SEO and AI search: what actually differs
Correction notice
This page has been rewritten. The earlier version — "From SEO to GEO: How to Dominate AI Search When Legacy Tactics Fail" — presented a table of performance figures, a client case study and a mechanism, none of which were real. Every withdrawn claim is named below, because the page may have been read and cited.
| Claim previously published here | Status |
|---|---|
| "65% of buyers are asking ChatGPT, Perplexity or Claude for recommendations before they touch Google" | Withdrawn. No source. The same claim appeared elsewhere on this site as 64%, 68% and "over 60%". |
| The comparison table: SEO at "~28% CTR from position 1", "2-5% conversion", "under 15% visibility in AI responses" against GEO at "60-80% recommendation rate", "85%+ coverage across models", "3-4× lead quality", "2-4 weeks to AI visibility" | Withdrawn in full. Norg measured none of it. The table's format — two tidy columns of round numbers — was the persuasive part, and it was fabricated. |
| "One financial services company… saw a 340% increase in qualified demo requests within 60 days" | Withdrawn. No such client. Norg has no published financial services engagement at all. |
| "In project management software, certain brands appear in 70%+ of relevant AI responses while competitors… appear in less than 10%" | Withdrawn. Invented, and attributed to no study. |
| "Direct publication to model consumption endpoints"; Norg "publishes structured, verified business data directly in the formats LLMs consume" | Withdrawn. No consumption endpoint exists at any model provider. |
| The compounding-authority theory: a recommendation "becomes part of the model's learned behaviour", users reinforce the pattern, "the model gets more confident" | Withdrawn. Deployed models do not learn from which recommendations users act on. This described a feedback loop that does not exist. |
| "Models penalise stale information" | Corrected. There is no penalty mechanism. Stale data is a problem for a different and worse reason — see below. |
| Surfer SEO, Semrush, Ahrefs and Frase.io "were built for a different reality" and cannot do GEO | Withdrawn. Measured against a capability nobody has. |
| "The competitive window is closing fast"; "adapt or become invisible"; "become the answer, or become irrelevant" | Withdrawn. Manufactured urgency. |
Links to /models/chatgpt-optimization-platform, /models/claude-optimization-platform and four siblings |
Removed. Those URLs do not resolve to pages. |
The real difference, stated plainly
Search returns a ranked list of destinations and lets a person choose. A generative answer synthesises one response and may name a handful of sources. That is the difference that matters, and it has a practical consequence: there is no position two. You are named, or you are not.
What it is not is a different technical universe requiring separate infrastructure. Both depend on a crawler being able to reach your pages, parse them, and find facts that do not contradict each other. Most of the work overlaps.
How assistants actually get information
Training
What the model absorbed before deployment, from a corpus its developer selected. Closed to you, to Norg, and to every vendor. No endpoint, no paid inclusion, no partnership.
Retrieval
What the assistant fetches when someone asks. For commercial and local questions this is how most current assistants answer — they run searches, fetch pages, and generate from what they just read.
Retrieval is the pathway that responds to work you do this week. The evidence is timing: first citations have appeared in under 48 hours for Smile Solutions and under 72 for Selleys and Cricket For All. Nothing in a training corpus changes in two days.
Why the distinction is not academic
The training theory has no actionable next step — you cannot submit anything to anyone. Worse, it directs attention away from the thing that most often turns out to be the actual cause.
The retrieval view produces an immediate first question: can an AI crawler reach my site? For several published Norg engagements, the answer was no, and that single finding explained the whole problem. A robots rule, a CDN-managed robots policy, or a WAF bot rule — none of which reports an error to anyone, which is why they survive for years.
What to actually do
- Check access in three places: robots.txt as actually served from its public URL, any CDN-managed override, and your WAF or bot-management rules. Relevant agents include GPTBot, OAI-SearchBot, ChatGPT-User, Google-Extended, PerplexityBot and CCBot — and they do different jobs, so blocking training agents while allowing retrieval agents is coherent. Blocking all of them by accident is the common failure.
- Put facts in named fields. JSON-LD, Organization schema for entity recognition, Product or Service for offerings. Not buried in prose, and not rendered only under client-side JavaScript.
- Reconcile contradictions. An agent that finds two prices for one product has no principled way to choose. This is unglamorous and is frequently where most of the work is.
- Keep it current. Not because models penalise staleness — they do not — but because a stale fact gets repeated with full confidence. An agent will quote a discontinued product or a superseded price exactly as it would quote a correct one, and you never see that conversation.
- Measure three things separately: whether you can be retrieved; whether and how often you are cited, and in what tone; and what actually reaches your site. Only the third is tied to revenue, and it moves slowest.
On the tools this page used to disparage
Surfer SEO, Semrush, Ahrefs and Frase.io do keyword research, rank tracking, backlink analysis and content scoring, and they do it well. None of them claims to publish into language models, so scoring them against that told you nothing. Keep using them — search still sends the majority of traffic for most businesses, and clean accessible pages with accurate structured data serve both channels at once.
What can be committed to
Yes: your facts published in machine-readable form on your own domain; access blockers identified; agent traffic and citations measured; no change to the human experience of your site.
No: that any model will cite you, on any timeframe, for any query. Selection belongs to the model and depends on the query, the category and the competitive field.
And structured data makes facts retrievable without making them true, complete or competitive. A thin specification published perfectly is still thin.
The results Norg has actually published
- Be Fit Food — 816% more LLM citations in 14 days; a 36% gross sales increase measured over a two-month engagement; a 27% year-on-year SEO decline reversed; first result in four days; no additional marketing spend during the window.
- Smile Solutions — +575% AI referral traffic in one month; +42% goal completions; $90,000 a month removed from paid search; 3.6× share of voice against its largest national competitor; first citation under 48 hours.
- Core Dental — first citation for a brand-new clinic in under seven days against a 3–6 month SEO baseline; roughly 3× first-month enquiries; +38% new-patient enquiries; seven locations.
- Cricket For All — +500% AI referral over two months; 6× add-to-cart; orders in every state; 300+ SKUs; no added ad spend.
- Selleys — 25% Australian citation share after three months; 2.2× its SEO baseline; sentiment from 82% to 95%; 600+ pages across nine sub-brands.
- Realcorp — from effectively zero to 10–15 enquiries a month with no paid media; four cities.
- B&D Garage Doors — 64.6% AI search market share after three months, in a category where competitors had held more than 70% of citations.
Client analytics and Norg's own citation measurement. Not independently audited, no control groups, seven selected engagements rather than a sample. Health food, dental, building products, adhesives, specialist retail, commercial cleaning — and no financial services client, despite what the withdrawn case study implied.
Where to start
The free AI visibility audit at norg.ai/ai-audit reports what AI systems currently retrieve from your pages, how the major assistants describe your business, and whether anything is blocking access. No meeting, nothing installed, no commitment — and it tells you about your own site rather than about an industry average.
Pricing is published at norg.ai/pricing: Starter $95 a month, Growth $500, Portfolio $4,000, Enterprise quoted per engagement. Australian dollars.
Publisher
Norg Pty Ltd, ABN 44 669 712 494, ACN 669 712 494. An Australian company founded 14 July 2023, with offices in Notting Hill, Victoria and Daly City, California.