Generative engine optimisation (GEO) is the practice of improving and measuring how a brand and its content appear in AI-generated answers. It concerns the information those answers use, the sources they cite and whether they represent the business accurately.
For businesses and agencies in Wales, a useful process starts with real buyer questions and recorded evidence. This guide explains how to establish a baseline, investigate content gaps and evaluate changes without treating an experimental score as proof of live citations.
What is generative engine optimisation?
GEO examines visibility inside generated answers, rather than only a website’s position in a list of search results. A response might mention your brand, use information about your service or link to a source page. Those are different observations and should be recorded separately.
Aggarwal and colleagues introduced a research framework for GEO and a benchmark for evaluating source visibility in their GEO: Generative Engine Optimization paper, accepted to KDD 2024. That research provides a way to investigate the problem; its experiments are not a forecast for your website.
This guide uses GEO as a practical discipline: document how answers represent the business, improve relevant content and assess what happens next. It does not assume every answer service operates in the same way.
How do generated answers use websites and sources?
A useful simplified model is: a person asks a question, a service retrieves relevant information, and a model produces an answer with supporting sources. The exact implementation depends on the service and the type of request.
Question: record what the person is trying to decide.
Sources: inspect the pages linked from the returned answer.
Answer: check what the response says about your brand and the question.
Evidence: save the answer, source URLs and collection context.
Google’s generative AI search guide describes retrieval of relevant pages from its Search index and related searches across subtopics. The simplified process above is a way to organise your observations, rather than a complete description of every service.
A brand mention is text naming the business. A citation is an attributed source or link; it may point to your website or to a third party discussing you. A linked page is worth inspecting, because the presence of a citation alone does not show that every nearby claim is supported.
How do GEO, SEO and AEO relate?
Use the terminology to organise work, rather than divide one website into competing copies:
SEO: make useful content discoverable and understandable in search.
AEO: make a page a clear and reliable answer to a user’s question.
GEO: investigate representation and source visibility within generated responses.
The same page may support all three. Google’s current generative AI search guide retains ordinary SEO foundations, so GEO does not remove the need for accessible pages and useful content. Our answer engine optimisation guide covers answer-page writing and technical accessibility.
For Google’s features, check indexed and snippet eligibility alongside Search Console’s Search generative AI inclusion setting. An accessible page alone does not establish that the site’s content is eligible for these features.
What should you measure before changing content?
Build a baseline from questions that reflect real customer decisions. Use a manageable set covering different needs, such as suitability, comparisons and limitations. Include questions where the business is relevant without naming it, alongside direct questions about the brand.
For each observation, save the prompt, date, service and available model or setting information. Preserve the full answer and linked source URLs. Record whether the brand was mentioned, which pages were cited and whether the description was correct.
Repeat some observations to see whether they vary. Keep results separated by service and question, and show the number of observations behind any summary. A small sample describes those tests; it does not represent every prospective customer.
Start with our guide to measuring and improving AI visibility for the measurement workflow. Use Answer Engine Insights to inspect saved answers, brand and competitor mentions, and cited sources.
Which content improvements should you investigate?
Choose improvements that make a relevant page more useful and accurate. Investigate gaps in the customer’s decision, unclear business facts and claims without evidence. Treat each change as a hypothesis to evaluate, rather than a guaranteed way to win a citation.
Check the evidence behind important claims
Keep a short record for each decision-relevant claim:
Claim: what does the page ask the reader to believe?
Source: which original document, product record or named source supports it?
Date: when was the information verified, and does it need another review?
Relevance: does the evidence apply to the reader’s situation and the exact claim?
A source from another market may provide background without proving a local result. An internal benchmark may help compare drafts without showing performance in a public answer service. State those boundaries where they affect the reader.
A hypothetical content-improvement hypothesis
Imagine a Newport training provider whose page names its courses but does not explain entry requirements. A useful hypothesis is that a verified requirements section will make the page more informative for people comparing courses.
The team could record answers to genuine eligibility questions, add the missing explanation and later inspect comparable answers again. A more accurate response would be a useful observation, but it would not establish that the edit alone caused the change. This is an illustrative workflow, not a reported customer result.
NeuraCite Labs helps teams benchmark pages in AI-style answers, investigate content gaps and prepare editable optimisation drafts. Its experimental scores help prioritise work; they do not prove citations in live AI search.
How can you evaluate GEO changes responsibly?
Record exactly what changed, when it changed and why. Repeat a comparable observation process using the same questions and settings where possible. Check whether the page is accessible and whether the service’s observed sources include the updated information.
Compare accuracy, mentions and citations separately. If a response changes, consider other explanations: different retrieved sources, service changes, prompt wording or variation between runs. Avoid attributing every improvement or decline to the most recent page edit.
Keep live observations separate from controlled experiments. The GEO research paper evaluates methods under specified conditions. Your own benchmark also has a defined scope; neither should be presented as a universal production result.
Review referral traffic and meaningful enquiries alongside visibility evidence. A mention, a linked source, a visit and a conversion are distinct events. AI visibility reports can help organise the evidence for your next decision.
Where should a Wales-based team start?
Select one real buyer question and the page best placed to answer it. Capture the baseline, verify the business facts, improve a specific gap and review the representation again. Keep the work narrow enough that the team can explain both the change and its limitations.
Include Welsh locations and service details when they matter to the answer. Accurate delivery areas, appointment locations and language availability provide useful context when verified. Replacing a place name across otherwise identical pages adds little decision value.
NeuraCite is based in Newport, Wales. Its platform helps businesses and agencies examine AI visibility evidence and prioritise practical content improvements. Start with your observed answers, then use Labs to investigate the page that needs attention.