AI discoverability is the work of making useful content accessible, understandable and worth referencing when people ask questions through AI tools. For content teams, the practical question is how to improve the chance of being found in ChatGPT search, Perplexity, Gemini and Google AI Overviews while still serving the person who needs the answer.
I approach this as a content and measurement problem. Who is asking the question? What would help them decide? Can they check the evidence? Can the relevant service access the page? What would count as a useful outcome for the publisher?
The method below is my proposed working approach, supported by the platforms' own documentation. It combines access checks, focused editorial improvements and repeatable observation. It does not promise citations, rankings or commercial results, and the worked example is explicitly hypothetical.
Key takeaways
- Check platform-specific search access and training preferences separately. Access does not guarantee citation.
- Answer a real reader question with clear scope, supportable evidence and useful next steps.
- Treat proposed layouts and hypothetical examples as editorial recommendations, not proven ranking factors.
- Measure citations, recorded referral visits and meaningful outcomes separately.
- Start with a bounded test, record its limits and expand only when the evidence justifies the effort.
Understand how AI discoverability differs from traditional organic search
AI answers create another place where a reader may encounter your information. A linked citation can bring someone to your page; an unlinked brand mention may provide no route back to the source. Those are different outcomes and should be recorded separately.
SEO remains relevant. Google says its generative Search features draw on its core Search ranking and quality systems, and that SEO best practices still apply (Google Search Central). AEO, or answer engine optimisation, and GEO, or generative engine optimisation, are labels used for this work. They should not become a reason to abandon useful content or ordinary search maintenance.
For planning purposes, I separate five questions:
- Access: can the relevant service reach the content?
- Retrieval: is the page selected as useful material for the particular question?
- Attribution: does the answer identify or link to your page, and does the source support the statement?
- Visits: do people follow a link to your website?
- Outcomes: do those visits help readers complete a meaningful task, such as evaluating a service or making a relevant enquiry?
This is an observation framework, not a description of every platform's internal process. A result at one stage does not establish success at another. A crawler request proves neither a citation nor a sale. Keep the evidence attached to the outcome you actually observed.
Check which AI search crawlers can access your pages
Begin with the public pages you want people to find. Review their access settings, robots.txt directives and relevant infrastructure rules. The aim is to understand and deliberately manage access, rather than treating every bot as the same service.
OpenAI documents separate controls for OAI-SearchBot, which supports ChatGPT search, and GPTBot, which concerns potential model training (OpenAI). These are independent preferences. Check the intended search and training settings separately; do not assume that one directive expresses both decisions.
Perplexity describes PerplexityBot as a crawler that surfaces and links websites in its search results, rather than collecting content for foundation-model training (Perplexity). Its documentation also advises checking web application firewall rules and verifying requests against published IP ranges (Perplexity). A robots.txt check alone may therefore leave an infrastructure problem undiscovered.
Google has a Search generative AI control in Search Console for including or excluding content and links from supported features such as AI Overviews and AI Mode (Google Search Console Help). Check that setting separately from Google-Extended, which governs specified Gemini training and grounding uses, including Gemini Apps, and does not control inclusion or ranking in Google Search (Google Search Central).
For each service, record the intended setting, what you checked, any access problem and who owns the correction. Follow the provider's verification guidance when inspecting crawler requests. Keep security controls deliberate and specific rather than broadly allowing traffic with an AI-related user-agent name.
These checks address eligibility and access. They do not establish that a particular answer will retrieve, cite or recommend your page.
Compare how major platforms retrieve and cite your content
Use platform documentation to establish what is supported, then inspect the answer you actually receive. Avoid importing a rule from one product into every other AI experience.
| Platform | What the documentation establishes | What to inspect in your own test |
|---|---|---|
| ChatGPT search | OAI-SearchBot is the search-related crawler; its control is independent of GPTBot (OpenAI). | Does the answer link to the exact page, and does that page support the cited statement? |
| Perplexity | PerplexityBot is intended to surface and link websites in search results (Perplexity). | Which source URL appears, and is its information represented accurately? |
| Gemini Apps | Responses may include sources and related links, but not every response has them (Google Gemini Apps Help). | Are links present? Record missing links rather than inventing a source trail. |
| Google AI Overviews and AI Mode | Supported generative Search features have a participation control in Search Console (Google Search Console Help). | Is a link to your page visible for the question, market and observation date? |
Gemini Apps and Google AI Overviews are separate experiences. Test and label them separately. Likewise, distinguish a linked citation from an unlinked mention, and a related page from a passage that directly supports the answer. Your record should describe what is visible without claiming knowledge of an undocumented retrieval mechanism.
