Kestrel PaymentsSample customer
Fintech · 420 employees · India · SEA
Demo Lab / modelled programme scenario
Displacing comparison sites in regulated answers
A payments platform shifted engine answers away from aggregator comparison pages by fixing structured data and earning citations from the sources engines already trusted.
Sample dataModelled scenario — the company names and numbers here are illustrative examples of how a CiteRank programme is reported. They are not real CiteRank AI customers, and no result shown has been measured for a named client.
The challenge
- —Comparison and aggregator sites monopolised answers for pricing and integration prompts.
- —Compliance review slowed any change to public claims.
- —Engine answers disagreed with each other about the product's own feature set.
What we ran
- —Structured-data and documentation cleanup so every engine read the same product facts.
- —Scheduled monitoring runs split by regulated vs non-regulated intents for faster compliance sign-off.
- —Targeted corrections with the handful of sources responsible for most citations.
Before / after
Metric movement over seven months
| Metric | Before | After | Change |
|---|---|---|---|
| AI Recommendation Share | 11% | 39% | +28 pts |
| Factually accurate product answers | 58% | 94% | +36 pts |
| AI Trust Score | 47 | 79 | +32 pts |
ROI summary
What it was worth
+1,340/qtr
Self-serve signups from AI-sourced sessions
Measured against the prior quarter baseline.
−64%
Support tickets from incorrect AI answers
Ticket taxonomy over two quarters.
6 months
Payback period
Programme cost against signup value.
Illustrative modelling on sample data — not a projection of results for any specific brand.
Coverage
ChatGPTGeminiClaudePerplexityAI OverviewsCopilotGrokMeta AI
Vertical benches currently run 8 engines; full tracking covers all 8.
More scenarios
Modelled scenario
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