·AEO

What Are Reputation-Shaped Queries? The AEO Test Most Audits Skip

Most AI visibility tools test whether a brand gets mentioned. Reputation-shaped queries test whether it gets trusted — prompts like 'reviews of X' and 'is X legit' that push AI engines onto third-party review sources. Here's the methodology.

A reputation-shaped query is a prompt phrased around trust rather than category — “reviews of [brand],” “is [brand] legit,” “[brand] complaints” — instead of “best [service] in [city].” AI engines answer these by reaching for third-party review platforms rather than the brand’s own website, which surfaces citation gaps a standard brand-mention audit never sees.

The industry calls this work AEO or GEO depending on who’s writing; the terms are used more or less interchangeably. Whatever the label, most tools in the category test the same thing: does the engine name your brand on a category query? That’s a real signal, and it’s the direct descendant of keyword-rank tracking. But it only tests queries where the brand controls the framing — and the query a buyer types right before they commit money is a different shape entirely.

TL;DR

  • Brand-mention monitoring tells you whether AI engines know your brand exists. It doesn’t tell you what they say when someone is checking whether to trust you.
  • Reputation-shaped queries push AI engines off a brand’s own site and onto Yelp, BBB, Trustpilot, Google Business Profile, and other third-party sources because that is where the engine expects trust signals to live.
  • Those profiles are often the least-maintained part of a brand’s footprint, which is why the gaps are so common.
  • Closing those gaps is often faster than building new content. Claiming and completing a profile can take an afternoon; earning citations with new pages can take months.

The shift: why this matters now

Fewer searches end in a visit than at any point on record. SparkToro’s June 2026 study, using Similarweb clickstream data from January through April 2026, found that 68.01% of US Google searches ended without a click — up from 60.45% in 2024, a 7.5-point rise in two years that SparkToro describes as the fastest acceleration it has measured. The split by device is sharper still: mobile zero-click approaches 77%, while desktop sits near 50%.

The practical read is not that search is dying. It is that the answer surface is moving upstream of your website. When a buyer’s question gets resolved inside a results page or chat interface, the sources the engine consulted matter more than your ranking — and on trust-shaped questions, those sources usually are not yours.

Why the query shape changes the sources

Run a category query — “best luxury real estate agent in Laguna Beach” — and an AI engine assembles an answer from directories, editorial roundups, and agent websites. Run a reputation-shaped query about a specific named brand, and the source mix shifts toward review platforms: Yelp, the Better Business Bureau, Trustpilot, BirdEye, Houzz, Google Business Profile, and increasingly Reddit threads.

“Is X legit” is a request for third-party verification — and a brand’s own marketing copy is definitionally not third-party.

The engine is not being clever. It is doing what the query asks. A brand can score cleanly across ten category queries and still lose the sale on the eleventh because its BBB profile is stale, its Yelp listing was never claimed, or its Google Business Profile has three reviews and no description. None of that appears in a standard brand-mention audit because a standard audit never asks the question that would surface it.

How to run the test

Before publishing a client result, run a fixed set of trust-shaped prompts against the same AI engine and record the date, full answer, cited sources, whether the brand was named, whether it was cited with a link, and whether the brand’s own site appeared. Use three to five prompts, including:

  1. reviews of [brand]
  2. is [brand] legit
  3. [brand] complaints
  4. [brand] customer service
  5. [brand] reputation

The method matters more than a one-off screenshot. Re-run the exact same prompt set monthly so the results are comparable over time.

The other half: GSC-grounded non-branded queries

Reputation-shaped prompts fix the branded side. They do not fix the non-branded side — and tools that test non-branded queries often have the buyer type them in by hand, which means the query list is a guess about what people search for. The better input is the client’s own Google Search Console: top queries by impressions over the trailing 90 days. That is demand the site is already registering in Google’s index, tested against AI engines rather than assumed. It replaces “what do we think buyers search for?” with “what do we already know buyers search for, and are AI engines citing us for it?”

How the three query types combine

A WebbROI audit runs three complementary query sets:

  1. Reputation-shaped branded queries — trust-testing prompts against the brand name and close variants.
  2. GSC-grounded non-branded queries — the client’s real top-impression queries, tested for AI citation.
  3. Direct brand-mention queries — the category prompts most tools already run, kept as a baseline.

