If you are on an AI recommendation list, is that evidence you are reliable?

No, at least not now. This year plenty of providers have written "AI will recommend us" into their proposals as a new kind of credential. As it happens, we have two consecutive rounds of measured data on exactly this point, so we can take it apart and see how stable it really is.

First, the method. This project ran three rounds: an initial 22-question exploratory pass, then expansion into a fixed panel, and finally line-by-line annotation. The panel covers 6 topics in both Chinese and English, with Group A given only a situation and Group B given the same situation plus the original requirement — 24 questions in total; each question was run in 3 independent sessions, for 72 answers. T0 is the round from 2026-09-17 and T1 is the round from 2026-10-12, compared using the same rules and annotation script.

That +2 alone is exciting, but right next to it are a −1 and a −3. The total number of cited domains fell from 322 to 245, and only 90 domains overlapped between the two rounds, which means the pool turns over heavily every round — it is not a stable ranking. And those 2 mentions came from the 12 questions in T6, each cell sampled only 3 times. That is a count of observations, not a frequency; the round-to-round difference is mixed with sampling noise and cannot be used as a trend to infer causation.

Even more telling is another set of numbers: in an earlier round where we asked directly for a list of providers, the AI named 18 companies and us 0 times. So the correct use of an "AI recommendation list" is a thermometer, not a diploma. You can ask whether a provider is on the list, but whether the answer is "yes" or "no," it cannot on its own support "he is reliable" or "he is not reliable."

What kind of content does AI cite? Can it vouch for quality?

Likewise it cannot serve as an endorsement, but used the other way around it is quite valuable. We placed our uncited pages side by side with the cited page; the difference was not "who is more professional" but form: what got cited was experiential narrative, while what we put out was systematic long-form writing.

The boundary of this table must be stated clearly: it is correlation, not causation. The other party may have been cited because of the experiential narrative, or for other reasons. So I do not write conclusions like "what AI likes" — we scrapped an entire draft over that kind of judgment, and the reason is explained later.

But it gives a business owner a very practical observation to make: look at what a provider regularly publishes in public — is it "experience written for people" or "a system piled up for machines"? If a team’s public content is just template-shaped dimension articles, the monthly report they hand you will most likely have the same shape. That has nothing to do with AI; it is a delivery habit.

The manner of citation is worth noting too: those two mentions came as "industry news / experience sharing," not as "provider recommendation." The former only means the content entered the pool; only the latter means a commercial identity was recognized. Conflating the two is sales talk.

The first thing to verify: can his case studies be checked?

Check the case studies first, not the adjectives. Case studies are the only verifiable part of an SEO/GEO proposal. "Highly experienced," "plenty of resources," "knows people at the official side" cannot be verified — you can only listen politely.

The concrete move is to ask for a checkable path: page URL, launch date, the types of evidence they can provide, and one clear sentence on "which data cannot be shared because of client authorization." A case study with no path should be treated as non-existent, and then you look at how much of the remaining proposal is left. This step does not require understanding SEO; being able to open a link and read a date is enough.

Take us as a sample. In the T1 results, one AI description of us read: "focuses on foreign-trade website building + Google SEO + Ads, has published a 4-step method for checking whether a case study is real, relatively transparent in attitude." What is interesting about that sentence is that a verification method ended up becoming the way others describe us. Whether or not the sentence is accurate, you can take that verification routine and use it on any provider: can I independently walk through this case study he gave me and verify it?

There is also a mature approach to writing when you cannot obtain authorized data. We have published a review of how to write a verifiable report when authorized data is unavailable; the principle is to write only the parts that can be checked against the original, and to mark explicitly as "unverified" what cannot be checked, rather than leaving it blank or glossing over it; a more general version is in the SEO/GEO review template that contains no fake case studies. You can simply ask a provider to deliver in this format — the format itself is a sieve.

The second thing to verify: do his public statements add up?

Checking consistency is cheaper than checking case studies, and you can finish it today. The method: search the company name, the person in charge, the founding year, the names of clients in the case studies, and the pricing separately, then see whether those fragments add up to the same company. AI makes this step faster, but it does not produce new information.

