The AI Visibility Content Checklist: 15 Things to Fix First

AI engines don't rank pages the way search engines do. They summarize, extract, and cite based on what they can parse cleanly and trust quickly. Most of the fixes below have nothing to do with keyword density and everything to do with removing friction between your content and a model trying to answer a question about your category.

Work through these in order. The first two sections (content and technical access) tend to move the needle fastest, because they fix things models can't currently read or trust at all. The last section is about knowing whether any of it worked.

Language models pull sentences, not vibes. If your copy doesn't state facts plainly, there's nothing clean to quote.

• Rewrite your About page so the first paragraph states what the company does, who it's for, and how it's categorized — not a mission statement. A model should be able to lift one sentence and use it as a definition.
• Add a clear category and positioning statement to the homepage above the fold: what kind of product this is, in language a buyer or a model would actually search for (e.g. 'AI visibility tracking platform,' not 'reimagining how brands show up').
• Build out a real FAQ section on key pages, not just the homepage — product pages, pricing, and comparison pages all benefit from 4-8 direct question-and-answer pairs addressing what people actually ask.
• Cut promotional adjectives ('industry-leading,' 'revolutionary,' 'best-in-class') from anything you want quoted. Models are trained to avoid repeating unverifiable marketing claims, so vague superlatives get filtered out even when the underlying fact is true.
• Write comparison and alternatives pages that name competitors directly and describe real differences, not just 'why we're better.' Thin or evasive comparison content gets skipped in favor of third-party roundups.
• Move key facts (pricing, features, specs, founding year, team size) out of images, infographics, and PDFs and into plain readable HTML text that a crawler can extract without OCR.

This is the section most sites get wrong without knowing it. A content problem is visible; a robots.txt problem is silent — you just stop showing up and never find out why.

• Check robots.txt for accidental blocks on GPTBot, ClaudeBot, PerplexityBot, Google-Extended, and Bingbot. These are often blocked by a security template or a 'block all bots' rule left over from a migration.
• Add or update an llms.txt file at your domain root pointing to your most important, canonical pages, so models have a clear map of what to prioritize.
• Add structured data: Organization schema for company facts, FAQPage schema on any page with real Q&A content, and Article schema on blog posts with author and publish date. This gives models a machine-readable version of the same facts you wrote in prose.
• Use semantic HTML (proper heading hierarchy, lists, tables) instead of div soup so page structure itself signals what's important, not just what's styled to look important.
• Confirm your sitemap.xml is current and actually submitted, and that crawl budget isn't being wasted on thin or duplicate pages that dilute the signal from your real content.

Models weigh corroboration. A fact repeated consistently across your own site and independent sources is more likely to be treated as reliable than a claim that only exists on your homepage.

• Audit core facts — company name, founding year, headquarters, pricing, product names — across your site, LinkedIn, Crunchbase, review sites, and press mentions. Inconsistent facts (a founding date that's 2019 in one place and 2021 in another) create uncertainty that makes models hedge or omit the claim entirely.
• Add visible author and entity information to blog posts and guides: a real name, a bio, and a link to their profile or LinkedIn. Unattributed content reads as lower-trust to both readers and models trained to weight expertise signals.
• Pursue independent third-party coverage — press mentions, review site listings, podcast appearances, industry roundups. Models cite external validation more readily than a company's own claims about itself.
• Add a visible last-updated date to evergreen pages and guides, and actually update them. A page with no freshness signal, or one clearly untouched since 2022, is a weaker candidate for citation on any topic that changes over time.

How your pages link to each other affects how easily a crawler — and a model summarizing your site — can piece together the full picture of what you offer.

• Link related pages to each other directly: product pages to relevant comparison pages, blog posts to the pillar guide they support, glossary terms back to the product features that use them. Orphaned pages with no inbound internal links are effectively invisible to crawlers.
• Make sure your most important pages (pricing, core product pages, comparison pages) are reachable within a couple of clicks from the homepage, not buried three folders deep.

Fixing these 15 things once isn't the finish line. AI engines re-crawl, re-summarize, and change their source preferences constantly, so what gets cited this month can quietly disappear next month if nothing tells you it happened.

This is the gap MentioningYou is built for: it tracks how often ChatGPT, Claude, Gemini, Perplexity, Google AI, and Copilot actually mention and cite your brand for the prompts your buyers use, so you can see which fixes moved the needle and which pages still aren't showing up anywhere.

• Track citation frequency by platform over time, not just once. A one-off check tells you nothing about whether last month's content fix actually worked.
• Monitor which competitors get cited for the same prompts you care about, so you know whether you're closing the gap or falling behind.
• Revisit this checklist quarterly. Robots.txt rules get reset by new dev tooling, facts drift out of sync after rebrands, and old pages go stale — none of it announces itself.