Make your answers useful, specific and easy to verify
Start with the decision your reader needs to make. Give the answer enough context to be useful, then show its evidence, conditions and limits. I recommend this because it makes content easier for people to assess; it is not a proven formula for obtaining AI citations.
For an existing page, ask what a reader would still need after reading it. A comparison may need eligibility conditions, a worked example or a clear explanation of trade-offs. A process guide may need ownership, inputs and a usable next step. Adding another definition is less useful when the missing answer is how to make a choice.
Google's guidance does not require special structured data, llms.txt files or fixed content chunk sizes for its generative Search features (Google Search Central). Use headings and tables where they help the reader. Choose the page's length and format around the task rather than an imagined universal AI preference.
Hypothetical worked example: imagine a team explaining its content approval workflow. The following wording and process are invented to illustrate editorial clarity; they are not a client policy or a measured search experiment.
Before: We take content quality seriously. Our rigorous process ensures every article meets high standards and delivers value through collaboration and expert oversight.
The passage sounds positive but leaves the reader unable to identify the work, the owner or the point at which publication can proceed.
After: Before a technical article is published, an editor checks the source links, copy and unresolved questions. A subject specialist reviews claims outside the editorial team's expertise. The article returns to its author when a material claim lacks support.
The handover includes the intended audience, the question being answered, the working draft, source links and a named reviewer. Publication waits until the recorded issues have been resolved.
The revised version supplies a scope, responsibilities, inputs and a decision condition. It gives a reader something concrete to understand and use. It does not demonstrate that a search system will select it.
Apply the same discipline to evidence. Attribute findings to the organisation or person who produced them, explain the scope of the work and show what a citation supports. If you have genuinely collected original observations, explain the method and limitations. If you have not, do not manufacture experience to make the page appear authoritative.
My guide to fact-check AI-generated content explains the editorial review step. Clear wording makes a claim easier to inspect; the source still has to justify it.
Set up a reliable tracking loop to measure your citations and referrals
Define your observations before changing the page. Visibility, attribution, website visits and business outcomes answer different questions, so keep a separate record for each.
For Google Search, Search Console's Generative AI performance report reports link impressions in AI Overviews and AI Mode (Google Search Console Help). Google notes that the report may be absent when there are insufficient impressions; exclusion from generative features is another possible explanation (Google Search Console Help). It is a Google Search report, not a dashboard for ChatGPT, Perplexity and Gemini Apps.
Alongside native reporting, I recommend a small, repeatable panel of realistic reader questions. Include the tasks your content is meant to support, such as understanding a concept, comparing approaches or resolving a problem. Avoid choosing only prompts that already mention your brand.
Record the prompt, platform and mode, observation date, market, exact linked URL and whether the linked passage supports the answer. Note an absent citation or an inaccurate summary as carefully as a successful reference. Repeat observations rather than treating a single response as a stable ranking.
| Observation | Useful question | Limit |
|---|---|---|
| A citation in the prompt panel | Did this answer reference the intended page accurately? | A selected sample does not establish market-wide visibility. |
| A recorded referral visit | What landing page and source does analytics actually show? | Missing attribution is missing information; do not relabel all direct traffic as AI traffic. |
| A relevant enquiry or completed task | Did the visit contribute to the outcome we chose? | A useful outcome alone does not prove which content change caused it. |
Review source and landing-page information actually recorded in your analytics, and state gaps in attribution. Keep bot requests separate from human visits. The purpose is to learn whether the work helps readers and supports the website's goals, not to produce an impressive visibility score.
Prioritise a small test before changing your whole site
Choose a limited set of pages where the reader's question matters and the evidence can be improved. A small test gives the team a manageable way to inspect quality, record effort and decide what to do next.
- Establish the baseline. Record the current page, access settings, relevant reporting and prompt-panel observations before editing. Identify who owns each technical or editorial issue.
- Choose a specific hypothesis. For example, a comparison page may fail to explain who each option suits. Improve that answer and its evidence rather than making every possible formatting change at once.
- Keep the comparison useful. Where practical, observe similar pages that have not received the same edits. Record other changes and give the revised material an appropriate observation period. A comparison can provide context without making the exercise a controlled causal experiment.
- Decide the next action. Review answer quality, citation accuracy, recorded visits, useful outcomes and the effort required. Expand a useful approach, revise an unclear hypothesis or stop work that does not justify its continuing cost.
Set the review date and decision criteria for your own publication cycle. There is no universal number of pages, referral threshold or fixed observation window that proves an AI discoverability strategy works.
The same discipline underpins my AI opportunity assessment approach: identify the problem, examine the evidence and choose a bounded next step before scaling implementation.
A credible content strategy leaves a record of what changed, why it changed and what was observed. That makes AI discoverability a practical editorial capability that can be reviewed and improved over time.