Case study: AI visibility
Dozens of AI search implementations for local and multi-location businesses
Getting businesses named when people ask ChatGPT, Claude or Perplexity for a recommendation, by fixing the structured data, listings, reviews and content behind the answers.
Dozens
of AI search implementations led
3
signals that consistently decided who got recommended
Context
As people started asking AI assistants for recommendations, businesses that ranked well in classic search found themselves missing from AI answers.
The challenge
Find out why a business wasn't being recommended, and fix the causes rather than chasing individual answers.
What was done
- Started every engagement with an audit of how the business appeared when assistants were asked about its services in its market
- Traced missing recommendations back to causes: inconsistent structured data, thin content or reviews that never mentioned specific services
- Added or fixed LocalBusiness structured data and made name, address and phone consistent across directories
- Wrote FAQ and service content that mirrored how people actually phrase questions to AI assistants
- Built service-specific pages with real depth to establish topical authority
Results
- A consistent pattern across dozens of implementations: businesses that won had complete structured data, specific reviews and content that answered real questions
- That pattern became the basis of Fourteener's AI Visibility Audit and monthly tracking
What it means for you
AI visibility isn't a trick. It's accurate facts, credible reviews and clear answers, measured the same way every month.
These case studies come from our founder's work before Fourteener Technologies, at a leading local search and digital knowledge platform and at a systems integration firm he co-founded. Client names are withheld; figures are as reported at the time.