Verdanta LivingSample customer
Real estate · 240 employees · Bengaluru · Hyderabad
Implementation story
Owning the long research window before the site visit
A residential developer tracked project-level prompts across two cities and corrected the outdated inventory facts engines were repeating.
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.
“Buyers research for months inside an assistant before they ever fill a form. We had no idea what it was telling them.”
The challenge
- —Engines repeated stale pricing and availability from third-party portals.
- —Project microsites were not resolvable as part of one brand entity.
- —Sales attributed nothing to AI because nothing was measured.
What we ran
- —City- and project-level prompt sets refreshed weekly.
- —Canonical fact sheets per project, mirrored into structured data.
- —Portal correction programme for the highest-cited third-party listings.
Before / after
Metric movement over five months
| Metric | Before | After | Change |
|---|---|---|---|
| Projects named in city prompts | 2 of 11 | 9 of 11 | +7 projects |
| Outdated facts in answers | 63% | 11% | −52 pts |
| AI Visibility Score | 26 | 59 | +33 pts |
ROI summary
What it was worth
+310/qtr
Site visits attributed to AI-sourced research
Captured through the visit booking form.
−19%
Cost per site visit
Blended across paid and organic.
4 months
Payback period
Programme cost against booked visits.
Illustrative modelling on sample data — not a projection of results for any specific brand.
Coverage
ChatGPTGeminiPerplexityAI OverviewsCopilot
Vertical benches currently run 5 engines; full tracking covers all 8.
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