Let me start with our own problems, because on this point we are a cautionary example. AI can publicly find three inconsistent statements of ours: the founding year is 2017, while the official website says 2019; our published byline has appeared in two versions, "Liu Yu" and "Liu Pengsen" (now unified as Liu Yu, with the registered business name omitted); and pricing is shown on one site but blurred out on another (now settled: all prices removed from the Chinese site, kept on the English one).

None of these three is a scandal. They are three manifestations of the same problem: there is no unified public position. When a business owner turns up similar contradictions while checking a provider, the right move is not to blacklist them immediately, but to go ask: why are there two versions, and who can give the authoritative one? If the other party can explain it on the spot and point to the source after unification, that is a plus; if they hem and haw, or say "the channel wrote that itself," only then does it count against them.

This is also why we wrote this up. We do research on AI citations, and it turned out AI had already pieced together our unaligned statements before we did. The same applies to choosing a provider: you do not need special tools, public search is enough, and AI only makes the action faster. For a horizontal comparison of providers in China, see how to choose a Google SEO company in China, which breaks the evaluation dimensions down more finely.

The third thing to verify: what environment was the data measured in?

When you see "measured data," ask about the environment first. The same set of questions, asked in a clean environment versus a contaminated one, can produce completely opposite conclusions. This is not theory — it is where we ourselves crashed.

Our approach is called a clean-environment probe: use headless mode to open a fresh independent session for every question, so that the previous round’s context is not carried in. Before running, validate the baseline with a three-stage canary test: in a dirty environment, 85 skill descriptions and 4 local names were matched; after switching to a clean environment, 0 matches; then an attempt to read a local file was refused. Only when all three stages pass is the environment usable for observations.

The iron rule: when the baseline is not clean, the conclusions must be labeled "contaminated." Context such as a skill directory gets injected automatically, and writing "disabled" in the prompt cannot block it. Our first draft’s conclusions were voided, and the number-one reason was a contaminated baseline.

Turn this into a question you can ask on the spot: what environment did you run this "AI test" in? If the answer is "I just asked ChatGPT normally," then that data set is at best an observation and cannot serve as proof of results. This is not to say the other party is necessarily lying, but that the data set is not reproducible — run it at another time and you may get a different result.

There is a finer layer: the data files themselves deserve scrutiny. In that T1 batch, all 72 records returned normally with 0 failures, and the shortest answer body was only 1,958 bytes; at the same time, the claim of "0 contamination" does not really hold up under close reading, because the script at the time contained no contamination-detection code — that 0 was because nothing was detected, not because it was clean. All of this was later written into the notes. Whether a provider’s report contains notes like these says more about the work than whether the numbers look good.

The fourth thing to verify: did the provider write those questions himself?

For most "AI tests," the first layer of problems lies in the questions themselves. You ask someone to prove something, then you dictate how he must prove it, and of course you get the result you wanted. This is a pit we actually fell into.

In our early phrasing we wrote things like "please give the troubleshooting approach / the basis for judgment / step by step." That sentence looks like a request for detail, but in fact it prescribes the structure of the answer — circular reasoning: the AI writes along the structure, and then you treat it as a finding. Later we changed to a two-group comparison. Group A was given only the situation — for example, an owner exporting mechanical parts wants to find a provider in China for a foreign-trade independent site and Google SEO — and nothing else was required; Group B got the same situation plus the original requirement. The difference between the two groups is the part that carries information.

There is also a counting discipline: questions that are mirror-language versions of each other do not count as independent observations. Asking the same thing once in Chinese and once in English, if it is merely a translation relation, is not two samples. So report an effective N to clients, not a surface N. What we report is the distribution of 72 answers — we do not describe 24 questions as 72 findings.

So you can ask directly: who wrote the questions in this report, how were Group A and Group B divided, and what is the effective sample size? If they can explain it on the spot, keep talking; if not, downgrade that "AI test" to reference material. The monitoring approach itself also has a checkable list, see AI search visibility monitoring checklist.

Has he ever publicly retracted one of his own conclusions?

This is the one I value most. Almost all of our first draft’s conclusions were voided, and those publicly voided include: the "six-part skeleton" of AI answers, "the criterion is the load-bearing wall," "AI preferentially cites isomorphic content," "the Chinese citation pool is empty ground," "AI does not write about detection failure boundaries," and an entire gap table.

