Before trying to be cited by AI, an offer has to answer a more strategic question: which box will AI put it in?
This study explores that problem through a Beyond Mentions corpus of 4,320 Perplexity sonar answers collected across 3 UTC days, from May 13 to May 15, 2026. The corpus does not measure existing Beyond Mentions visibility. It tests a pre-launch situation, but the risk also applies to launched offers: when category granularity is weak, LLMs use available categories to explain them.
Pre-launch GEO: an audit of how LLMs understand a market, offer or category before the brand has strong public signals.
Study Status
This study is the consolidated reading of the first Beyond Mentions Observatory wave. It is based on 4,320 completed answers, 240 unique questions per day, 6 passes per question per day, across 3 consecutive UTC days.
Method note: the first day is complete at answer level, but came from an internal collection structure that differs from the next two days. The public unit is therefore the completed answer, not the technical object used by the pipeline.
Key Takeaways
- The risk is not only invisibility. It is being visible in the wrong category.
- In this corpus, source dependency appears in
4,137/4,320answers (95.8%). - Documentation and proof appear as decision inputs in
2,687/4,320answers (62.2%). - Category compression appears in
1,061/4,320answers (24.6%). - Bridge Vocabulary helps AI understand a new thesis with words the market already parses.
What the study measures
The corpus contains:
| Corpus element | Volume | Publishable interpretation |
|---|---|---|
| Completed answers | 4,320/4,320 | Complete answer-level coverage |
| Collection days | 3 | Day 1 2026-05-13, day 2 2026-05-14, day 3 2026-05-15 UTC |
| Unique questions per day | 240 | 64 market baseline, 64 concept boundaries, 64 launch framing, 48 category compression |
| Passes per question per day | 6 | 18 observations per question across three days |
| Answers per day | 1,440 | Distributed across the four question panels |
The method is exploratory. Percentages below describe recurrence inside this controlled corpus, not market shares or representative truths about all LLMs.
Three-day Consolidated Signals
| Signal | Answers | Public reading |
|---|---|---|
| Source dependency | 4,137/4,320 (95.8%) | AI answers depend heavily on reusable sources to justify comparisons |
| Proof reuse | 3,714/4,320 (86.0%) | Documented proof becomes more useful when AI can reuse it in recommendations |
| Source churn | 3,789/4,320 (87.7%) | Cited domains change materially even when source classes remain recognizable |
| Documentation and proof | 2,687/4,320 (62.2%) | Technical documentation acts as decision infrastructure |
| Shortlist and vendor evaluation | 2,168/4,320 (50.2%) | Useful presence is measured in shortlist logic, not only citation |
| Category compression | 1,061/4,320 (24.6%) | An offer can be understood through an existing category that is too reductive |
This is not proof of commercial causality. It is a pre-commercial map: it shows which categories, proofs, sources and shortlist logic AI mobilizes before web analytics or CRM data become legible.
Source Stability: Stable Classes, Volatile Domains
The source layer is strong enough to audit, but too volatile to be treated as a definitive list of authorities.
| Public panel | Domains cited all 3 days | Domains cited at least once | 3-day Jaccard | Top-20 overlap |
|---|---|---|---|---|
| Market baseline | 445 | 1,147 | 38.8% | 14/20 |
| Concept boundaries | 428 | 1,258 | 34.0% | 12/20 |
| Launch framing | 469 | 1,558 | 30.1% | 11/20 |
| Category compression | 343 | 1,176 | 29.2% | 13/20 |
Beyond Mentions therefore cites source classes cautiously and does not treat an individual domain as public proof without human validation of the page, date and context.
Main Finding: AI Compresses What It Cannot Name Precisely Enough
When an offer is framed too generically, LLMs do not remain neutral. They make it understandable by attaching it to categories they already know.
The main buckets observed in this corpus are:
| Cognitive bucket | Role in the corpus | Business interpretation |
|---|---|---|
| AI visibility / GEO / AEO | Default compression bucket | The offer can be read as generic AI visibility work |
| SEO / content marketing | Secondary fallback | The method can be reduced to content or optimization |
| Procurement / vendor evaluation | Adjacent drift bucket | The topic can be narrowed into RFPs or supplier scoring |
| Documentation / proof clarity | Best bridge bucket | The market understands the issue better when tied to proof and documentation gaps |
| Shortlist / decision presence | Best outcome framing | The value becomes placement in decision logic, not just citation |
Beyond Mentions insight: LLMs do not only lack information. They compensate for unclear information by folding offers into categories they already know how to explain.
That compression can dilute the value proposition. When AI compares a premium offer with semantically adjacent but economically incomparable alternatives, it can move the discussion toward price, flatten technical nuance and weaken margin potential before the first sales conversation.
Why “being cited by ChatGPT” is too narrow
The market already talks about AI visibility, AEO, GEO and citations in AI answers. These terms are useful entry points, but they do not capture the strategic risk.
A brand can be:
- cited but wrongly categorized;
- visible but compared with the wrong competitors;
- mentioned without reusable proof;
- present in an answer but absent from the shortlist;
- well indexed but understood as a more generic offer than it really is.
