Inside an AI Search Audit: What We Check and What We Fix

The $49 AI Search Audit I offer isn’t a software report. It’s a diagnostic I run manually — checking seven specific signals, testing citations directly across all four engines, and delivering a fix list ranked by impact.

Here’s exactly what happens inside that process, from the moment an order comes in to when the report lands in your inbox.

If you’re evaluating whether to get an AI search audit — or any audit from any provider — this is the benchmark for what a real one should include.

Why most “AI SEO audits” aren’t audits

The market is full of automated schema checkers being sold as AI SEO audits. You paste your URL, the tool validates your JSON-LD, and you get a PDF with a list of schema warnings.

That’s not an audit. That’s a schema validator with a price tag.

A real AI search audit answers three questions the automated tools can’t touch:

1. Are you being cited right now? Not “could you be cited” — are you actually appearing in AI-generated answers on your target queries today?
2. Why not? Is it schema? Entity recognition? E-E-A-T? Content architecture? The root cause drives the fix priority.
3. What exactly do you fix first? A prioritized list with specific instructions — not a ranked list of warnings sorted by severity score.

Those three questions require a human running real tests across real AI engines.

The seven things a real audit checks

1. Schema completeness and accuracy

Every page type on your site gets checked against the schema it should have:

• Blog posts: Article with dateModified, author → Person node, mainEntityOfPage
• About page: Person with sameAs, knowsAbout, jobTitle
• Homepage: WebSite with publisher → Person node
• Cluster articles: FAQPage with questions sourced from PAA data
• Process articles: HowTo with named steps
• Every page: BreadcrumbList

Each one validated in Google’s Rich Results Test. Errors and warnings logged with specific fix instructions.

The most common failures: dateModified never updated after initial publish; FAQPage schema with generic questions that don’t match real user queries; Person schema with sameAs pointing to dead or inconsistent profiles.

2. Entity recognition

This is the check most schema validators skip entirely. It answers: does Google (and by extension, the AI models trained on Google’s knowledge graph) recognize you as a named entity?

The practical test: search for your name or brand in Google. Does a Knowledge Panel appear? Do your social profiles show in the SERP? When you search your name in conjunction with your expertise area (“Bryan Collins SEO” or “Bryan Collins authority sites”), does your content dominate — or does someone else?

The signals I audit:

• Name consistency across all external platforms (LinkedIn, YouTube, Amazon, any published work, any guest posts)
• sameAs properties in Person schema — are they pointing to the right profiles, and are those profiles accessible?
• About page entity signals — does it clearly establish who you are, what you know, and where you can be verified?
• External mentions — are other sites citing your name in connection with your expertise area?

3. E-E-A-T signal inventory

All 14 E-E-A-T signals checked and scored across your top pages:

• Experience: first-person language, original media, case studies with real numbers, Insider Tip callouts
• Expertise: named author above H1, linked author page, credentials stated inline, primary source citations
• Authoritativeness: named entity on homepage, external mentions, topic cluster depth
• Trustworthiness: contact information, business model transparency, schema accuracy

I score each signal as present, partial, or missing. The output is a priority-ranked list showing which E-E-A-T gaps are costing the most citations — not a general statement that “E-E-A-T needs improvement.”

4. Topical coverage mapping

Your content is mapped against the full question surface for your target topic cluster.

This works by pulling People Also Ask data for your primary keywords, then checking whether your content covers each question. The gap report shows exactly which questions are being asked by your target audience that your site doesn’t currently answer — and which competitors are filling those gaps right now.

This is where most sites discover their biggest AI citation opportunity. The schema and entity work gets you technically citation-ready. The content coverage gaps show you what to write next to capture the actual citations.

5. Direct citation testing

This is what separates a real AI search audit from a schema checker.

For your top 10 target queries, I run live tests across all four engines:

• ChatGPT (with browsing enabled)
• Perplexity AI
• Claude (with search enabled)
• Google AI Overviews

I document:

• Whether your site appears in the AI-generated answer
• Whether your site is cited by name (vs. silently used)
• Which competitors are cited on queries where you’re absent
• Whether the citation is from your pillar, a spoke article, or a standalone page

This becomes your citation rate baseline. Every subsequent audit — at 30, 60, and 90 days — runs the same tests and measures change.

6. Technical crawl health

The technical issues that prevent AI engines from reliably accessing your content:

• Canonical errors — pages with canonical tags pointing to incorrect URLs, or self-referencing canonicals on paginated content
• Redirect chains — multi-hop redirects that waste crawl budget and dilute link equity
• Crawl budget waste — faceted navigation, parameter URLs, or near-duplicate pages that exhaust crawl budget before reaching your valuable content
• Indexation gaps — important pages that aren’t indexed, or indexed pages with no-cache headers that slow AI engine access

For most sites under 100 pages, technical crawl issues are minor. For larger sites, they’re often the primary barrier to citation — the content is good, but the AI engine can’t reliably reach it.

7. Internal linking audit

The internal linking pattern determines how link equity flows through the site and how clearly the topical hierarchy signals to AI engines.

The specific failure I look for: cluster spokes that link only to a conversion page (like /contact or /order/) instead of linking back to the pillar page. This is the most common internal linking mistake on cluster builds. It drains equity from the hub, suppresses topical authority signals, and reduces citation likelihood across the entire cluster.

The fix: every spoke links to its pillar three to four times, with anchor text including the primary keyword. The pillar links down to every spoke. AI SEO Services: The Complete Guide covers the full internal linking architecture in more detail.

What the deliverable looks like

The audit output is a structured document, not a spreadsheet dump. It includes:

Section 1 — Citation rate baseline Table of top 10 queries × 4 engines. Cited (yes/no), position in response, URL cited.

Section 2 — Schema audit Page-by-page table: page type, schema present, validation status, specific errors, fix instructions.

Section 3 — Entity and E-E-A-T scores Each of the 14 E-E-A-T signals rated present/partial/missing. Priority-ranked fix list.

Section 4 — Topical coverage gaps PAA questions mapped against existing content. Gap list with recommended article titles and target keywords.

Section 5 — Technical findings Any crawl, canonical, or redirect issues found. Severity rating and fix instructions.

Section 6 — Prioritised action plan Everything ranked by impact-to-effort ratio. The top 10 items are the 90-day focus. The rest are the backlog.

The document is yours to keep and execute on — whether you work with me afterward or not.

What happens after the audit

Immediate wins (weeks 1–2)

The schema fixes and entity signal updates can usually be implemented within the first two weeks. These are technical changes — not dependent on content production. You implement them, Google crawls the updates, and citation rate checks at day 30 typically show measurable improvement on the queries where schema was the blocker.

Medium-term wins (months 1–3)

E-E-A-T signal improvements — adding first-person language to existing articles, restructuring content for direct-answer format, improving author page signals — take one to three months to register in AI citation patterns.

Long-term compounding (months 3–9)

Filling topical coverage gaps — writing the cluster spokes that answer the questions your competitors are currently owning — takes sustained content production. Each new spoke builds the cluster’s topical authority at the hub level. This is the highest-leverage work, and the longest time horizon.

Most sites that implement the full audit roadmap see citation rates improve measurably within 90 days on schema-related queries, and continue improving over 6–12 months as the content architecture fills in.

The difference between a real audit and a software report

Frequently asked questions

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