Is AI SEO different from regular SEO?

They overlap heavily. The work that ranks a page in Google (entity-clear copy, structured markup, genuinely useful content) is most of what gets that page retrieved and cited by ChatGPT, Perplexity, and Claude. AI SEO adds a layer on top: llms.txt, AI-crawler access decisions, answer-first formatting, and citation tracking. One investment covers both channels.

Does llms.txt actually matter?

It is cheap insurance, not magic. llms.txt gives language models a clean, curated summary of what your company is and which pages matter. Adoption by AI systems is uneven, but the file costs an hour, cannot hurt, and forces you to write the one-paragraph entity description most sites are missing anyway.

How do I check if ChatGPT cites my company?

Ask the questions your buyers ask, in fresh sessions, and log whether your company is named. Run the same query set against ChatGPT, Perplexity, and Claude on a regular schedule and track mentions over time. Position in AI answers is the new rank tracking, and almost nobody measures it yet.

Checklist

The AI SEO checklist: 21 checks for ranking in Google and getting cited by LLMs

By Connor Cady · July 31, 2026 · 7 min read

This is the checklist we run on our own properties, and it is why our own B2B niche site holds #1 on its money keywords with zero ad spend. It covers both halves of the modern problem: ranking in Google, and being the company ChatGPT names when your buyer asks it who to hire. Work through it top to bottom. Most sites fail more than half of these.

Part 1: Entity clarity (5 checks)

Search engines and LLMs both have to answer one question before they can recommend you: what is this entity? Most B2B sites never say it plainly.

1. The first sentence of your homepage says what the company is in plain words. Not a slogan. "X is a [category] for [buyer]." If a stranger can't repeat your category after one line, neither can a language model.

2. Every major page opens by answering "what is this page about" in its first two sentences. LLMs retrieve passages, not pages. The passage has to stand alone.

3. Organization schema is on the homepage: name, URL, description, founder. This is the machine-readable version of check 1.

4. Your company name, description, and category are identical everywhere they appear: site, LinkedIn, directories, podcast bios. Inconsistent entities get low-confidence treatment, and low confidence means you don't get named.

5. An llms.txt file exists at your root, with a one-paragraph entity description and a curated list of your pages with one-line summaries. An hour of work, and it forces the discipline of checks 1 through 4.

Part 2: AI crawlability (4 checks)

6. Your content is in the HTML. If the page needs JavaScript to show its main copy, most AI crawlers see an empty div. Server-rendered or static beats client-rendered for anything you want cited.

7. robots.txt reflects a deliberate decision about AI crawlers (GPTBot, ClaudeBot, PerplexityBot, Google-Extended). Blocking them by default is a choice some sites inherit from a plugin without ever making it. If you want citations, they need access.

8. The sitemap is current and every page you care about is in it, with real lastmod dates.

9. One H1 per page, descriptive title under 60 characters, meta description that answers the query. Boring, still load-bearing.

Part 3: Content that gets cited (5 checks)

10. Each page targets one question or one keyword cluster, not five. Pages that try to answer everything get retrieved for nothing.

11. Answers come first, explanation second. Put the direct answer in the first two sentences under each H2, then elaborate. LLMs lift the tight passage; readers skim the same way.

12. Real specifics: numbers, dates, named tools, actual prices where you can, first-hand observations. Generic content is statistically invisible because a model has already seen ten thousand versions of it.

13. Topical clusters, interlinked. Three to five posts around each theme, each linking to the others and to the money page. Depth on a narrow topic beats breadth every time; it is the entire method in our AI SEO playbook.

14. Content carries a visible date and gets updated. Stale content decays in both channels.

Part 4: Schema markup (4 checks)

15. Article schema on posts, with author and publisher.

16. FAQPage schema wherever you genuinely answer questions. FAQ blocks are disproportionately retrieved by LLMs because they are pre-formatted as question and answer.

17. Service schema on service pages: what it is, who provides it, where.

18. Validate it. One malformed JSON-LD block silently disables the rest. Google's Rich Results Test takes two minutes.

Part 5: Measurement (3 checks)

19. Search Console is connected and someone reads the query report, because impressions on new queries are the earliest signal that targeting is working, months before clicks arrive.

20. AI citations are tracked: run your buyers' actual questions against ChatGPT, Perplexity, and Claude on a schedule and log whether you get named. This is the least crowded scoreboard in marketing right now.

21. You measure in business terms somewhere: queries with buying intent, form fills, booked conversations. Traffic that never becomes pipeline is a hobby.

Where sites fail this checklist

In the audits we have run, the same three failures repeat. The entity is never stated plainly (checks 1 and 4), the content answers nothing specific (checks 10 and 11), and nobody measures the AI channel at all (check 20). Fixing just those five checks puts a site ahead of most of its market, because most of its market has not noticed the second search channel exists yet.

If you would rather have operators run the whole list, that is literally the job: AI SEO is one of four systems in our marketing engine for the MSP ecosystem, proven on our own site first.

Talk to the team → The full AI SEO playbook →