Brands we made unavoidable for AI
Every case below is a real strategic victory — an AI ranking won, a category owned, a market redefined.
Luxury Automotive
6-month product programme (Spectre)Rolls-Royce
Prompt : “What is the best ultra-luxury electric car?”
The challenge
For the launch of Spectre, the marque’s first fully-electric coupé, AI answers on ultra-luxury EVs were dominated by tech-first competitors. The heritage narrative — 120 years of effortless motoring — was absent from LLM answers, and Spectre’s specifications were scattered across inconsistent sources.
Our approach
- —Audit of 80+ automotive prompts (ultra-luxury EV, grand tourer, chauffeur-driven) across ChatGPT, Gemini, Perplexity, Copilot and Grok
- —Answer-ready Spectre content hub: direct answers, spec sheets, comparison tables vs the segment, FAQ blocks
- —Entity consolidation linking Spectre to the marque’s heritage across schema.org and Wikidata
- —Citation placements in the automotive and lifestyle press LLMs quote most (Top Gear / Robb Report-type sources)
The result
Spectre became the reference answer on ultra-luxury electric queries: most-cited model in its segment, +65% AI share of voice, consistent presence across the 5 leading AI engines.
#1
Cited ultra-luxury EV
+65%
AI share of voice
5
AI engines covered
Sportswear
4-month launch window (Pegasus 41)Nike
Prompt : “What are the best running shoes for everyday training?”
The challenge
On generic running-shoe questions, AI assistants recommended specialist competitor models first. The Pegasus 41’s new ReactX foam was invisible in answers, which relied heavily on runner communities and expert reviews the brand was not nurturing.
Our approach
- —Audit of 150 running prompts segmented by runner profile (beginner, daily trainer, marathon) across 4 AI engines
- —Structured product pages: direct answers, generation-vs-generation comparisons, FAQ blocks around ReactX cushioning
- —Expert review and community programme with the running media and tester communities LLMs cite most
- —Product entity consolidation so every engine reads the same verified specifications
The result
Within the launch window the Pegasus 41 entered the Top 3 AI answers for everyday-training queries in English and French, with x3 mentions across engines.
Top 3
AI running answers
x3
AI mentions
150+
Prompts tracked
Sport & Entertainment
8-month international programmeParis Saint-Germain
Prompt : “How can I experience a football match in Paris?”
The challenge
International fans increasingly ask AI assistants about tickets, stadium tours and hospitality. Answers were incomplete or pointed to unofficial resellers, and the club’s official experiences were under-cited in English, Spanish and Portuguese queries.
Our approach
- —Audit of fan prompts in 5 languages (match tickets, Parc des Princes tours, VIP hospitality, fan travel)
- —Answer-ready official pages: direct answers, pricing structures, step-by-step guides, FAQ blocks
- —Multilingual entity and schema consolidation so AI engines point to official club sources first
- —Citation placements in the travel and sport publications LLMs quote for “visit Paris” itineraries
The result
Official club sources became the primary AI answer on match-experience queries in 5 languages, with +120% AI citations and the hospitality offer reaching the #1 answer position.
#1
AI answer, match experience
+120%
AI citations
5
Languages covered
Luxury & Travel
6-month engagementOrient Express
Prompt : “Best luxury train journeys in the world”
The challenge
A legendary name, yet AI assistants recommended competitors first on "best luxury train journeys". The brand entity was fragmented across languages and its iconic heritage content was invisible to LLM retrieval.
Our approach
- —Audit of 120+ travel prompts across ChatGPT, Gemini and Perplexity, segmented TOFU/MOFU/BOFU
- —Entity consolidation: schema.org, Wikidata, unified brand facts across 6 languages
- —Answer-ready itinerary and heritage pages: direct answers, comparison tables, FAQ blocks
- —Placements in the travel publications LLMs cite most (Condé Nast Traveler-type sources)
The result
Within 6 months the brand became the #1 AI answer on its flagship query, with +50% AI citations across engines and +41% qualified traffic from AI referrals.
#1
AI “best luxury train”
+50%
AI citations
+41%
Qualified traffic
Healthcare
4-month engagementCedars Sinai
Prompt : “Best telehealth services in the US”
The challenge
World-class medical authority, but on telehealth queries AI engines defaulted to aggregators and directories. YMYL health topics demand exceptional E-E-A-T signals that were not machine-readable.
