Rank in Google, and be the source the AI quotes back
Most "AI SEO" is just SEO with a new sticker. Typical SEO standards are also LLM citation standards. So here are the 10 pillars to get you ranking in classic search and named inside the answers ChatGPT, Perplexity, Claude, Gemini and Google's AI Overviews hand people instead of a list of links.
Search stopped sending you the click
Things have been getting pretty uncomfortable. Around 60% of Google searches now end with no click, because the AI Overview answers the question on the spot and the person never reaches your page. If your whole plan is ranking number one for "what is X", you're ranking number one for a question the machine already answered for free.
That's not a crisis, it's a shift in where the value sits. The traffic that does arrive from AI search converts at roughly three times the rate of traditional organic traffic, because those are users at the decision stage, not the looking-it-up stage. So you're getting fewer visitors, but you're getting better visitors, if you can reach them.
This is why the job has changed. It's no longer just "get the click". It's "be the source the answer is built from". When the model writes its reply, it pulls factual chunks from a live index. Either it pulls from your content, or it assembles an answer from whatever else it can find. You want to be the source it leans on.
The brands that win the next five years won't be the ones that game the model. They'll be the ones the model can quote without flinching.
Ten pillars, on one screen.
Weighted by impact. The bar is the weight. The dots are the tier mix: win, should do, could do. Tap any pillar to jump in.
Own a canonical source of ground truth
Most sites treat their own pages as a brochure: persuade the human, skip the boring specifics. An AI answer engine wants the opposite. It runs on retrieval, pulling factual chunks from a live index, and if you haven't written down the dull truths (founded when, made of what, costs how much, who actually runs it) the model fills the gap from third-party scraps or invents something plausible (spoiler alert: you don't want this). The trap is thinking this is a content job. It's a records job. You're not writing copy, you're keeping the official register of what is true about you, and the win is that the machine then quotes you instead of guessing.
Why it matters nowDeployed AI uses retrieval-augmented generation: it scans a live index for factual chunks and stitches them into an answer. If you own and publish those facts, the model leans on your accurate content rather than fabricating, which is why this is the single biggest lever and the foundation the other nine pillars sit on.
Write down what's true about you, or the model will write it for you.
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Winsite
Gather every verifiable fact about your entity (history, products, services, specs, people, values) and publish it as deliberate, fact-dense, structured pages on your own site. Treat the site as the official reference, not the brochure.
If an AI scraped only your own site, could it answer the specific factual questions a customer would ask about you without guessing?
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Should doprocess
Give your factual content a named owner and a review cadence, and update the substance when the facts change, not just the date stamp.
Does one named person own keeping your facts current on a set schedule, and do you change the words when the facts change rather than only touching the date?
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Should dosite
Consolidate duplicate and conflicting information into one authoritative page per fact-set, and point your internal links at it so nothing competes or contradicts.
Is there exactly one canonical page for each fact about you, with no older or contradictory versions still live elsewhere on the site?
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Should dosite
Put clear, current commercial facts where customers and answer engines can use them to judge whether you fit.
Could someone compare your price, scope, availability and terms without opening a PDF or contacting sales?
Structure content for AI chunking and retrieval
Most people read "structure for AI" as a markup task and bolt on FAQ schema. That misses the mechanism. Retrieval doesn't grab your page, it grabs a passage, drops it next to passages from three other sites and asks a model to assemble an answer. So the real test is brutal and simple: take any paragraph off the page, show it to someone who has never seen the rest, and see if it still stands up. If it leans on the sentence before it, or on a heading three scrolls up, it's not a chunk, it's a fragment. Good headings aren't keywords with a question mark stapled on. They're the actual query, so the retriever has a clean handle to grab and a passage sitting right underneath that answers it.
Why it matters nowDeployed AI uses retrieval-augmented generation: it pulls factual chunks from a live index and stitches them into an answer rather than reading your page top to bottom. Structured, scannable pages are the path of least resistance to being the chunk it lifts, so the formatting work that used to be polish is now the difference between being cited and being skipped.
