models flattening your differentiation into "a consulting firm." If buyers' copilots can't say what you are, they won't say your name.
Ask about the Shortlist variant →Start with three beliefs, free.
Send us your brand and market; within five working days you'll get three real transcripts of what AI assistants currently assert, get wrong, or judge about your brand — each with the source it likely came from. No deck, no obligation. If the findings don't unsettle you, delete the email.
Models already have beliefs about you. The only question is whether you've read them.
The Agentic Shelf Audit is live.
Fixed price, one brand at a time, ten working days — Share of Answer, the Model Belief Report, the Source Graph, and the influence roadmap. Start with the free teaser.
Visit the Agentic Shelf Audit →The instrument & further reading
Method & boundaries · Findings above come from a composite sample battery, not a client engagement. All audit fieldwork is repeated across separate days and reported as probabilities; sponsored placements are logged separately and never blended into organic metrics; and we do not astroturf the information environment — no fake reviews, no seeded forums, no prompt games.