This is why Beyond Mentions separates AI Cognitive Map Audit from simple visibility. The question is not only whether a brand appears. The question is which market logic AI applies before it recommends, compares or excludes.
Forced category vs Bridge Vocabulary
The most useful test compares two approaches.
| Public test | Signal | Answers | Reading |
|---|---|---|---|
| Forced bridge vocabulary | GEO/AEO/AI visibility pull | 111/144 (77.1%) | AI strongly returns to the GEO bucket when framing is too close to the category |
| Natural bridge vocabulary | GEO/AEO/AI visibility pull | 15/144 (10.4%) | Natural bridge wording reduces GEO pull |
| Forced bridge vocabulary | Shortlist logic | 134/144 (93.1%) | Decision logic remains highly present |
| Natural bridge vocabulary | Shortlist logic | 85/144 (59.0%) | Shortlist framing remains legible without over-triggering GEO pull |
| Forced false-category mapping | Category drift | 109/144 (75.7%) | An imposed category can strongly pull the answer into the wrong frame |
| Natural false-category mapping | Category drift | 73/144 (50.7%) | Even without forcing, drift remains a real risk |
Bridge Vocabulary is wording that helps AI understand an offer before proprietary concepts are introduced.
In this corpus, phrases such as documentation blind spots, technical proof visibility or how LLMs shape buying criteria are more useful as the first layer than abstract categories launched alone. They give the model a concrete anchor: documentation, proof, criteria, shortlist.
Eight drift risks to monitor
| Risk | Business consequence | Mitigation |
|---|---|---|
| Compression into GEO/AEO/AI visibility | Differentiation disappears into a known category | Use GEO as a bridge, then define the proof/decision layer |
| Compression into SEO/content marketing | The offer looks like content production | Anchor pages in vendor evaluation and decision criteria |
| Procurement/tender drift | The category narrows too early | Qualify the topic as AI-mediated evaluation, not only pre-tender work |
| Arbitrary reconstruction of proprietary terms | AI explains the term in the wrong direction | Introduce the proprietary concept after a bridge definition |
| False precision | The research loses credibility | Show denominators, state the exploratory status and avoid market extrapolations |
| Over-generalized niche advice | The paper sounds rigorous but becomes less actionable | Use niche material only as filtered context |
| Overvisible sources | The wrong actors look strategically important | Separate citation volume from authority quality |
| Vague prompts | The model maps the wrong object | Use descriptive, contextualized wording |
What Beyond Mentions Applies to New or Poorly Granularized Offers
A category launch, or the repositioning of an already launched offer, should not start with a proprietary name. It should start with an audit:
- Which buckets does AI use spontaneously?
- Which substitutes and implicit competitors appear in those buckets?
- Which criteria are repeated in comparisons?
- Which proof is missing to justify differentiation?
- Which terms create confusion?
- Which Bridge Vocabulary lets the offer enter the existing cognitive map?
This extends the logic behind AI Cognitive Map Audit and Bridge Vocabulary. The study formalizes the method at a publishable level: audit the cognitive map before publishing.
What not to conclude
This study does not prove that all LLMs classify all offers the same way. It does not compare multiple models. It does not prove that one page causally changes future AI answers or revenue.
It shows something more precise: in this exploratory corpus, insufficiently framed wording is vulnerable to category compression, and bridge wording reduces confusion better than forced proprietary category names.
From study to action
For a company launching or repositioning a B2B offer, the operational output is not a visibility report. It is a decision map:
- dominant cognitive buckets;
- implicit substitutes and competitors;
- terms to avoid;
- bridge terms to use;
- documentation gaps to fix;
- Decision Share of Voice metrics to track over time.
The first KPI is not: “are we visible?” It is: “which decision logic are we understood through?”
Read Next
- Category Compression Risk: understand how the wrong category changes the competitor set.
- Stable Claims Are Not Enough: see how unstable signals reveal documentation gaps.
- Beyond Traffic: measuring Decision Presence: connect the findings to pre-commercial metrics.
FAQ
Is this study representative of the whole GEO market?
No. It is an exploratory corpus study based on 4,320 Perplexity sonar answers collected across three UTC days, from May 13 to May 15, 2026. It describes a controlled Beyond Mentions corpus, not a universal statistical truth about all LLMs or all B2B markets.
Why use Perplexity sonar rather than a multi-model panel?
Perplexity sonar provides sources attached to answers, which makes it possible to analyze both generated claims and the documentation ecosystem behind them. The limitation is explicit: this study is not a multi-model benchmark.
What does Category Compression Risk mean?
Category Compression Risk is the risk that an LLM folds an offer into an existing category such as GEO, SEO or procurement, even when the actual value proposition is more specific.
What is the main recommendation?
Before publishing or repositioning an offer, audit the LLM cognitive map: the categories it uses, the criteria it repeats, the proof it expects and the bridge vocabulary that prevents wrong-category mapping.
What question does the buyer ask AI?
Which documentation simplification can lower the standard?
Which technical requirement must be clearly formulated?
Which evidence should be requested or published?
Which criterion excludes an insufficient answer?