Meridian Dental GroupSample customer
Healthcare · Multi-clinic · 18 clinics · India · Metro clusters
Implementation story
Location-level AI visibility across eighteen clinics
A multi-location dental group built a per-clinic view of how assistants answer local recommendation prompts, then fixed the entity and review signals behind the weakest sites.
Sample dataSample data — the companies, people, quotes, logos and numbers on this page are placeholders used to illustrate the format. They are not real CiteRank AI customers or results.
“Patients now ask an assistant before they ask a friend. We needed to know which of our clinics got named — and which did not.”
The challenge
- —Local recommendation prompts returned aggregator directories rather than the group's own clinics.
- —Listing data drifted between locations, so engines could not resolve them as one brand.
- —No way to compare performance clinic-by-clinic for the marketing budget conversation.
What we ran
- —Location-split prompt sets covering treatment, price and 'near me' intents.
- —Entity-health review of every clinic listing, profile and schema block.
- —Quarterly source programme targeting the local publications engines quoted most.
Before / after
Metric movement over four months
| Metric | Before | After | Change |
|---|---|---|---|
| Clinics named in local prompts | 5 of 18 | 15 of 18 | +10 clinics |
| Entity Health Score | 44 | 82 | +38 pts |
| AI Mention Share vs local set | 9% | 34% | +25 pts |
ROI summary
What it was worth
+180/mo
New-patient enquiries citing an AI assistant
Self-reported at intake across all locations.
₹9.6 L/yr
Marketing spend reallocated from directories
Directory listing renewals not repeated.
3 months
Payback period
Programme cost against enquiry value.
Illustrative modelling on sample data — not a projection of results for any specific brand.
Coverage
ChatGPTGeminiPerplexityAI OverviewsCopilotMeta AI
Vertical benches currently run 6 engines; full tracking covers all 8.
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