A paragraph that needs the one above it isn't a chunk. It's a fragment the model skips.
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Winpage
Rewrite your H2 and H3 headings as the actual questions your audience types or speaks, so a query maps straight onto a section.
Do your section headings read like real questions a person would ask, rather than two-word topic labels?
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Winpage
Tighten each section into one self-contained idea and put a direct answer in the first 40 to 60 words, so a passage still makes sense when it is lifted off the page.
Could you copy any single paragraph out of a page and have it still make complete sense on its own?
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Winsite
Give permitted AI crawlers a clear route to the complete HTML on every page you want them to quote.
Can a permitted AI crawler read the useful page content from the initial HTML, or does it appear only after JavaScript runs?
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Should dopage
Give your key pages FAQ sections and scannable formats (lists, steps, comparison tables) with FAQ or Article schema so machines parse them cleanly.
Do your important pages use lists, steps, tables or FAQ blocks backed by structured markup, not just walls of prose?
Add the GEO trust elements: statistics, quotes and citations
The interesting bit isn't that AI likes statistics. It's why. A model can't tell whether a sentence is true, so it leans on the shape of corroboration instead: a number, a name with credentials behind it or a link it can chase. Those are proxies for "someone checked this", and the model treats them as such. So the job here is not to sound authoritative, it's to be checkable. Most "AI SEO" advice stops at "add stats", which is how you end up with pages stuffed with round numbers and no source attached. A round number with no source behind it isn't evidence, it's decoration. The trust comes from the attribution, not the digit.
Why it matters nowResearch from Princeton and Georgia Tech into Generative Engine Optimisation found that statistics, attributed expert quotes and citations to authoritative sources can lift visibility in AI answers by 30 to 40% (Whitehat). That is rare in this work: a measured effect amid plenty of confident guesses. Aleyda Solis's April 2026 AI Traffic vs AI Citations study of 40 US sites using Semrush data found that brand-entry pages drew 57.7% of AI traffic but only 3.0% of AI citations. The pages AI systems cite are not the pages people click, so earning a citation is a separate job.
A statistic with no source is a confident guess. The model can tell.
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Winpage
Work attributed, specific statistics through the body at roughly one every 150 to 200 words, each with its source named.
Does a typical section of your page carry a hard, sourced figure rather than a vague claim like 'many' or 'leading'?
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Winpage
Quote named experts and show their credentials, so the authority is visible and not just asserted.
When you quote someone, does the page say who they are and why their view counts?
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Should dopage
Link out to authoritative, verifiable sources inline, right where each claim is made, so a model can corroborate you.
Can a reader (or a robot agent) click through from your claims to the sources that back them?
Run regular AI footprint audits
Most people audit their site and never audit their reflection. But there's a second version of you that customers now meet first: whatever ChatGPT, Gemini and Perplexity say when someone types your name. You don't control it, you rarely see it, and it's often wrong in ways that would mortify you if a salesperson said them out loud. The audit isn't a vanity check on whether you get a mention. It's reading back the model's draft of your reputation so you know which facts to go and publish. Every confident falsehood the engine returns is a content brief in disguise.
Why it matters nowA bad answer here isn't a missed click, it's a wrong answer reaching a buyer before you do. With more AI-mediated discovery, the synthetic version of you is doing the first sales conversation, and you can't fix what you've never looked at.
There's a second version of you out there answering customer questions, and you've never read its script.
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Winprocess
Ask ChatGPT, Gemini and Perplexity the questions a customer would, using your brand, product and key people's names, and write down exactly what each one says back.
Have you, in the last month, actually read what the major answer engines return when asked about your brand and people?
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Should doprocess
Turn every wrong, missing or unflattering answer the engines gave into a specific page or FAQ that publishes the correct fact.
Does each inaccuracy or gap you found in the audit have a matching content task to correct it?
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Should doprocess
Put the audit on a fixed schedule, monthly say, and keep the answers so you can see how they shift over time.