Each result is scored on three things: whether the brand was named, whether it was cited with a link, and which source the engine pulled from. That third field drives the fix list. Knowing you are absent is less useful than knowing the engine went to Yelp and found nothing. Competitor names appearing in the same results are logged as a share-of-voice byproduct — a record of who else shows up, not a scored comparison.

What to do with the findings

An audit that ends at “you’re not cited” is not worth much. Reputation-query findings map to a short, ordered fix list:

  1. Claim and complete every review profile the engine reached for. If the engine pulled from a Yelp page you have never logged into, that page is part of your brand’s answer surface. Claim it; add hours, categories, description, and photos.
  2. Fix the Google Business Profile first among them. It has the widest downstream reach — AI Overviews, Maps, and local packs all draw on it.
  3. Get review volume onto the platforms that actually surfaced. Not all of them: the two or three engines reached for on your queries. Review requests aimed at a platform no engine cites are wasted effort.
  4. Add Organization schema with sameAs links to those claimed profiles. This connects the profiles to the brand entity explicitly rather than leaving the engine to infer the association.
  5. Check which competitor the engine showed instead on the same platforms. If a competitor’s Yelp page surfaced and yours did not, the comparison is right there: review count, recency, response rate, photo volume, and category selection.
  6. Re-run the same queries in 30 days. A fixed query set makes meaningful comparison possible.

What this covers — and what it does not

Reputation-shaped testing tells you where the trust conversation is happening and whether you are present in it. Its boundaries are worth stating plainly:

  • It finds gaps; it does not close them. Claiming and populating review profiles is client-side work.
  • Share of voice is not benchmarking. Competitor names in shared results are a byproduct. A scored head-to-head comparison is a different product.
  • The automated layer is Perplexity-first. Perplexity surfaces source URLs natively, which makes citation attribution unambiguous. ChatGPT, Gemini, and Google AI Overviews are spot-checked manually in a full audit.
  • This is a methodology, not a proprietary black box. The value of a paid audit is scale across engines and query types, consistent scoring, competitor logging, and month-over-month tracking against a fixed prompt set.

Reputation-shaped queries FAQ

What is a reputation-shaped query?

A prompt phrased around trust or legitimacy rather than category — “reviews of [brand],” “is [brand] legit,” or “[brand] complaints” — instead of “best [service] in [city].” AI engines answer these by pulling from third-party review platforms rather than the brand’s own website.

How is this different from brand-mention tracking?

Brand-mention tracking asks whether an engine names your brand on category queries. Reputation-shaped testing asks what the engine says when someone is specifically checking whether to trust you, and which third-party sources it consults. The two can give opposite results for the same brand.

Can I run this myself?

Yes. Pick three to five trust-shaped prompts, run them against Perplexity or ChatGPT, and document which sources each answer cites. That alone will show whether your review profiles are carrying your brand’s reputation in AI answers. A paid audit adds scale across engines and query types, consistent scoring, competitor logging, and month-over-month tracking.

What is the difference between competitor share of voice and competitor benchmarking?

Share of voice is a byproduct of any multi-query audit — a log of which competitor names surface alongside yours. Benchmarking is a scored head-to-head comparison built to rank brands against each other on fixed criteria. WebbROI audits include the former; the latter is a separate product.

Does this replace Google Search Console?

No. GSC-grounded querying uses Search Console data as an input. It makes better use of GSC by testing already-ranking queries against AI engines instead of guessing at a prompt list.

How often should reputation queries be re-run?

Monthly, on a fixed query set. The value is in the comparison over time, which only works if the prompts stay constant.

Get a reputation-shaped AEO audit

WebbROI runs fixed-price AEO audits combining reputation-shaped branded queries, GSC-grounded non-branded queries, and multi-platform citation checks across ChatGPT, Perplexity, Gemini, and Google AI Overviews. Get a quote or call (949) 547-0494.

Source: SparkToro / Similarweb, “In 2026, Less than One Third of Google Searches Still Send a Click,” June 2026.