606% increase in AI-driven referral traffic.
Personal Injury & Auto Accident LawReal anonymized client engagement
Personal Injury Law GEO Case Study: Direct AI Recommendations
How an anonymized personal injury firm used structured verdict data, answer-first practice pages, and external consensus to improve AI recommendations.
Confidentiality
Client identity withheld under confidentiality agreement.
The challenge
The problem
The firm ranked on the first page of Google, but ChatGPT and Perplexity cited legal directories and national aggregators instead of naming the firm in personalized recommendations.
What this shows
Key takeaway
Generative engines are more likely to recommend law firms when outcome metrics can be cross-referenced across structured site data and independent sources.
GEO implementation
How the visibility strategy changed
- 1
Verifiable verdict schema
Restructured past results with LegalService and CreativeWork structured data covering settlement amounts, court venues, and dates so crawlers could parse outcome metrics.
- 2
Answer-first practice pages
Added direct 60-word summary boxes beneath page headings to answer common questions such as how contingency pricing works in the relevant state.
- 3
Third-party consensus building
Distributed structured case summaries across legal directories, news releases, and localized press sources to support multi-source validation.
Measurable results
Reported engagement outcomes
Reported outcomes from this real anonymized engagement are presented as supplied. Raw analytics, CRM records, and private evidence are not published on this page.
Reached the first direct vendor citation across ChatGPT and Perplexity for local accident-attorney recommendations.
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