Is your AI footprint audit a tracked, repeating habit rather than a one-off you did once and forgot?
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Should doprocess
Track Aleyda Solis's five presence KPIs separately across a useful prompt set, so you can distinguish visibility, endorsement, citation, preference and accuracy.
Could you tell whether your weak point is being found, recommended, linked, preferred or described correctly?
AI drafts, humans verify (the cyborg technique)
People hear 'cyborg technique' and expect a clever hack. It's the opposite: the plain admission that an AI draft is a first draft, and Google's content effort signal is built to spot the people who stopped there. What's important to understand is that content effort is site-level, not page-level. So one expert running ten brilliant articles past a hundred templated ones is still publishing on a domain that reads as low-effort in aggregate. The expert is real but the signal still drops. That's the part people miss: they think they can carve out a few good pages and let the rest sit around hoovering up long tail keywords. The site as a whole is the unit being judged whilst the median page is what sets the tone.
Why it matters nowGoogle's leaked documentation confirms content effort, an LLM-based, site-level quality signal that demotes low-effort, scaled or unoriginal AI text. It means the cost of mass-producing AI text just flipped from a shortcut into a liability that can drag a whole domain down, not just the page it sits on.
An AI generated draft is a first draft. The content effort signal is built to find the people who stopped there.
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Winprocess
Make expert human review a hard gate before anything publishes. A named subject-matter expert fact-checks every AI-assisted draft, adds first-hand experience and signs it off before it goes live. No exceptions.
Does every AI-assisted draft pass through a named expert who fact-checks it and signs off before it goes live?
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Should dopage
Put visible effort into the page that a competitor could not cheaply copy: original research, your own data, custom visuals, genuine analysis. Use AI to sharpen the human work, not to stand in for it.
Does this page contain original data, custom visuals or first-hand analysis a rival could not just rewrite from yours?
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Should dosite
Audit the whole site for thin and low-effort pages, then prune, merge or upgrade them. Keep quality consistent across every page, because content effort judges the domain, not the post.
Are there thin or low-effort pages on your site that you have not yet pruned, merged or upgraded?
Pivot to decision-intent keywords
For fifteen years the SEO playbook was a funnel: catch people early with "what is X", earn the click and walk them down to a sale. AI Overviews have eaten the top of that funnel and most "what is" answers now happen on the results page without generating a visit. The mistake is when teams double down on the very informational content the answer engines now give away, then panic when sessions slide. This work has now moved down the funnel, to the queries where someone is choosing between options or about to buy. This type of content is messier to write, harder to rank and far more valuable, because the answer can't be settled in two sentences and the user is ready to act.
Why it matters nowAround 60% of Google searches now end with no click, because AI Overviews answer definitions on the spot, so informational traffic is plateauing. The flip side is the prize: visitors who arrive from AI search convert at roughly three times the rate of traditional organic, and those are decision-stage queries.
The traffic you're losing was never going to buy anything.
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Winstrategy
Reweight your content and keyword plan toward bottom-funnel, decision-stage queries instead of 'what is X' definitions the answer engines now give away for free.
Is most of your keyword effort aimed at queries where someone is choosing or buying, rather than just learning?
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Should dostrategy
Reset your KPIs for a zero-click world: measure presence in the answer, conversions and the quality of assisted AI traffic, not raw informational sessions.
Have you redefined success as being in the answer and converting, rather than counting informational clicks?
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Could dostrategy
Segment your analytics so conversions from AI answer engines show up as their own line, and you can see the higher-converting AI visitors on their own.
Can you see conversions from AI-referred traffic as a separate segment in your analytics?
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Could dopage
Help the right buyers choose you by stating exactly who will benefit, who won't and what they gain or give up.
Could a buyer tell from the page why your offer suits them, or why they should choose something else?
