The one technical requirement that changed: your site being included in generative AI features
Google's guide adds one item to the technical requirements of classic search: the site must be included in Search generative AI features in Search Console 1 . If that setting is off, you are not shown on those surfaces no matter how good the content is. That is why the first item in our audit is not content but the setting itself — and who changed it, and when.
The other direction of that control is written in the document as well. Sites that opt out of these features do not receive traffic and impressions from them; however, this preference is not used as a ranking signal in search results outside generative AI features 6 . In other words, the answer to "will I be penalised if I opt out?" is not a guess, it is the publisher's own announcement.
The second change is scale. According to Google's announcement, AI Overviews passed 2.5 billion monthly active users and AI Mode passed 1 billion monthly users 6 . On a surface that size, how your brand is summarised is no longer only a traffic question; being described incompletely inside an answer nobody clicks is also a measurable loss, and it comes back to your sales team as an objection.
The combined consequence of those two changes is this: AEO is not a campaign, it is a maintenance discipline. The state of the setting, indexing health and content freshness are items to be re-checked every month. On the agency side, that translates into a fixed checklist and the same three verifications repeated in every report.
What we do not sell: lists of secret settings marketed as "AEO technique". We keep the work bounded by what publishers have written, what we can measure, and the real questions in your market. In the proposal, that corresponds to countable items: eligibility audit, question map, answer passages, evidence layer and a monthly measurement report.
Official measurement of AI visibility: Google's two announcements describe the rollout differently
There are two official reports that show a publisher visibility inside AI answers: one at Google, one at Bing. On the Google side, two separate Google documents describe who the report is open to today in two different ways. The Bing report, meanwhile, measures a surface that holds under one percent share in Turkey. This is the most inconvenient truth about the service we sell, and we say it before the proposal stage. The table below places the two side by side against the same criteria.
What Google's report gives, and what it does not
The Search Console generative AI performance report was announced on 3 June 2026 4 . The metrics listed in the announcement are impressions, pages, countries, devices and dates 4 ; a click metric is not on that list. The announcement describes not one screen but separate reports for Search and Discover 4 . The scope of the Search report is narrow: AI Overviews and AI Mode only 5 , with a separate report shown for Discover 5 . Google is opening the report to a subset of sites rather than to all sites 4 .
The real uncertainty is in the rollout. On its corporate blog, Google writes that it is first opening the new controls and insights to a subset of site owners in the United Kingdom 6 . The Search Console help document, on the other hand, explains the reasons for not seeing the report in terms of the property rather than the country: the rollout is spread over time, and the site may not have received enough impressions 5 . Neither document counts Turkey as in scope; one limits scope by country, the other does not relate the rollout to country at all.
Whether the report is open in your account can only be seen by looking at Search Console; that is the first item in our audit. Until the screen appears, we do not fill the gap with guesswork: classic Search Console data, movement in branded queries and page-level impression series are what we have. These are not a direct measure of the AI surface but an indirect indicator, and they are labelled as such in the report. What the AI Overviews surface actually is, is explained in our AI Overview explainer ; the question here is not the definition but the measurement.
Bing's citation report and what it is worth in Turkey
Microsoft opened AI Performance inside Bing Webmaster Tools on 10 February 2026; the report shows citations of your content in Microsoft Copilot, Bing's AI summaries and selected partner integrations 7 . The caveat applies not to the whole data set but to a single metric: Microsoft writes that grounding queries data is a sample of total citation activity 7 . On the total citation metric there is a separate note: the number does not indicate the placement or presentation of your content within the answer 7 .
On the Turkish side the picture is this: in July 2026 Google held 80.5%, Yandex 17.5% and Bing 0.97% share 12 . So Microsoft's brand-new citation report describes a very small surface in this market. The report is not worthless — it is honest and useful — but on its own it cannot build an AEO measurement system for Turkey.
The Bing report is still a gain: it is the first screen giving a publisher data at citation level, and we enable it routinely for brands working in global markets with English content. For a brand aimed at the Turkish market, we keep its weight low.
On the chat interface side, the official document we have is not a measurement document either: OpenAI's developer documentation gives publishers the ability to manage crawler permissions 8 , not a visibility report. The sum of the table is this: there are two official publisher-facing screens; who one of them is open to today is described differently by two Google documents, and the other measures a small surface in our market.
llms.txt and "AEO requirements": what we leave out of our package
We do not sell some of the items we frequently see in competing proposals. The most visible example is llms.txt. There is a lot of content presenting this file as a mandatory "AEO requirement"; put two primary sources side by side and the picture becomes clear.
Who was llms.txt written for?
The specification itself says this is a proposal, not a standard: a proposal to standardise on an /llms.txt file to provide information that helps agents use a website. Its author is Jeremy Howard; first published 3 September 2024, with the second version last changed on 10 August 2026 9 .
The adopting side is not search engines either: AI labs publish this file for their own developer documentation 9 . On the Google Search side the ruling is explicit; you do not need to create new machine-readable files, AI text files or markup to appear in Google, because Google Search does not use them 1 .
For an item to enter our package it must meet one of two conditions: either it is explicitly requested in a publisher document, or we can see its effect in our own measurement. Work that meets neither does not enter the proposal; if the client wants it, it is done as a separate item with the expectation written down.
