Search
Understand how content is discovered, ranked, reused and connected in classic search engines and AI-generated answers.
Why Beyond Mentions
GEO does not stop when your brand gets mentioned.
Beyond mentions. All the way to the shortlist.
Beyond Mentions exists to measure and correct what happens after visibility: the criteria AI systems retain, the proof they reuse, the competitors they associate with your market and the reasons that can move you into or out of a shortlist.
The finding
Our SEO and GEO experience led us to a simple limit: being cited does not mean being preferred.
After analyzing business performance across dozens of sites, we saw the same gap appear: dashboards can tell whether a brand gains visibility, but not whether it is compared with the right criteria, retained in the right category or recommended with the proof that justifies its value.
With LLMs, that gap becomes more critical. AI does not only list brands. It prepares the decision: it summarizes the market, builds a comparison grid, identifies risks, selects proof and sometimes proposes a shortlist before the first sales conversation.
The real risk is not only invisibility.
The real risk is being visible inside a grid that commoditizes your offer, ignores your proof or compares you with players that do not operate at the same level of requirement.
The subject sits at the intersection of search, LLMs, knowledge graphs and business.
LLMs
Observe how ChatGPT, Claude, Gemini, Perplexity or DeepSeek phrase criteria, associate competitors and stabilize a recommendation.
Knowledge graph
Make entities, relationships, categories, proof and sources explicit enough for AI to position an offer correctly.
Business
Connect AI visibility to what matters commercially: margin, preference, shortlist quality, differentiation and commoditization risk.
Our position
Beyond Mentions is among the first firms to treat GEO as a decision problem, not only as a citation problem.
Mentions remain useful. They indicate that a brand exists in an answer. But in complex B2B markets, the decisive question comes next: does AI understand why you should be chosen, with which criteria, which proof, against which competitors and in which category?
A method built to move from AI answer to correction plan.
- 01
Build the buyer-query corpus
Framing, comparison, risks, proof, shortlist, specification and objections. The corpus starts from what buyers actually ask before speaking to sales.
- 02
Capture multi-LLM answers
The same scenarios are tested across multiple models to distinguish recurring signals from isolated answers.
- 03
Extract criteria and proof
We isolate retained criteria, proof used, competitors associated, categories applied and reasons for inclusion or exclusion.
- 04
Compare the AI grid with business reality
The goal is not to fix a sentence. The goal is to see whether AI understands your level of requirement, thresholds, differences and real competitors.
- 05
Correct and re-measure
The documentation plan is prioritized, executed with your teams or agency, then measured again to track how the grid moves.
Proprietary tools
We built our own tools because citation dashboards do not answer the right question.
Our tools do not only count mentions. They read the decision logic produced by AI systems, compare it with your business reality and turn the gaps into executable documentation corrections.
- Extraction and clustering of AI-mediated buying criteria.
- Decision presence and proof reuse scoring.
- Category compression and wrong-neighborhood competitor detection.
- Generated shortlist tracking by model, persona and buying scenario.
- Executable documentation correction matrices.
What changes
A Beyond Mentions audit does not only answer “are we cited?”.
It answers:
- Which criteria does AI use to compare us?
- Which proof does it ignore even though it is decisive?
- Which competitors frame the grid in our place?
- In which situations are we retained or excluded?
It produces:
- a measured baseline;
- a competitive matrix;
- a prioritized correction plan;
- executable documentation briefs;
- ongoing re-measurement.
Let us discuss your market
If AI systems already cite you, the real question is what they do with you next.
A first call checks whether your market deserves a Beyond Mentions audit: decision complexity, proof requirements, buying cycle, AI shortlist exposure and commoditization risk.