Embed disambiguation factoids in schema
Entity confusion is the failure that hides in plain sight, because the model never tells you it's confused. It just answers, fluently, about a company with your name in another town, or stitches your founder's bio onto someone else's creating some sort of hybrid amalgam of Steve-Bill Job-Gates. Schema markup is awesome but it often gets treated as the fix when it's really only the machine-readable half, and it confirms facts the page already states plainly. If your About page never says what you are not, no amount of JSON-LD will stop an entity-resolution system filling that gap with a guess. The aim here is to remove the guess: pin down exact identity, founders and scope in plain language first, then wrap it so a machine can't misread it.
Why it matters nowDeployed AI builds answers with retrieval-augmented generation: it pulls factual chunks from a live index rather than reasoning from scratch. So when two brands share a name, the model resolves which one you are from whatever it can retrieve and if your own facts aren't there, structured and unambiguous, it leans on a similarly named rival or invents the difference.
The model never says it's confused. It just confidently answers about someone else with enough of a similar sounding name.
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Winpage
Write plain, legible disambiguation statements on your About and entity pages: exact identity, who founded it, what you actually do and what you are not.
Does your About page state, in plain words, what your business is not and how it differs from similarly named ones?
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Should dosite
Wrap those identity facts in Organization and Person JSON-LD, with sameAs links pointing to your verified external profiles.
Do your home and About pages carry Organization (and Person) JSON-LD with a sameAs array linking your verified profiles?
Earn co-occurrence and brand mentions
Links are an old proxy for a newer requirement: does the wider web agree on what you are? A generative engine doesn't tot up your backlinks, it reads the company you keep. If the same three trusted sites name you next to the topic you want to own, the model treats that as settled. The mistake is chasing the link and ignoring the sentence around it. An unlinked mention next to your core topic on a site the model already trusts can do more for you than a followed link buried in a footer that says nothing about who you are. This is already well known to any serious SEO link builders, but the trouble is the ratio of clueless SEO link builders outnumber the competent ones by something like 100:1
Why it matters nowGenerative engines weigh entity-level consensus, not raw link volume, so a consistent pattern of mentions across diverse trusted sources is what marks you as an authority in a given context. Old link-building optimised for the count whilst modern citation building optimises for the pattern, which is harder to fake and slower to build and that's exactly why it works.
The model doesn't count your links. It reads the company you keep.
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Winentity
Map where your competitors get named on trusted sites in your niche, then pitch to be named on those same pages. Run it as a standing programme, not a one-off push.
Do you have a deliberate, ongoing programme to earn brand mentions on authoritative third-party sites, informed by where your competitors already appear?
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Should doentity
Get your brand and its core topics mentioned together across diverse trusted sources off your own site, so third parties corroborate what you do rather than you only asserting it.
Is your brand consistently named alongside your core topics across several trusted external sources, not just on your own pages?
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Should doentity
Keep the profiles that speak for your business aligned with the facts on your own site.
If an answer engine checked your main profiles today, would it find one account of the business or several competing versions?
Define author entities to prove E-E-A-T
You need to hit your Experience, Expertise, Authority and Trust (EEAT) goals but most sites treat the byline as a courtesy: a name in grey text, maybe a headshot, no link. Google treats it as an entity-resolution problem. It's trying to work out whether the person named on the page is a real human with a track record, or a label slapped on AI output. The trick isn't writing "by Jane Smith" at the top, it's making Jane resolvable: one consistent identity, a real bio with credentials and profiles that corroborate each other off-site. An author who only exists on your own domain is, to a machine, indistinguishable from a pen name that could just as easily have been assigned to a robot. One thing AI genuinely cannot fake is having actually done the work, so the bio that says "I fitted 200 of these" beats the one that lists three qualifications and nothing lived.
Why it matters nowGoogle's ranking infrastructure tracks author reputation and tries to match the entity named on a page to its actual author, so a credentialed, resolvable author is what proves the real-world experience an AI cannot manufacture. Get it wrong and your expert content reads, to a machine, like anonymous scaled text.
An author who exists only on your own site is, to a machine, just a name you typed.