Access is a separate matter and should not be confused with llms.txt. Which permissions you give AI crawlers is determined by robots rules and server-side settings; we check that item by item in the technical section of the audit. Which crawler to allow is a commercial decision, not a technical default; we write the decision and its rationale into the report.
The two sources do not contradict each other; their audiences differ. llms.txt is a proposal that may be meaningful on the agent side, not a requirement for Google visibility. The commercial conclusion follows: we do not bill clients for llms.txt. If they want it we add it, and we write into the report why we added it and what it does not solve.
Click data conflicts: that is why we give no guarantees
On the effect of AI summaries on clicks, three serious measurements point in three different directions. The category has not settled either. The chart below shows how the trigger rate changed direction twice within a single year.
The category has not settled: the AI Overviews trigger rate changed direction twice in one year
According to Semrush's study covering more than 10 million keywords, AI Overviews appeared for 6.49% of keywords in January 2025; the rate rose to 24.61% in July 2025 11 and fell to 15.69% in November 2025 11 . This curve can still change, so building a one-year commitment on it would not be honest.
The same study also finds a shift on the intent side: in January, 91.3% of queries triggering an AI Overview were informational, while by October that share had fallen to 57.1%; summaries on commercial and transactional queries increased 11 . For a service page the consequence is direct: AI summaries are now entering purchase questions.
The conflict is exactly here. Pew Research's measurement is based on the March 2025 browsing records of 900 US adults 10 : users who saw an AI summary clicked a traditional result in 8% of visits, while those who did not saw one clicked in 15% 10 . Semrush, comparing the same search terms before and after summaries, found that people clicked slightly more 11 . Google, for its part, says clicks coming from result pages with AI Overviews are of higher quality 3 .
The planning effect of this volatility is concrete. We do not tie traffic forecasts to the behaviour of a single surface, and we build the content plan so that it works for both classic search and answer surfaces. That way the same page keeps doing its job whether the rate goes up or down.
All three measurements are serious and all three say different things. That is why we give no ranking or citation guarantee in AEO. What we commit to is process, evidence and reporting: we write down what we did, on what date, and what we were able to measure.
In return we expect one thing from you: not to tie the decision to a single metric. Visibility on AI surfaces gains meaning when it is read together with branded searches, direct traffic and the "I saw you there" sentences in sales calls. A reporting setup that demands a single metric will produce the wrong decision in this category.
AEO pricing: why we have no fixed package
We do not publish a fixed AEO package, because two engagements sold under the same price tag turn out to be completely different pieces of work. One brand's technical debt may be clean while another's pages are not being indexed; selling both the same package means over-serving one and under-serving the other. So instead of a package we price by scope: the exact figure is set in a written quote by the four variables below.
The four variables that determine the proposal
- Existing content inventory and technical debt: how many pages are eligible, how many need rewriting.
- Number of target languages and markets: a single-language site and a three-language structure are not the same job.
- Measurement access: is the generative AI report visible in the account, which tools do we have access to.
- Production volume: how many answer passages, evidence layers and updates are planned per month.
The pricing conversation starts with those four headings as well. We first run a short audit and determine which layers will be active; if the scope changes the price changes too, and that change arrives in writing as a separate item. The only way to avoid surprise invoices is to write the scope down item by item from the start.
The proposal is written according to the answers to those four headings, and the scope sits in the contract item by item. If you would like to weigh the subject up on your own first, our AEO ebook covers the "is SEO dead" debate and which old tactics no longer work; it does not replace the measurement framework on this page, and it arrives by email once you fill in the form. After that, in an audit call, we look together at the current state of your pages; you leave that call with at least an eligibility table and a first question list.
Who needs AEO today, and who does not
An AEO budget makes sense if your pages can be indexed and shown with a snippet, and if you have content that answers the purchase question clearly; if those two are missing, they come first. The decision table below is a shortened version of the questions we ask in the first meeting.
The real message of the table is not ranking but sequence. For the brands in the first two rows, an AEO budget is early today; the same money spent on clearing technical debt or producing content that answers questions pays off both in classic search and on answer surfaces. The brands in the third and fourth rows are the group that sees the effect of the work fastest.
In Turkey the category does not yet produce commercial search demand
In our own Search Console records, the 15 queries in the AEO and GEO cluster received a total of 182 impressions and zero clicks over ninety days 14 . In other words, in Turkey this category is not a matter of capturing existing demand but of creating it. A brand that sets out knowing this works with the right expectation; a brand expecting customers from an "aeo agency" search will be disappointed.
This table is also our filter. We do not start working with a brand whose expectation is "ChatGPT ranking"; we first propose a short audit showing which surface can be measured today. The audit produces two things: a page-level eligibility table and a prioritised question list. The expectation framework becomes clear through those two documents.
The profile that benefits most is also clear: B2B brands with long sales cycles, research-driven decisions and sound technical infrastructure. For those brands, being cited on a single answer surface moves branded search and proposal requests together.
In two of the five situations in the table our answer is no. It may look odd for an agency to write down the cases in which it does not sell its own service; for us this is the section that determines the credibility of the proposal. You will see which box we placed you in, with the reasoning, in the first audit report; if the box changes, we tell you along with the reason.
Ossvy
