SEO, answer engines and AI search: what is actually different

The three terms, without the mystique

SEO concerns search discovery; AEO and GEO describe work aimed at answer-based and generative search experiences. They overlap rather than replace one another. A useful buying distinction is between technical eligibility and the quality of the page’s answer, not a claim that three acronyms require three separate contracts.

• SEO — being crawlable, indexable and relevant enough that your page appears in search results.
• AEO (answer engine optimization) — being the source an answer is assembled from, rather than the tenth link under it.
• GEO (generative engine optimization) — the same idea aimed at AI assistants that generate prose instead of listing links.

Technical SEO addresses crawl and index problems. AI search optimization addresses evidence and answer structure on priority pages. The scope can overlap, so a quote should identify the actual work instead of billing the same check twice.

What the platforms actually document

Read the platform guidance before buying an AI-specific tactic. Google describes ordinary indexing and snippet eligibility for its AI search features. OpenAI documents separate controls for search crawling and model-training crawling. Neither document makes a supplier’s implementation a guarantee that a particular page will be chosen for an answer.

• Google documents no additional technical requirements. Its AI features guidance describes indexing and snippet eligibility, along with ordinary search practices.
• Google now offers a dedicated AI performance report. The generative AI performance report in Search Console shows impressions from AI Overviews and AI Mode. It reports impressions only — no AI-only clicks, leads or revenue — so do not present a rise in total Web clicks as clean AI attribution.
• Inclusion is the default. The Search generative AI control includes sites by default, and child properties inherit their parent’s setting; check yours under Settings in Search Console rather than assume exclusion.
• OpenAI separates search and training controls. Its crawler documentation explains the independent OAI-SearchBot and GPTBot settings, along with user-triggered access that may behave differently.
• Verifying a crawler means checking published IP ranges. A request claiming to be a bot in its user-agent string proves nothing; the documented method is address verification.

Eligibility is not selection. Clearer content and permitted access can be verified; future AI-answer placement cannot be promised.

What should change in your content?

Write a page that helps a buyer decide even when no search system sends a visit. State the offer, price basis, process and evidence clearly. A self-contained answer is useful to a reader and available for extraction, but that does not establish how often an assistant will choose it.

Use real specifics rather than predictions about traffic: which parts are included, which decisions remain with the owner, and what the implementation record actually proves. Keep qualifications next to the claim they constrain. A screenshot of a working interface is not a measured business outcome.

What to actually do, in order

Begin with the existing page and its purpose. Verify access, write the direct answer, support it with first-hand material and check the published output. Measure what becomes observable after publication. A new URL or special file should not be the default response to a content or indexing problem.

1. Be fetchable. Check the response, indexing controls, internal links and important rendered text. Record the actual fault rather than assume a technology is always unsuitable.
2. Answer directly. Give the conclusion and its conditions before the detail.
3. Publish specifics. Prices, process steps and evidence should be accurate and attributable, not invented to fill a template.
4. Match structured data to visible content. Describe on the page what you claim in the markup. Nothing more.
5. Decide your crawler policy deliberately. Search crawlers and training crawlers are separate choices. Write the decision down instead of inheriting a default.
6. Measure what you can, and admit what you cannot. Impressions, clicks and average position are available, and the generative AI performance report adds AI-feature impressions. Clean attribution of leads or revenue to AI answers is not available. Track inquiries — the thing that pays.

What to ignore

• "AI-specific schema." There is no such requirement in Google's documentation.
• Guaranteed AI citations or ranking positions. Not a controllable output; not sellable.
• Bulk AI-written content. The problem it solves is production volume, which was never the constraint.
• Traffic-share statistics with no source. If a figure about AI search cannot be traced to a named methodology, treat it as marketing.
• Vendor response-time and conversion statistics. Widely repeated, rarely sourced. Measure your own.

A worked example from this site

The quote-comparison guide is an own-site implementation example, not a citation case study. It presents Wilde Digital’s prices as the studio’s own rates, explains the scope behind them and gives readers questions they can use with any supplier. That is its purpose independent of search placement.

• It answers the cost question in its first section rather than after eight hundred words of preamble.
• It states specific rates with their basis and date, and says plainly that they are one studio's published rates and not market averages.
• It includes a test the reader can run today without hiring anyone, and a list of eleven questions to ask any supplier — including competitors.
• Its structured data describes the article that is actually on the page, nothing else.

Applying this approach is a scoped service: $1,500–$3,000 USD per scoped project. It covers agreed priority-page implementation, not a promise of traffic. No AI-citation or commercial result has been measured for this example.