What good looks likeweight 8
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Winpage
Put a named byline on every substantive page and link it to a real author bio that states credentials.
Does every meaningful page name its author and link out to a proper bio page, not a dead name in grey text?
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Should dosite
Make each author a resolvable entity: one consistent identity, connected professional profiles and Person schema with sameAs links.
Could Google connect your author to the same person on LinkedIn or elsewhere, with Person schema and sameAs to back it up?
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Could dopage
Show first-hand experience in both the bio and the writing: evidence the author has actually done what they're talking about, not just read about it.
Do your author bios and articles show the person has actually done the work, rather than giving a generic overview?
Go where search now happens (search everywhere)
The phrase "search everywhere" sounds like a brief to be on every platform, and that's how most people botch it. The actual point is narrower and odder: a few platforms have signed data deals that pipe their content straight into the models, so a Reddit thread or a YouTube transcript can end up inside an answer your own site never gets near. That's not distribution, it's sourcing. Posting a link back to your site on those platforms misses it entirely. The expertise has to live there natively, in the format the platform rewards, because the model is reading the post, not following the link. So "be everywhere" misreads the brief. The point isn't presence, it's being readable where the models actually source.
Why it matters nowSearchers have spread their queries well beyond Google, and the major LLMs now have direct data deals with platforms like Reddit, so native posts on Reddit, YouTube and Quora land in the third-party databases RAG engines crawl to build answers. Skip them and you've left a side door into the answer wide open for someone else.
The model reads the Reddit post. It doesn't follow the link back to your site.
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Winentity
Repurpose your core expertise into platform-native content on YouTube, Reddit and Quora, so it stands on its own there rather than pointing back to your site.
Does your expertise live natively on YouTube, Reddit or Quora as real content people engage with, rather than as links back to your own pages?
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Should doentity
Build and keep up a real presence on the platforms LLMs are known to source from, so retrieval engines can find and surface you.
Are you actually present on the data sources that feed AI answers, like Reddit and YouTube, in a way a retrieval engine could pick up?
Start where the lever is heaviest
Don't try tackling all 10 pillars at once. There's an order, and the standard makes it plain: get the wins first, address the should-dos next and then the could-dos when those are in place.
The heaviest single lever is owning your facts and structuring them in a way the machines can lift them out. Deployed AI uses retrieval: it scans a live index for factual chunks and builds the answer from those. So publish a complete, verifiable set of facts about who you are and what you do on your own site, then format it into self-contained chunks with question-shaped headings and a direct answer in the first 40 to 60 words. That's two pillars ticked off right there, and it's the foundation the other eight build on.
With those two in place, the remaining pillars have something solid to attach to. Add the GEO trust elements that research from Princeton and Georgia Tech found lift AI visibility by 30 to 40%: attributed statistics, named expert quotes, citations to authoritative sources. Pin down your identity in schema and put real authors behind the work, because Google's leaked documentation describes a site-level content effort signal that demotes low-effort scaled text, and visible human expertise is how you clear it. Then check what the engines actually say about you, and fix the gaps before a customer finds them.
Straight answers.
Is SEO dead now that AI answers everything?
No, but the job has split in two. Around 60% of Google searches now end with no click because AI Overviews answer definitions on the spot, so the old informational traffic is drying up. The work now is to be the source the AI quotes, and to win the decision-stage queries that still pay: visitors arriving from AI search convert at roughly three times the rate of traditional organic.
What is GEO and is it actually different from SEO?
GEO (generative engine optimisation) is getting your content retrieved and cited inside AI answers, where classic SEO is about ranking blue links. Most so-called AI SEO is the same work relabelled, but GEO does have its own moves: Princeton and Georgia Tech research found that adding statistics, attributed expert quotes and citations to authoritative sources lifts visibility in AI answers by 30 to 40%. So treat them as one job with two scorecards, not two separate disciplines.
How do I actually get cited by ChatGPT or Perplexity?
