AI Visibility Checklist

Entity Clarity — Is Your Brand Correctly Defined?

• ✦ Organization schema deployed. Your site has a complete Organization JSON-LD node with name, url, description, logo, email, telephone, founder, and sameAs (LinkedIn at minimum). Why this matters →
• ✦ Person schema for named expert. The founder or primary named expert has a Person JSON-LD node with jobTitle, worksFor, and sameAs linking to their LinkedIn profile. Author schema on all article and blog content.
• ✦ Consistent brand description across all pages. The Organization description in structured data matches what your about page, homepage, and meta descriptions say. AI systems use inconsistency as a confidence-reducing signal.
• ◆ sameAs populated with verified profiles. LinkedIn, Crunchbase, company Wikipedia entry (if exists), industry directory listings. Each verified external profile strengthens entity corroboration.
• ◆ knowsAbout populated on Organization schema. An array of specific topic areas your organization is authoritative on — not generic terms, but the specific disciplines you want AI systems to associate with your brand.
• ▸ Google Business Profile verified and current. For local or hybrid businesses, a complete and verified Google Business Profile contributes to Gemini's Knowledge Graph entity recognition.

Schema & Structured Data — Is Every Page Correctly Marked Up?

• ✦ FAQPage schema on all pages with Q&A content. Every service page, comparison page, and learn page that contains questions and answers should have FAQPage JSON-LD. This is the single highest-return schema implementation for AEO.
• ✦ No JSON-LD validation errors. Run all pages through Google's Rich Results Test and Schema.org Validator. Broken schema is silently ignored — it produces no errors visible to you but also no benefit.
• ✦ Single @graph block per page. All structured data on each page should be in one consolidated @graph array. Multiple separate ld+json blocks can create entity conflicts.
• ◆ Article/BlogPosting schema on all blog and content pages. With author pointing to the Person node (not the Organization), datePublished, dateModified, wordCount, and articleSection.
• ◆ HowTo schema on process and step-by-step content. Any content that walks through a numbered process is eligible for HowTo schema and rich result extraction.
• ◆ BreadcrumbList schema on all pages. Correct breadcrumb schema helps AI systems understand your site's content hierarchy and authority structure.
• ◆ SpeakableSpecification deployed on key pages. Signals to AI assistants and voice search systems which passages are the most authoritative and extraction-ready.
• ▸ DefinedTerm schema on glossary definitions. Marks up individual definitions as canonical answers to definitional queries.

Content Architecture — Is Your Content Extraction-Ready?

• ✦ Answer-first paragraph structure. Every section opens with the direct answer or claim in the first sentence. Context and qualification follow; they do not precede the key point.
• ✦ Declarative prose throughout. Subject-verb-object sentence structure. Active voice. Concrete claims over vague descriptions. AI extraction systems favour content that makes clear, direct assertions.
• ✦ Topical completeness across key query categories. Do you have substantial content covering every significant aspect of your primary topic area? A single strong page is less citation-ready than a complete topic cluster.
• ◆ llms.txt deployed at domain root. A machine-readable manifest listing your most authoritative pages, content hierarchy, and citation preferences. See Brainpan.AI's llms.txt as a reference.
• ◆ Fact-dense introductory paragraphs. Key pages open with specific, citable facts, figures, or definitions — not with background context or marketing language.
• ◆ Internal linking connects related entity content. Learn pages link to service pages; service pages link to comparison pages; comparison pages link back to definitions. This creates the interconnected entity graph that AI systems use to assess topical authority.
• ▸ Content hierarchy visible in URL structure. /learn/ /services/ /compare/ — clean, logical URL architecture with no excessive nesting or parameter pollution.

Measurement — Can You Track Your AI Visibility?

• ✦ AI-referred traffic segmented in GA4. Sessions from perplexity.ai, chat.openai.com, chatgpt.com, gemini.google.com, and claude.ai are filterable in your analytics platform.
• ✦ Share-of-model baseline documented. You have a dated record of your brand's citation presence across a defined query set on each major AI platform, benchmarked against competitors.
• ◆ AI-session conversion rate tracked. You can compare the conversion rate of AI-referred sessions against your overall organic conversion rate in your analytics platform.
• ◆ Citation accuracy reviewed quarterly. A team member regularly queries AI systems with your brand name and category queries, reviewing and documenting what AI systems say about your brand.
• ▸ Structured reporting cadence established. Monthly share-of-model measurement is scheduled and documented in a permanent log that allows period-over-period comparison.

What to Do With Your Results

If you have fewer than 8 of the 22 items checked: the foundational layer needs significant work before content-level optimization will be effective. Prioritize entity clarity and schema completeness first.

If you have 8 to 15 items checked: you have a foundation in place and the gaps are addressable with targeted content and technical work. An AI Visibility Audit will identify the highest-priority items specific to your category and competitive position.

If you have 16 or more items checked: your infrastructure is solid and the opportunity is in measurement, content depth, and entity corroboration. Share-of-model tracking will show you exactly which query categories still have citation gaps to close.