Better questions. Clearer decisions.
AI search visibility: ten questions about evidence and measurement
Distinguish SEO, GEO, crawler access, citations and recommendations without promising guaranteed AI placement.
Use these answers to prepare a decision, then open the linked detailed guide for implementation. The questions represent common buying situations; they are not a ranked search-demand list or a claim about actual Gavek customer interviews.
What is the difference between SEO, GEO and AEO, and which services would actually help a small business become easier to discover through AI search?
SEO concerns discovery and usefulness in search; GEO and AEO are industry labels used for work around generated answers and answer systems. Ask suppliers to translate the label into actual deliverables and platform-specific evidence. Accessible pages, useful explanations and credible references can matter across channels, but the labels do not establish a universal ranking method or guaranteed recommendation.
When someone asks ChatGPT or Gemini to recommend an SEO company, how can our business improve its chances of being considered for the right customer?
Make the business easy to understand and verify through accurate services, evidence and consistent references. Check access for the relevant product, then observe whether useful pages are retrieved or cited. A citation, brand mention and provider recommendation are different outcomes. Neither access nor an agent-readiness score proves that an assistant will select your company for a particular buyer.
We rank reasonably well in Google but rarely see our company mentioned in AI answers. What should we investigate before rewriting the whole website?
Record the product, search mode, query, date and market before comparing channels. Generated answers may use different retrieval and presentation processes, and one output is not stable proof of exclusion. Examine whether the cited sources address a different decision or use stronger evidence. Repeat a consistent observation set and keep ordinary search performance separate from AI citation measurements.
People ask AI long questions with several requirements. Should we write an article for every question, or organise our content around the underlying customer needs?
Break the prompt into underlying decisions rather than copying every sentence into a new URL. A buyer asking about cost, delivery and suitability may need a clear service page plus one comparison guide. Map several phrasings to the same useful destination when intent overlaps. Publish a distinct page only when it adds a different answer, evidence or task.
An AI answer links to one of our articles but does not recommend our company. How should we improve the path from useful information to a relevant enquiry?
Let the educational page resolve its own question first. Add a contextual link to the relevant service when the reader needs implementation or a defined review. Explain the next step accurately instead of inserting a sales pitch into every paragraph. Track available referrals and subsequent enquiries, but do not equate being cited with a visit or a sale.
Could our hosting, firewall or robots settings be stopping AI search tools from reaching our pages, and how should a specialist check this properly?
Identify the specific crawler and purpose in its official documentation. Inspect robots rules, firewall responses, status codes and readable content; confirm legitimate requests using the provider’s supported verification information where available. Search access and model-training permission are separate choices. A user-agent name alone can be spoofed, and a successful access test establishes availability rather than citation eligibility or selection.
Do genuine reviews, case studies and mentions on other websites matter for AI discovery, and what should we build without buying artificial publicity?
Inventory accurate independent references, genuine work evidence and consistent company information. Improve missing or misleading material through legitimate channels rather than buying invented reviews or endorsements. Evaluate the audience and editorial context of each reference. External evidence may help people and retrieval systems assess a business, but no fixed number of mentions guarantees inclusion in generated answers.
How should we measure visibility in ChatGPT, Google AI features and other assistants without treating a few screenshots as proof of market-wide performance?
Maintain a stable observation log with prompt, platform, search mode, language, market, date and cited URLs. Classify citation, mention and recommendation separately, and repeat observations to expose variation. Combine available referral information with qualified enquiry feedback. Platform reports have different coverage; do not combine incompatible counts or describe the absence of a measurable referral as proof of no influence.
Do we need llms.txt, special AI schema, FAQ markup or a particular writing style to appear in AI search answers?
No file or layout creates a general entitlement to recommendation. Separate an integration checklist from each search product’s published eligibility guidance. Gavek’s agent-readiness result verifies the tested discovery and protocol checks; it is not a Google ranking or AI endorsement. Use structured data only when it accurately describes visible content, and assess formatting by whether it improves the reader’s task.
An agency offers a GEO package promising top placement in ChatGPT and Gemini. What deliverables, evidence and limitations should I ask it to explain?
Require named platforms, an access baseline, a content/evidence diagnosis, actual deliverables, implementation ownership and an observation method. Ask what the proposed metric measures and what it cannot establish. Reject guaranteed recommendations and unexplained proprietary scores as substitutes for evidence. A useful pilot tests a defined decision and page group before committing to broad publishing or expensive data subscriptions.
Put the decision into a brief
Copy this worksheet into your project notes. Add the evidence URL or document, a named owner and a review date for each applicable row. An empty field identifies work to investigate; it is not permission to invent a claim.
| Observation field | Record | Do not infer |
|---|---|---|
| Context | Product, search mode, language, market and date | All AI products behave identically |
| Input | Exact prompt and repeated observation | One answer is a stable ranking |
| Output | Citation URL, brand mention or recommendation | A mention is a qualified enquiry |
| Business evidence | Available referral and qualified outcome | Missing attribution means zero influence |
Relevant service and next step
Review the relevant Gavek service scope · Discuss a defined project
Describe the page or business decision, available evidence, internal implementation help and any deadline. Confirm specialist capacity, access, price and approval responsibilities before work starts.
Primary references
- Google: optimizing for generative AI search
- Google: AI features and your website
- OpenAI: searching the web with ChatGPT
- OpenAI: overview of crawlers
Reviewed 1 November 2026. References support platform-specific principles; the decision frameworks and labelled examples are Gavek editorial guidance. Platform behaviour can change. No guaranteed rankings, citations or commercial outcomes.
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