Own the facts and structure them so a machine can lift them out. Deployed AI uses retrieval-augmented generation: it pulls factual chunks from a live index and stitches them into an answer, so it cites the page that already holds the fact in a clean, self-contained passage. Publish a complete, verifiable set of facts about your entity on your own site, then break each section into standalone chunks with a direct answer in the first 40 to 60 words.
Do AI Overviews kill my traffic?
They kill the easy informational clicks, not the valuable ones. Around 60% of searches now end without a click because the answer sits on the results page, so 'what is X' traffic is plateauing. The fix is to stop chasing definitions AI gives away free and weight your content toward bottom-funnel, decision-making queries: those visitors arriving from AI search convert at roughly three times traditional organic.
Can I just use AI to write all my content?
Not if you want it to rank. Google's leaked documentation describes content effort, an LLM-based, site-level quality signal that demotes low-effort, scaled or unoriginal AI text, and being site-level means a pile of thin AI pages can drag the whole domain down. So use AI the sensible way: let it draft for speed, then put a named expert in front of it to fact-check, add first-hand experience and original analysis, and sign off before it goes live.
Why does AI keep getting facts about my business wrong?
Usually because you have not published those facts cleanly anywhere it can retrieve them, so it guesses or stitches together fragments. The fix is two-part: first run a footprint audit (ask ChatGPT, Gemini and Perplexity about your brand, products and key people and record what comes back), then publish the correct facts as canonical pages and wrap your identity in Schema.org structured data (Organization, Person, sameAs) so entity-resolution systems lean on your verified facts instead of inferring. Do the audit on a cadence, because the answers drift.
Schema markup, does it still matter for AI?
Yes, more than ever, because it is how you stop the model guessing who you are. LLMs make inferential leaps and confuse similarly named brands, and clear factual statements about your identity wrapped in structured data force entity-resolution systems to rely on your verified facts. Put crisp disambiguation factoids on your About and bio pages (exact identity, founders, what you do and what you are not) and mark them up with Organization and Person JSON-LD plus sameAs links to your real profiles.
Do backlinks still matter, or is it all about brand mentions now?
Both matter, but generative engines weigh entity-level consensus, not just link volume. A consistent pattern of brand mentions across diverse trusted sources, linked or not, signals that you are an authority on a topic, so a mention with no link still counts. Look at where competitors get named, pitch to be named on those same authoritative pages, and get your brand and your core topics talked about together off your own site.
Who this is for, and who it isn't
This is for people who want a defensible read on where they stand and a ranked list of what to fix next. It's weighted, tiered and checkable, so a high score means you've built the signals current search and AI systems actually reward. It won't promise you a ranking or a citation, because nobody honest can. What it does is find the weak spots, sort them by impact and settle the argument about what to do first.
It isn't for anyone chasing a magic trick or one more hack to game the algorithm. There isn't one. It's also not a finished verdict on its own. The on-page parts a tool can read; the process and off-site reality, like whether you genuinely run AI footprint audits or earn third-party mentions, need a real look at how you work. If that's the kind of honesty you want, good.
Score your own site against the Standard.
The guide is the map. The scorecard turns it on your site: tick the 32 criteria, get a weighted score and a ranked plan. Or skip the self-diagnosis and I'll do it with you.
What this standard is built on.
The standard is built on 17 years of practice in search. These are the independent sources that sharpened it, worth your time in their own right.
- Shaun Anderson's excellent Hobo SEO Quadrilogy · the Strategic AiSEO, Technical SEO and Strategic SEO guides
- How SEO Really Works in 2026 · Whitehat
- SEO Has Changed: Here's What Works Now · Semrush · YouTube
- 7 SEO Strategies That Actually Work in 2026 · Marketing School · YouTube
- The SEO Playbook That Actually Works in 2026 · Leveling Up with Eric Siu · YouTube
- How to estimate the traffic impact of SEO fixes · Search Engine Land
- SEO in 2026: What's Changing and What Actually Works Now? · r/digital_marketing
- The AI Search Optimization Checklist · Aleyda Solis