AI SEO has two common meanings: using AI tools to carry out SEO work, and improving a business's visibility inside AI-generated search answers. Before I scope an engagement, I establish which one the buyer means. A team can generate content with AI and still do nothing to investigate whether customers find the business in ChatGPT or Google's AI features.
At Searchmaxxed, the commercial goal is to help a buyer find you, understand why you're relevant and take the next step. That means working on Google visibility and the answers buyers encounter in tools such as ChatGPT and Perplexity.
For a business owner comparing proposals in September 2026, the useful question is what changes on the website and how the team measures the result. I'd start with access, the buying decision each page answers and the sources supporting the claims.
How do SEO, GEO, AEO and LLM SEO differ?
These terms overlap, but they describe different emphases. Ask the provider to define its scope before comparing prices.
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| Term | Typical emphasis | What I'd ask to see |
|---|---|---|
| SEO | Visibility in search results | Indexed pages, relevant rankings and business outcomes |
| AI-assisted SEO | AI tools used in research or implementation | Finished work, human checks and measured results |
| GEO | Visibility in generative answers | Saved answers and the sources they cite |
| AEO | Answer-oriented visibility, including direct answers | The answer surface being targeted |
| LLM SEO | Brand and page visibility in language-model experiences | Named models, retrieval settings and repeated observations |
A proposal can cover several of these. The label on the cover won't tell you whether the team will repair your site, publish pages, investigate third-party sources or only send a dashboard.
Does Google require special optimisation for AI Overviews?
Google says the same foundational SEO practices apply to AI Overviews and AI Mode. Its published eligibility requirement is an indexed page that can appear in Search with a snippet. Google states there are no additional technical requirements. [1]
Google also describes query fan-out: an AI feature may issue related searches across subtopics while developing an answer. That helps explain why checking one exact query gives an incomplete view of the pages a generated answer could use.
I would keep important information in visible text, make useful pages reachable through internal links and check the technical path Google takes to fetch them. Where structured data applies, it must agree with the visible content. Google does not name a special AI schema that guarantees inclusion.
What would I check before rewriting any copy?
I'd pick one commercially important page and trace whether a buyer and a crawler can both reach its useful content.
Start with the response code, indexing directives and canonical URL. Check whether the main text appears in the rendered page and whether links from relevant pages lead to it. Search Console's URL Inspection gives first-party evidence about Google's treatment of the URL.
Next, check the business facts. Is the service named? Can the reader see where it's offered, who qualifies, what happens next and what supports the claim? A page that leaves those questions unanswered needs more than a new opening paragraph.
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| Check | Failure to look for | Useful next action |
|---|---|---|
| Access | A blocked or failing request | Fix the access fault |
| Indexing | A noindex directive or wrong canonical | Resolve the intended indexable URL |
| Page content | Important details hidden or missing | Put the answer in visible text |
| Internal links | A useful page has no relevant route into it | Link from the service or topic it supports |
| Business facts | Different prices, locations or service descriptions | Correct the source that owns the fact |
| Evidence | A claim without a named source | Add the underlying record or narrow the claim |
My rule is to name the failure before commissioning a rewrite. It keeps the work tied to something we can inspect again.
What should an AI-search page actually contain?
An AI-search page should help a person complete a decision. I favour a direct answer, enough context to judge it and a clear route to the next useful page.
Take a service page for a business selling into several markets. A strong version explains who the service fits, the work included, exclusions, relevant locations and how pricing is determined. It links to evidence and to the enquiry route. A generic introduction about how technology is changing the industry adds little to that decision.
For a comparison, explain the selection criteria before the options. For an original study, publish the method and the observations. For a technical guide, show the setting or command and what success looks like.
The Searchmaxxed AI SEO service brings those tasks into an implementation scope. Our Agentic Website work covers the website that supports them.
Where do external sources fit?
External sources can be useful discovery and verification surfaces. A buyer may compare your service on a directory, read a review or encounter a video before arriving on your site. AI search can retrieve external pages too.
In our exploratory Google Australia capture on 21 September 2026, 40 selected SEO-related queries produced 16 captures with returned AI Overview source lists. Across those lists we retained 122 query-and-source URL occurrences, deduplicating a repeated URL within each query. YouTube accounted for 14 occurrences.
Those observations make video worth investigating for that query set. They don't establish a universal platform preference or prove that making a video causes citations. The capture and method let you inspect which questions and URLs produced the count.
For your own market, open the sources that appear for relevant buyer questions. Decide whether you have a legitimate contribution to that page or platform. A detailed review, an interview and a technical demonstration require different work.
How should AI SEO be measured?
I separate being mentioned from being cited and from being recommended.
| Observation | What to record |
|---|---|
| Brand mention | The brand name appears in the answer |
| Citation | A displayed source link points to your page or a page discussing you |
| Recommendation | The answer selects you for the user's stated need |
| Referral | A visit is observable in analytics or logs |
| Commercial outcome | An enquiry or sale with its attribution limits |
An answer can cite a company while criticising it. Being named first can be an introduction rather than an endorsement. Read the surrounding text before reporting a win.
Keep the questions and observation conditions stable, record the model or product surface and repeat the collection. A first check helps choose the next investigation. It cannot establish a durable visibility rate.
Can ChatGPT do SEO work?
ChatGPT can help analyse supplied data, draft content and propose code changes. Its output still needs source checks and a route into the actual website.
ChatGPT Search can also search the web and return citations. OpenAI describes those features explicitly. [2] A response from a model API without search is a different observation from a response in the ChatGPT product with search enabled.
That distinction matters both when producing content and when measuring it. Record which workflow you used so someone else can repeat the check.
What would I put in the first month's plan?
I'd choose one valuable service or product area. Fix its access and indexing faults, map the buyer questions to existing or missing pages, and publish the work that closes the largest gap.
Before release, record the current pages and search observations. After release, verify the pages are live and readable, then watch indexing, search activity and enquiries. Keep the AI-answer observations alongside those measures so the team can see what each channel actually did.
If you're choosing who should run that work, my Australian AI SEO agency shortlist explains what I'd compare. If you want Searchmaxxed to own the implementation, tell us which market you want to grow.