Technical SEO audit checklist for search and AI visibility

A technical SEO audit checklist covering access, indexation, rendering, architecture, schema, performance, analytics and production evidence.

Citable Agency

Editorial team 3 min read

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In this article
  1. 01 1. Access and crawl controls
  2. 02 2. Indexation and canonicalization
  3. 03 3. Rendering and extractability
  4. 04 4. Information architecture and internal links
  5. 05 5. Structured data and entity consistency
  6. 06 6. Performance and accessibility
  7. 07 7. Measurement readiness
  8. 08 8. Implementation tickets
  9. 09 What the final audit should deliver

A technical SEO audit should make implementation easier. If the final document contains hundreds of issues but no page groups, evidence, owners or acceptance criteria, it has described a site without creating a delivery plan.

Use this checklist to evaluate both conventional search foundations and the technical conditions that affect AI retrieval.

1. Access and crawl controls

Verify robots.txt, page-level robots directives, authentication boundaries, firewall behaviour and relevant bot access. Test live responses instead of relying only on configuration files. Record redirects, status codes and any difference between user and crawler access.

For AI retrieval, document which bots are intentionally allowed or restricted. Do not assume every AI product uses the same crawler or that access automatically creates citations.

2. Indexation and canonicalization

Map indexable page groups and compare them with what search engines appear to index. Review canonicals, redirects, pagination, query parameters, duplicate templates, staging paths and soft-error patterns.

The key question is not “how many URLs are indexed?” It is “are the right commercial and evidence pages indexable under one stable canonical URL?”

3. Rendering and extractability

Compare source HTML, rendered HTML and visible content on representative templates. Important service facts, headings, links and structured data should not depend on fragile client-side execution.

Check heading order, answer-first section openings, table semantics and whether critical content remains understandable without visual context. Extraction is a content-and-template property, not a schema switch.

Map how the homepage, service pages, audience pages, resources and articles connect. Flag orphan pages, deep commercial pages, duplicated hubs and generic anchor text.

Internal links should explain relationships: a technical audit article should lead to the audit service; a small-business GEO guide should lead to the relevant audience page; supporting articles should link back to their hub.

5. Structured data and entity consistency

Validate Organization, Service, Article, Breadcrumb and other relevant schema against visible content. Check stable entity IDs, canonical URLs, names, descriptions and supported sameAs references.

Remove unsupported ratings, invented service areas and properties that exist only in JSON-LD. Structured data should make existing facts easier to interpret.

6. Performance and accessibility

Review performance where it affects crawling, rendering or conversion. Separate template-wide causes from one-page anomalies. Include accessibility issues that block navigation, form use or content comprehension.

Avoid turning a performance report into a list of laboratory scores. Name the affected experience, technical cause, implementation dependency and validation method.

7. Measurement readiness

Test analytics loading, consent behaviour, form events, calls, bookings or other meaningful conversions. Document what cannot be measured reliably.

For AI Search, define the query panel and observation fields separately from web analytics. Referral traffic can support the picture but does not capture unlinked mentions or zero-click influence.

8. Implementation tickets

For every priority, record:

  • the affected page group or template;
  • the observed evidence;
  • commercial and discovery risk;
  • recommended change;
  • owner and dependency;
  • acceptance criterion;
  • post-deployment validation.

Severity should combine impact, confidence and effort—not simply inherit a crawler tool’s default label.

What the final audit should deliver

The final output should contain an executive diagnosis, evidence appendix, severity-graded backlog and a bounded sequence of work. It should state what was not tested and where access limited confidence.

Citable’s Technical SEO capability turns approved findings into production changes. If you need the broader commercial and AI-answer baseline first, compare the three Diagnose levels.

Frequently asked

Questions buyers ask before booking

What should a technical SEO audit include?

It should examine crawler access, indexation, status codes, canonicalization, rendering, information architecture, internal links, structured data, performance, accessibility and measurement. Findings should include affected URLs, evidence, severity, ownership and validation criteria.

Is a crawler export enough for an audit?

No. A crawl is one evidence source. A complete audit also checks rendered pages, templates, analytics, search-console evidence, server behaviour and the business importance of affected page groups.

Should the audit include implementation?

Only when the agreement says so. An audit can equip an internal team or include a bounded implementation workstream. The boundary should be explicit before work begins.

Production review

Technical SEO and AI Search audit

  • Crawler access and robots directives verified
  • Indexation, canonicals and status codes sampled
  • Rendered HTML compared with user-visible content
  • Information architecture and internal links mapped
  • Structured data validated against visible facts
  • Performance and accessibility blockers prioritised
  • Analytics and conversion events tested
  • Each priority assigned an owner and acceptance criterion

Ready to diagnose your AI Search gaps?

Use the free checker for a quick structural signal. Start with Diagnose when you need an evidence-led baseline and a decision-ready next step.

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