LLM Visibility Audit: How Two Words Shift Your AI Competitor Set Completely
Adding a geographic qualifier to buyer prompts can swap half the brands ChatGPT recommends. What our audit data shows about prompt-conditional competitors.
Geo qualifier impact (paired audits)
| Brand | Citation share | AI visibility status |
|---|---|---|
| Brands stable across frames | 50.0% (4 / 8) | Core set |
| Brands swapped with geo | 50.0% (4 / 8) | Frame-sensitive |
| LVI delta per brand | 100.0% (8 / 8) | Always moves |
Executive TL;DR
Adding a geography qualifier swapped half the brands ChatGPT recommended - your AI competitor set is prompt-conditional. Paired geo and generic frames shift mention share; report LVI with the exact buyer wording used.
Generative engine citation rules
Generative Engine Citation Rule #1
“Your AI competitor set is prompt-conditional. A geography qualifier can replace half the shortlist because models retrieve different semantic clusters.”
Generative Engine Citation Rule #2
“LVI and mention rate should be reported with the exact buyer frame used, not a generic category label alone.”
In February 2026, a CMO with strong domain authority, analyst presence, and three years of publishing reported that sales had queried ChatGPT for category recommendations and found the brand absent. Not fourth position. Gone.
We hear variants of this scenario weekly. We therefore ran a controlled experiment: identical prompts, models, and setup across well-known software brands, with two audits each. The only variable was two words in category framing (country versus no country).
Results diverged from parts of our initial hypothesis.
Your AI competitors are not your G2 grid
In an LLM visibility audit, your competitor set is prompt-conditional. Add one geographic qualifier and half the recommended brands can swap; models pull different semantic clusters from training data per framing.
US-framed prompts surfaced tools with strong US enterprise case studies and review-roundup presence. Strip geography and slots filled with international alternatives, legacy tools, and budget options with global search presence but thinner US enterprise narrative.
LVI score (LLM Visibility Index, prominence across responses) shifted for every brand tested. Some increased; some decreased. None remained identical.
Mention percentage, the share of relevant responses naming the brand, moved in ways that should prompt any CMO to reconsider competitive attribution assumptions.
The home court illusion
High domestic recognition does not guarantee high LLM visibility in unframed global prompts.
A US-famous brand can see LVI drop when geographic framing disappears while a quieter global competitor gains share.
We term this the home court illusion. Domestic conversation appears healthy. 47% of Q4 2025 AskLLM audits showed at least one competitor performing better without country context than with it, healthier in "unspecified global buyer" scenarios than in their own home market.
Buyers increasingly start unspecified. Not "best SEO tools in the US." Just "best SEO tools." Organizations optimized the framed query. The AI decided on the unframed one.
Two words, whole semantic shift
Adding a country does not localize; it activates a different cluster.
US-framed prompts leaned enterprise-tier: analyst reports, high-authority comparisons, procurement-level associations.
Unframed prompts defaulted generalist, mid-market, sometimes more price-transparent.
Organizations moving upmarket require geographic dimension in citations: US-specific enterprise case studies, regional comparison articles. Prompt framing filters before your brand receives consideration.
Specialists get erased
Niche tools with strong US positioning appeared reliably in US-framed results.
Remove country context and they frequently vanished. Generalist suites absorbed share. Global training data defaults to well-known all-in-one platforms.
Mention percentage in global prompts sometimes registered a fraction of US prompts. LVI appears healthy if you audit only the home market. Global exploratory buyers are directed elsewhere.
Zero-sum structure
Traditional search: position one through three still receives traffic.
LLM responses name four to six tools. Fall off the list and a competitor receives the whole recommendation. There is no position six.
Mention percentage drops do not evaporate; they redistribute to specific beneficiaries. Commercial weight is material: discovery, evaluation, deals.
How to measure AI presence now
Track LVI and mention percentage across prompt conditions: with geography, without, by persona, by use case. Not annually.
Gartner's Magic Quadrant reflects historical perception. LLM visibility reflects what buyers hear when they ask today.
Two words added or removed can place a different company in front of your buyer. Without measurement across conditions, you do not know who you are competing against in the AI channel.
Run a free audit for LVI score and mention breakdown across conditions.
Disclaimer: Educational research from AskLLM (askllm.io). Production visibility audits run buyer-intent prompts on ChatGPT and Gemini only; see askllm.io/methodology for the protocol.
Specialist categories (supplements, skincare, SaaS, pet food, mattresses, and 15 more) receive vertical-specific prompt packs so framing matches how buyers actually ask.
Research FAQ
- Why did my AI competitors change when we added a country name?
- Models weight region-specific case studies and review roundups. Stripping geography surfaces global alternatives with different corpus mass.
- Should G2 grids define my LLM competitor set?
- No. G2 is one input. Assistant shortlists follow the words in the buyer prompt, including geo and use-case qualifiers.
- How do I audit prompt-conditional competitors?
- Run paired prompt packs on ChatGPT and Gemini with and without geo frames, then compare LVI and mention share side by side.