An AI visibility audit tests whether ChatGPT, Perplexity, and Google AI Overviewsname your business for target queries, then scores entity consistency, schemacoverage, GBP alignment, and answer-first content against a baseline evidence packyou can re-run monthly — covering citation testing, entity checks, schemavalidation, and reporting.
Why should local businesses run an AI visibility audit?
Most owners discover AI search gaps only after losing leads to competitors cited in ChatGPT or Google AI Overviews. A structured audit reveals whether answer engines can find, trust, and name your business before you invest in content or ads.
How is an AI audit different from a traditional SEO audit?
Traditional audits focus on crawl errors, rankings, and backlinks. AI audits add citation testing, passage extractability, entity graph completeness, and GBP-to-website alignment.
How often should you re-run the audit?
Monthly for citation queries; quarterly for full entity and schema reviews unless you ship major site or GBP changes.
How do you test AI citations for your business?
Run the same 10 target queries in ChatGPT, Perplexity, and Google AI each month. Record whether your business is named, which competitors appear, and what sources are cited in footnotes or links.
What queries should a plumber in Surrey test?
Examples: best emergency plumber in Surrey, who fixes burst pipes near White Rock, and recommended HVAC companies in Langley. Use natural language — how customers actually ask.
Should you use incognito or logged-out sessions?
Yes. Test in fresh sessions without personalization bias. Screenshot results with timestamps for your evidence pack.
What entity signals should the audit score?
Score NAP consistency, GBP category depth, service-area definitions, founder Person schema, and sameAs links to verifiable profiles. Entity mismatches are the fastest way to lose AI citation confidence.
What is a common entity failure for service-area businesses?
Website lists five cities but GBP service areas show three, or the business name differs slightly between directories and schema.
How do you score content for AI extractability?
Each service and location page should open with a direct answer, use question-style H2 headings, include FAQ schema on real customer questions, and link internally to related services and city pages.
What pages fail extractability most often?
Homepages with vague marketing copy, location pages duplicated across cities, and service pages that bury the answer below long intros.
What is the step-by-step workflow?
- Define target queries: List 10 questions customers ask before hiring you in each service area.
- Run citation tests: Query ChatGPT, Perplexity, and Google AI. Screenshot whether your business is named.
- Audit entity consistency: Compare NAP, services, and service areas across website, GBP, and top directories.
- Validate schema graph: Check Organization, LocalBusiness, Person, Service, and FAQPage JSON-LD with Rich Results Test.
- Score content extractability: Verify each key page leads with a direct answer under question-style H2 headings.
- Document baseline evidence: Archive screenshots, crawl exports, and schema validation results in one folder.
- Prioritize fixes: Fix entity mismatches and missing schema before publishing new content.
- Re-test monthly: Re-run the same queries and compare before/after citation evidence.