CASE STUDY · GEO · ANONYMIZED · LUXURY
A Swiss luxury watchmaker, audited end to end.
Prompts › engines
Delivered
Prompt clusters
Branded
Category
Comparative
Heritage
AI engines
ChatGPT
Gemini
Claude
Perplexity
Measured
Mapped
Prioritized
Client
Anonymized — Swiss luxury watchmaker
Industry
Premium horology
Service area
SEO & GEO
Engagement type
Project (one audit cycle)
Status
Delivered
prompts across 9 topic clusters
AI models — ChatGPT · Gemini · Claude · Perplexity
mentions across a year of social listening
actions — Do Now · Do Soon · GEO-Native · Later
01 — The brief
The brand was surfacing on every branded prompt across the major AI engines — but the narrative was wrong. AI answers were pulling from outdated sources, misattributing model history, and confusing the brand with competitors in the same horological category. On generic category prompts — the kind a high-intent buyer would type without yet knowing which Maison they want — the brand was missing entirely from some of its core categories.
The ask: measure how the brand actually shows up across the AI engines today, identify where the gaps are, and give the marketing team an action plan they can sequence and execute.
02 — What we did
01
Prompt design
Built a 180+ prompt audit across nine topic clusters: branded queries (the brand by name), category queries (horological style and price tier), comparative queries (head-to-head against named competitors), heritage queries (model history, provenance, craftsmanship), and several adjacent buyer-intent clusters. The set was designed to mirror how real buyers ask AI about the category, not to maximize coverage on synthetic terms.
02
Multi-engine measurement
Every prompt run across ChatGPT, Gemini, Claude, and Perplexity. Same prompt, four engines, four answer patterns. Brand presence scored at the engine level because each engine surfaces different sources and reaches different conclusions.
03
Brand visibility scoring
For every prompt where the brand could plausibly appear, we measured whether it did — and how. Mentioned vs. recommended. Cited from the brand’s own content vs. paraphrased from someone else’s. Framed accurately vs. confused with a competitor. Every gap traced back to a content, structure, or authority root cause.
04
Social listening overlay
A year of social listening data across Reddit, Quora, and category forums — 6,000+ mentions of the brand and its competitors. Community narrative leaks into AI training corpora at much higher rates than brand-owned content; mapping that narrative was essential to understanding why the AI answers were what they were.
05
Action plan
30 prioritized actions across four time horizons — Do Now (immediate fixes the brand owns), Do Soon (structural changes in the next quarter), GEO-Native (new content built specifically for AI retrieval), Later (long-horizon authority work). Each action mapped to a specific gap, with the team that owns it named explicitly.
03 — How the engagement runs
Fixed-price project across an eight-week audit cycle. Delivered as a structured action plan plus the underlying data, plus working sessions with the marketing team to align on sequencing and ownership. The audit itself was the deliverable; execution sat with the brand’s internal team and existing content partners.
04 — What changed
“
Visibility across the four major AI engines is measurable. Gaps are mapped to specific content, structural, and authority root causes. The action plan is sequenced — the team knows what to do first, what waits, and what’s not worth doing at all.
The more substantive change: the marketing team has language for a problem that was previously hard to even describe. “AI is misrepresenting our heritage” became “on prompts X, Y, and Z, the brand is being paraphrased from outdated source A; we need to update content B and authority signal C.” The conversation moved from anecdotal to operational.
Future audit cycles can measure progress against this baseline. GEO is a measurable channel for this brand now, in a way it wasn’t before the audit.