Direct Answer: An AI Visibility Audit systematically inspects your website across four operational pillars: (1) Discover (Crawler Access): verification of robots.txt, HTTP status codes, and latency for 10+ AI crawlers; (2) Understand (Semantic Structure): validation of JSON-LD schema graphs (Organization, Service, FAQPage), entity disambiguation, and heading hierarchies; (3) Trust (Machine Readability & Evidence): direct-answer formatting, data citations, and /llms.txt availability; and (4) Recommend (Citation Share): empirical share-of-voice testing across ChatGPT, Perplexity, and Gemini for target commercial queries. It identifies exact barriers preventing AI systems from citing your business.
Why Traditional SEO Audits Leave You Blind in AI Search
Traditional SEO audits inspect metadata length, keyword density, broken links, and backlink authority designed for Google's 10 blue links. While technical health remains vital, traditional audits cannot tell you if a large language model can extract your service pricing, whether your schema graph connects to external knowledge bases, or if an AI crawler is blocked by your firewall.
An AI Visibility Audit inspects machine legibility, semantic graph integrity, and multi-engine citation presence.
The 4 Operational Pillars of an AI Visibility Audit
Pillar 1: Discover (Crawler Access & Infrastructure)
- Verification of
robots.txtrules for 10+ AI crawlers (GPTBot, ClaudeBot, PerplexityBot, Google-Extended, Applebot-Extended, Amazonbot, Meta-ExternalAgent, Bytespider, CCBot). - Server-level WAF response codes, checking for silent 403 Forbidden or CAPTCHA barriers.
- HTTP response times and clean server rendering for headless scrapers.
Pillar 2: Understand (Semantic Graphs & Entity Clarity)
- Validation of JSON-LD schema graph connectivity using consistent
@idanchors. - Presence of specialized schema subtypes (e.g.
Dentist,LegalService,LogisticsServicevs. genericLocalBusiness). - Verification of authoritative
sameAslinks connecting your domain to ACRA, SGDI, LinkedIn, and official industry registrations.
Pillar 3: Trust (Extractable Content Architecture)
- Evaluation of answer-first content density: testing whether core commercial queries are answered in the first two sentences.
- Validation of structured HTML tables and numbered process steps.
- Inspection of
/llms.txt(proposed community convention) for machine-readable site navigation.
Pillar 4: Recommend (Empirical Citation Share-of-Voice)
- Execution of our standardized 24-prompt battery across ChatGPT (GPT-4o), Perplexity, and Google Gemini.
- Measurement of brand citation frequency, placement prominence, and competitive displacement.
Sample Audit Findings: Before & After Remediation
In a recent diagnostic evaluation of a Singapore aesthetic healthcare clinic:
| Audit Pillar | Day 0 (Initial Baseline) | Day 60 (Post-Remediation) |
|---|---|---|
| Discover (Crawler Access) | Score 20/100 (WAF blocking GPTBot) | Score 95/100 (Clean crawler policies) |
| Understand (Schema) | Score 15/100 (Missing JSON-LD) | Score 90/100 (MedicalBusiness + FAQPage) |
| Trust (Extractability) | Score 30/100 (Slogan copy) | Score 85/100 (Answer-first pricing tables) |
| Recommend (Citations) | Score 0/100 (0 citations in 24 prompts) | Score 62/100 (15 citations in 24 prompts) |
| Overall Score | 16 / 100 (Critical) | 83 / 100 (Dominant) |
Frequently Asked Questions
Next Step: Learn about our AEO & Schema Structuring service, explore How We Work, read definitions in our Glossary (such as Answer-First Content and llms.txt), or run your Free AI Visibility Audit now.