30 → 75 GEO Score in 30 Days

The problem

The starting GEO score was 30/100 — the charity was invisible to AI citations. Schema markup was broken or missing on most pages, with no structured data for charity / donation entities. Meta descriptions were generic and keyword-sparse. No FAQ schema meant AI assistants couldn't extract Q&A pairs. There was no llms.txt to signal AI crawlers, and trust signals (UEN, IPC, charity registration number) were buried in the footer instead of surfaced for AI extraction.

Approach

• Phase 1 — Foundation (+18 pts): Full schema rebuild (Organization, NGO, Service, FAQPage), canonical tag fixes, meta / H1 cleanup, trust signals surfaced in headers + Open Graph.
• Phase 2 — Content (+12 pts): FAQ schema deployment on key pages, complete service page rebuild, Twitter / Open Graph cards, llms.txt deployment.
• Phase 3 — Authority (+8 pts): Backlink audit, citation building, press kit creation, About page rebuild for authority signals.
• Phase 4 — Monitoring (+7 pts): AI citation tracking via Tavily MCP, score validation, iteration playbook handover.
• Used custom Python for schema generation and Claude + structured prompts for FAQ extraction to speed iteration.
• Every change validated in Schema.org Validator before deployment. Scored before each phase to attribute lift correctly.

Results

• Final GEO score: 75/100 — top quartile for the charity / NGO vertical, ready for AI-assistant citations.
• 12 phases shipped end-to-end: Audit → Schema → Content → Authority → Monitoring, with a fully documented playbook for replication.
• 100% reproducible — the same methodology is now being applied to GER Singapore (sprint started May 11, 2026).
• Moved from invisible-to-AI to high-quotability status for ChatGPT, Gemini, Perplexity, and Claude queries about zakat platforms and Asian charities.

Tech stack