The difference between SEO and GEO

The difference between SEO and GEO is mainly a difference in emphasis. Search engine optimisation helps content become discoverable in search experiences. Generative engine optimisation focuses on how a business and its information appear in AI-generated answers, including mentions, source links, and descriptions. The two overlap substantially, particularly where generative answers draw on search indexes.

For a business owner, the useful question is how that changes the work. You need to know whether the website requires a new strategy, whether familiar SEO practices still matter, and what evidence would show that an investment is helping. A new acronym should clarify those decisions rather than make them harder to assess.

My approach is to separate the shared foundations from the additional measurement and platform questions. Clear information, accessible pages, and credible evidence remain valuable. AI-generated answers introduce another place where that information may be interpreted, so accuracy and visibility need to be assessed there too.

What SEO is responsible for

SEO addresses how search engines discover, interpret, and present content, alongside how well the resulting page meets the searcher’s need. It includes technical access, site organisation, relevant content, internal links, and useful search presentation. A strong approach connects those elements to the business’s actual audience and goals.

For example, someone searching for WordPress maintenance in Mauritius may be comparing providers. An appropriate page should explain the offer, its scope, and the route to an enquiry. An article explaining backups can support that journey, but it should not replace the information needed to evaluate a maintenance relationship.

SEO has never been limited to repeating keywords on a page. Google’s starter guide describes practices that help search engines understand content and users decide whether to visit. The purpose is a discoverable, useful destination. Google’s SEO starter guide.

What GEO adds to the discussion

GEO is a term for work concerned with visibility in generative answers. That might include whether an answer names your organisation, cites one of your pages, describes your expertise accurately, or uses your information to explain a topic. These are different observations and should not be bundled into a single claim of success.

A business can be mentioned without receiving a link. A page can be cited as evidence without the business being recommended. An answer can also describe an offer incorrectly even when it links to the right website. The commercial meaning depends on the context in which the information appears.

I find the term useful when it leads to clearer questions about those outcomes. It becomes less useful when it suggests there is one universal method for making every AI system recommend a business. Different products have different retrieval methods, controls, and reporting, so claims need to identify the platform concerned.

Google places its AI search features within SEO

Google places its generative Search features within SEO and says no special schema is required. This is Google’s guidance, not a rule for every provider. Google’s generative AI search guide.

The practical implication is to preserve a coherent website strategy. If your existing pages are vague or difficult to use, relabelling a content programme as GEO does not resolve those weaknesses. If your structure and content already serve visitors well, investigate specific gaps before commissioning a parallel set of AI-focused pages.

This is also a reason to keep editorial judgement. A substantive article can explain a complex decision through connected paragraphs and useful headings. You do not need to turn every section into a tiny answer fragment because somebody has claimed that AI only understands short blocks.

Search retrieval and model training are different processes

An AI system may use retrieved information while answering a question, rather than relying only on what was learned during training. That distinction matters for website owners because a decision about search access is not automatically the same as a decision about training. The relevant controls depend on the provider.

Perplexity distinguishes its search crawler from foundation-model training. That documented distinction is provider-specific. Perplexity’s crawler documentation.

Before changing access settings, identify the outcome you want and the specific system affected. Review the provider’s current documentation, the website’s configuration, and available logs. A broad block applied without that understanding may interfere with intended discovery while failing to achieve the restriction the business actually wanted.

Review current controls rather than old assumptions

As checked on 14 October 2026, Google’s Search generative AI control manages specified search features separately from training, with property inheritance possible. Google’s control documentation.

This is a concrete example of why AI visibility advice needs a date. An older checklist can describe a previous product state accurately and still lead to an incomplete decision today. Review material platform changes when setting the strategy, rather than relying on a screenshot or a summary with no clear source.

Control choices belong to the business. The person configuring them should explain the expected effect, the scope, and how the result will be checked. They should also distinguish eligibility from actual appearance: making content available does not establish that a system will select it for a particular answer.

Make your organisation easy to describe accurately

Start with the information that a reader needs to understand your business. Explain what you do, who it is for, where you operate, and how the work is approached. Use consistent descriptions across important pages and relevant business profiles, while allowing each page to address its own audience and purpose.

