Kirgo GEO case study
Kirgo is a SaaS company whose product pages were heavily JavaScript-dependent, limiting what AI crawlers could read. Its pre-optimization GEO score was 32 out of 100, the lowest of our seed cohort.
Where Kirgo started before optimization.
Client overview
Kirgo is a SaaS company whose product pages were heavily JavaScript-dependent, limiting what AI crawlers could read. Its pre-optimization GEO score was 32 out of 100, the lowest of our seed cohort.
Industry: SaaS
Website: https://kirgo.com
Problem
Client-side rendering hid most content from AI crawlers, and the site had no structured data describing the product, organization, or FAQs.
GEO score before audit
Kirgo’s GEO score before optimization was 32/100.
The GEO score is the agency’s own 0–100 measure of how readable, structured, and citable a site is to AI search engines, combining citability, schema coverage, technical access for AI crawlers, and content authority signals.
Methodology
- 01Audited server-side rendering and JavaScript dependency
- 02Tested rendered vs. raw HTML available to AI crawlers
- 03Reviewed schema coverage and content authority signals
Findings
- Core content only available after client-side JavaScript execution
- Zero structured data on key product and pricing pages
- No author or organization authority signals
Recommendations
- Move to server-side rendering / static generation for key pages
- Add Organization, Product, and FAQPage schema
- Introduce authored, credentialed content with Person schema
Frequently asked questions
- What was Kirgo’s GEO score before optimization?
- Kirgo scored 32 out of 100 on its initial Generative Engine Optimization audit.
- How did JavaScript rendering hurt Kirgo’s AI visibility?
- Most of Kirgo’s content only appeared after client-side JavaScript ran, so AI crawlers that read raw HTML saw little usable content to cite.
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