Atlas Legal PartnersSample customer
Legal services · 60 fee earners · Mumbai · Delhi
Demo Lab / modelled programme scenario
Winning the sources that answer engines trust
A commercial law firm traced which third-party sources engines cited for its practice areas and built a targeted authority programme around them.
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
- —Regulatory constraints on marketing claims narrowed the usable content playbook.
- —Engines defaulted to general legal-information sites over first-party expertise.
- —Partners wanted evidence before committing time to content.
What we ran
- —Prompt set built around client intake questions rather than keyword volume.
- —Source-authority mapping to rank the external sites engines actually quoted.
- —Partner-bylined explainers placed with the highest-cited sources.
Before / after
Metric movement over five months
| Metric | Before | After | Change |
|---|---|---|---|
| AI Authority Score | 38 | 71 | +33 pts |
| First-party citations per 100 answers | 6 | 29 | +23 |
| Recommendation Gap vs peer set | 41 pts | 12 pts | −29 pts |
ROI summary
What it was worth
+22
Qualified enquiries per month
Tracked through the firm's intake system.
₹-6.4 L
Average matter value from AI-sourced enquiries
Rolling six-month average.
5 months
Payback period
Programme cost against fee income.
Illustrative modelling on sample data — not a projection of results for any specific brand.
Coverage
ChatGPTGeminiClaudePerplexityAI Overviews
Vertical benches currently run 5 engines; full tracking covers all 8.
More scenarios
Modelled scenario
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