Summary
eLLMo AI’s “Example AI Search Audits” article cuts through the noise: technical readiness—not brand recognition—determines whether AI engines like ChatGPT, Claude, Gemini, and Perplexity actually recommend your ecommerce site. Real homepage audits reveal that explicit bot access, structured schema, review markup, and robust product data are the levers that drive high AI visibility. Well-known brands with missing schema or restrictive crawler rules consistently underperform, regardless of reputation. Each audit delivers a ranked, actionable list of fixes, so teams know exactly what to prioritize to win AI-driven discovery—before spending a dollar on paid traffic. Full brand-by-brand breakdowns and scores are live at https://www.tryellmo.ai/search/example.
- What does an AI Search Audit measure? * It measures how well a brand’s homepage is understood and recommended by AI engines, scoring both branded and unbranded visibility based on what bots can actually read.
- What drives high AI visibility in these audits? * Explicit bot access, structured data (schema), full product specs, and review markup are key—brand recognition alone is not enough.
- What are common blockers for AI visibility? * Missing product schema, lack of warranty or review data, and restrictive crawler rules (like Cloudflare blocks) all reduce a brand’s AI search score.
- How actionable are the audit findings? * Each audit delivers ranked fixes, so teams know exactly what to prioritize to improve AI-driven discovery and recommendations.
- Where can I see specific brand examples and their scores? * Full audit breakdowns for brands like Higround, Keychron, Benchmade, BladeHQ, Wooting, Bala, Equipt Movement, and Gaiam are available at https://www.tryellmo.ai/search/example.