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Check the evidence behind an AI SEO recommendation, separate requirements from performance promises, and decide what to implement, test, or reject.

Understand how model weights, retrieval, vector search, context selection, generation, and citations can combine to produce an AI answer, and where the six AI SEO pillars apply.

Route a specific AI visibility, citation, rendering, identity, content, measurement, or accuracy problem to the right pillar and first verifiable action.

A practical XML sitemap tutorial for choosing URLs, generating valid files, submitting them, and detecting stale or contradictory entries.

A repeatable playbook for finding content, links, metadata, and structured data that disappear or change when a page depends on JavaScript.

Review crawler policy, server delivery, discovery, rendering, index controls, and verification across 29 technical checks.

Copyable robots.txt policies for conventional search, AI search, model training, and public paths—plus the checks that keep them from conflicting.

Maintain a verified register of answer-engine referrals, URL evidence, and documented crawler controls without classifying ordinary search traffic as AI traffic.

Describe an editorial page with its headline, images, accountable author, publisher, and meaningful publication dates.

Describe a page's position in a useful site hierarchy with ordered, canonical breadcrumb items.

Describe visible publisher-written questions and answers without implying current rich-result eligibility.

Describe one physical business or branch with its public address, contact details, coordinates, and maintained opening hours.

Review a product feed against the live page, its structured data, and the system that owns each fact before submitting it to an AI merchant program.

Describe one set of transaction terms or summarize multiple offers for the same item without merging unrelated products.

Describe an organization's public identity, with an OnlineStore variation for merchant policies, shipping, and membership programs.

Describe an identifiable author, founder, instructor, or profile subject with restrained, supportable public facts.

What OpenAI, Google, Microsoft, Perplexity, Amazon, and Anthropic currently accept as merchant product data, which fields they require, and how to keep a feed consistent with the page and its markup.

Describe one product and its current offer with identifiers, images, price, availability, condition, and seller.

Connect a parent product to selectable variants with explicit variation properties, identifiers, URLs, and offers.

Describe attributable reviews and a supported rating summary for the exact item visitors can evaluate on the page.

Use 26 Schema.org selection entries to choose markup that accurately describes visible, maintainable page content.

Capture deployed JSON-LD, test its syntax and consumer eligibility, compare it with visible facts, and record a recheckable result.

Describe an individual page, connect it to the website, and identify the principal entity explained by an About page.

Describe a website and its publisher with a stable identity, accurate home-page facts, and an optional compatibility note for SearchAction.

Create one reviewed internal record for an organization's, product's, person's, brand's, or location's approved identity facts and relationships.

Develop useful, inspectable expertise or evidence about an entity, distribute it through legitimate channels, and assess independent references without promising visibility outcomes.

Review entity identity, relationships, controlled facts, source roles, independent evidence, and material conflicts with observable results and owners.

Compare an approved entity fact with conflicting website, feed, profile, listing, knowledge-panel, and independent-source values through correction and recheck.

Track material claims, exact supporting evidence, source context, limitations, review decisions, and later changes for a substantive page.

Move a substantive page from a defined reader need through expert input, drafting, fact-checking, review, publication, and maintenance with clear human accountability.

Check whether a page has a defined reader question, appropriate evidence, clear explanation, accountable review, accurate implementation, and a maintenance trigger.

Map a reader question to a page, find the highest-impact evidence or explanation gap, revise the page, and validate a clearer answer with a complete hypothetical example.

Plan and write useful pages with clear answers, credible evidence, persuasive detail, and context that survives excerpting, with two complete fictional examples.

Calculate bounded presence, citation, recommendation, and representation-accuracy measures from a defined set of observed AI answers.

Assess whether AI answer visibility or a related intervention changed demand beyond a credible comparison, without relabeling unattributed visits as AI traffic.

Review an AI SEO measurement program by answer observation, platform and website data, tools, metric definitions, and influence-study modules.

Preserve prompts, complete answers, citations, entity mentions, observation conditions, classifications, and review decisions for repeatable AI SEO measurement.

Create a documented GA4 view of recorded AI-answer referrals, verify its rules with real traffic, and keep unknown traffic out of the AI total.

Build, test, freeze, and maintain a prompt sample that supports defensible AI-answer measurement without overstating what the observations represent.

Define and report recorded AI referrals, acquisition cohorts, and known assisted outcomes without treating them as proof of no-click influence.

Turn a reported or measured AI-answer problem into an owned case with preserved evidence, verification, classification, severity, and a defined investigation path.

Review 52 checks for intake, evidence, classification, source tracing, controlled corrections, escalation, rechecking, closure, and prevention.

Turn a questionable AI answer into a preserved, consistently classified case with consequence-based severity, ownership, and the right response path.

Take a verified case through source correction, outside reporting, comparable rechecks, and closure with explicit remaining uncertainty.