Suppose a consultancy’s homepage describes strategic advisory, its service page describes low-cost development, and its profile lists a retired offer. A human visitor has to resolve that inconsistency, and an automated summary may reproduce part of it. The sensible first step is to reconcile the public information.

Consistency does not mean repeating identical paragraphs everywhere. It means maintaining the same underlying facts. Give examples the context needed to interpret them, keep names and responsibilities clear, and make dates visible where they affect the meaning. These practices improve the quality of the source material regardless of where someone encounters it.

Build evidence into the explanation

A claim is more useful when the reader can understand how it was reached. If you report a performance improvement, describe what changed, the measurement conditions, and the limits of the comparison. If you recommend an approach, explain when it is suitable and where another choice may be better.

For a hypothetical example, reducing the transferred data on a page by half does not prove that enquiries doubled. Those are separate outcomes requiring separate evidence. Keeping that distinction visible makes the content more trustworthy and reduces the chance that a summary will turn a limited result into an exaggerated promise.

Structured data can help describe page information in a format search engines understand and support eligibility for relevant rich results. It should correspond to the actual content and follow the relevant requirements. It does not create evidence that the business has not published. Google’s introduction to structured data.

Measure AI visibility as several related outcomes

Traditional search reporting commonly examines impressions, clicks, queries, and landing pages. For generative answers, also distinguish citation frequency, brand mentions, descriptive accuracy, and visits that reach the website. None of those alone establishes commercial value, but together they can help explain how people are discovering and evaluating the business.

Google’s Generative AI performance report records impressions for supported Search features. The report may be absent when impressions are insufficient. Google’s report documentation. These observations do not establish qualified enquiries or revenue.

Microsoft’s AI Performance documentation describes citation activity across supported experiences, not ranking or placement. Microsoft’s AI Performance introduction. Different metrics should not be combined as though they measured the same outcome.

Use manual checks as research, not a market-wide score

Testing representative questions can reveal inaccurate descriptions or missing information. Record the question, platform, date, relevant settings, answer, and cited sources so that the observation can be reviewed later. Select questions based on real customer needs rather than prompts designed to produce a flattering result.

Treat the result as a sample. Answers may vary across sessions, locations, product versions, and wording. One favourable response does not establish lasting visibility, and one absence does not prove the website has a technical fault. Repeated checks become more useful when the method is consistent and the limitations are stated.

Connect the observations to business evidence. Review relevant visits and enquiries, and ask prospects how they found you where appropriate. Attribution will remain incomplete, but that is preferable to presenting a collection of screenshots as proof of revenue. Measurement should support decisions about what to improve next.

Decide where additional work earns its budget

Begin with the shared foundation: accessible content, clear page roles, accurate business information, and a usable route to action. SEO foundations before content volume explains that sequence in more detail. These improvements give the website a stronger basis for both search visitors and automated interpretation.

Then assess platform-specific needs and a proportionate measurement routine. A business receiving questions about a complex technical product may benefit from clearer documentation. A local organisation may first need consistent public details. A small consultancy may learn more from improving its service explanations than from purchasing a large prompt-tracking package.

Treat optional technical experiments with the same discipline. What is llms.txt and why does it matter? examines one such proposal and its limits. Before adopting it, identify the intended consumer and useful outcome rather than assuming that an AI-related file produces search visibility.

Ask for a clear explanation of the work

When evaluating SEO or GEO support, ask which audience, platform, and outcome the work addresses. A useful recommendation should explain the problem, the proposed change, the evidence behind it, and how progress will be assessed. The label on the proposal matters less than the quality of that reasoning.

Be wary of guaranteed recommendations or universal visibility scores with unclear coverage. Your business needs a website that explains its expertise accurately and helps relevant people take the next step. AI-generated answers add another context in which to assess that job; they do not remove the need for sound judgement.

For your own website, define the visibility problem before buying a solution. Ask whether the missing evidence concerns discovery, an inaccurate description, a weak page, or the route to an enquiry. Those problems call for different work. A clear diagnosis makes the SEO and GEO distinction useful without allowing the terminology to determine the budget.