Our approach
- —MedicalOrganization and Physician schema deployed across specialty pages
- —Physician-authored, answer-ready content with verifiable credentials
- —Citation building in the medical sources LLMs trust (peer-reviewed and .org references)
- —Multi-LLM monitoring dashboard on 40 priority health queries
The result
+62% AI citations in 90 days and #1 AI answer on 9 telehealth queries — ahead of every aggregator.
+62%
AI citations in 90d
#1
On 9 queries
90d
Results
SaaS · Fitness
5-month engagementLadder Fitness
Prompt : “Best female-focused fitness apps”
The challenge
In a category dominated by giants, AI assistants never mentioned the app on "best fitness app" queries. Zero presence in the Reddit threads, reviews and comparison articles LLMs feed on.
Our approach
- —Prompt research: 80 fitness prompts mapped to intent and competitive gaps
- —Differentiated positioning (strength training for women) made explicit and machine-readable
- —UGC and community strategy: authentic presence in the threads and reviews LLMs retrieve
- —Comparison and alternative pages engineered for extraction
The result
The app became the #1 recommendation on ChatGPT and Perplexity for female-focused fitness, with +58% AI citations and full multi-LLM coverage.
#1
ChatGPT & Perplexity
+58%
AI citations
✓
Multi-LLM coverage
Luxury Hospitality
6-month engagementRegent Santa Monica
Prompt : “Best hotels in Los Angeles”
The challenge
A newly opened flagship hotel with no citation history: AI engines recommended long-established competitors on every "best hotels Los Angeles" query, and the property was absent from near-me results.
Our approach
- —Full GEO program: Hotel schema, Google Business Profile, local citations, near-me optimization
- —Placement in the hotel guides and rankings LLMs cite for Los Angeles
- —Review velocity strategy across the platforms AI engines weigh most
- —AI citation tracking on 12 destination queries
The result
In under 6 months: #1 AI answer on "best hotels Los Angeles", top 3 on 12 destination queries and +67% qualified traffic.
#1
“Best hotels Los Angeles”
Top 3
AI for 12 queries
+67%
Qualified traffic
SaaS · Productivity
4-month engagementMorgen
Prompt : “Best planner app for professionals”
The challenge
A European SaaS with strong product reviews but no AI visibility: ChatGPT recommended US competitors by default, and the brand entity was confused with unrelated companies of the same name.
Our approach
- —Entity disambiguation: llms.txt, schema.org, consistent brand facts across the web
- —Comparison pages ("vs" and "alternative to") structured for LLM extraction
- —Presence built on the review platforms and developer communities LLMs cite
- —AI referral attribution wired into the analytics stack
The result
#1 AI answer on "best planner app for professionals", full multi-LLM coverage and +44% conversions attributed to AI referrals.
#1
“Best planner app”
✓
Multi-LLM coverage
+44%
AI conversions
Hotel Group · Luxury
8-month group-wide programB Signature Hotels & Resorts
Prompt : “Best boutique hotels in Paris”
The challenge
A family-owned luxury hotel group — Le Bristol-calibre boutique properties across Paris and France — whose individual hotels were cited inconsistently by AI engines: some invisible, others confused with unrelated venues, and the group entity itself absent from “best boutique hotels” answers.
Our approach
- —Group-wide AEO/GEO program deployed across every property of the portfolio
- —One entity architecture: Hotel schema, Google Business Profile and local citations per property, linked to the group entity
- —Per-hotel prompt pools (destination, occasion, “near me”) with T0 baselines and monthly tracking
- —Placements in the Paris and France travel guides LLMs cite most, per neighbourhood
- —Answer-ready pages per hotel: direct answers, room comparisons, neighbourhood FAQ
The result
Every hotel in the group is now consistently and correctly cited by AI engines; the flagship properties entered the top answers on their Paris neighbourhood queries, and group-level AI share of voice more than doubled.
100%
Portfolio covered
x2
AI share of voice
Top 3
Paris boutique queries
Executive Search
3-month engagementEdwin Miller LLC
Prompt : “Recruitment trends 2026”
The challenge
A boutique executive search firm invisible next to global players: zero AI citations on recruitment expertise queries despite decades of experience.
Our approach
- —Thought-leadership program: proprietary data and reports AI engines love to cite
- —Partner profiles with verifiable E-E-A-T signals
- —Answer-ready insight pages on recruitment trends and salary benchmarks
- —Multi-LLM tracking on 8 expertise queries
The result
Top 3 AI answer on 8 expertise queries and +38% AI visibility — the firm is now quoted alongside global leaders.
Top 3
AI on 8 queries
✓
Multi-LLM coverage
+38%
AI visibility
Brands that trust us