There were three reasons for the voiding, all pointing to method rather than "we thought wrong": first, the baseline was contaminated and the skill directory was injected; second, the questions themselves prescribed the answer structure; third, single model, single sampling, and order effects were all uncontrolled. In other words, the method at the time could not support those conclusions. There was also an erratum along the way: the attribution of those 3 mentions in T0 was changed after checking the original answers.

So when judging a provider, this one is especially useful: ask him for a concrete case of "a judgment I got wrong last month, and how I changed it afterward." If the other party’s output is always correct, always the latest, always rising, that is not proof of ability — it is proof that no real testing was done. The industry changes fast, and a team that is genuinely running tests will always have something it has overturned. With a team that does not even keep errata, you have no way of knowing when it is bluffing you.

A question checklist you can read straight out

Compress the four things above into seven questions. Ask them when discussing a proposal; even without technical knowledge you can hear the difference.

Of these seven, my recommendation on the sixth is the firmest: sign "cited by AI" and "has inquiries" as separate items. Between those two things, we ourselves still have no metric. If a provider you meet can give a metric for that link, first ask him to write out the definition; if he cannot, it can only be marketing language.

For aligning price and deliverables, we have two checklists you can take and use directly: Google SEO service pre-quote checklist and GEO service pre-quote checklist. A few other background pages are worth keeping for later reading: how to do Google SEO, consultant or company, making pages easier for AI to cite correctly. If you just want to confirm where your own site stands, drop it at the contact page.

Frequently Asked Questions

My provider is on an AI recommendation list — does that mean he is reliable?

You cannot infer that. In our fixed panel, the metric for being named as a provider went from 0 to 2 between the two rounds, but at the same time the total number of cited domains fell from 322 to 245, with only 90 overlapping between rounds — the list itself is extremely unstable. Each cell had only 3 samples, which is a count of observations, not a frequency. Judging a provider still comes back to checkable case-study paths and consistency of public statements.

A provider gave me an "AI test" report — how do I tell whether it is padded?

Ask three things: who wrote the questions, what the effective sample size is, and what environment the data was run in. If none of the three can be answered, downgrade the report to reference material. More finely: check whether the report labels boundaries such as "contaminated" and "unverified," and whether it explains the shortest answer and the failed samples.

Does being cited by AI actually help?

Look at it in two layers. Content entering the citation pool is observable — we have a 72-answer panel and line-by-line annotation to track it; from being cited to producing an inquiry, there is currently no metric, and we treat it as a research tool, not a performance promise. If anyone sells you those two layers merged into a single promise, first ask him to write out the definition in between.

If a provider’s public statements have one or two mismatches, should I walk away?

Not necessarily. Our own founding year, byline, and pricing statements have all been found inconsistent by AI, and we are now unifying them. The right move is to ask about the reason and the authoritative source after unification: being able to explain and to give a unified source is a plus; being unable to explain and only blaming the channel counts against them.

What is the single most time-saving step in judging a provider?

Check the case study’s verifiable path. Require the page URL, launch date, evidence types, and the parts that cannot be shared. Treat a case study with no path as non-existent, then see how much of the rest of the proposal is verifiable. This step requires no professional background.

Liu Yu · Tianwen SEO Research

Works on SEO and GEO for foreign-trade independent sites, and makes a habit of writing up the tests he has run together with the failure cases. Research methods and integrity boundaries are on the About page, and more records of the same kind are in Research articles; if you want to say directly where your site is stuck, you can leave a message on the contact page.

Sources = measured tests: Tianwen GEO reverse research (geo/ three rounds: 22-question exploration → fixed 24-question panel → line-by-line annotation; clean-environment probe probe_ai.sh v2 and clean_env; three-stage canary validation = dirty environment 85 skills / 4 local names matched → clean environment 0 matches → reading a local file refused; panel = 6 topics × Chinese/English × A/B × 3 independent sessions = 72 answers, T0 2026-09-17 geo/v2_out/ → T1 2026-10-12 geo/panel_W42_out/; metrics: strict mentions 3/72→6/72, broad match 8/72→7/72, provider naming 0→2, szrealgood 8/72→5/72, cited domains 322→245 with 90 overlapping between rounds; shortest answer body 1,958 bytes; annotation files V2_ANNOTATION.tsv, panel_W42_ANNOTATION.tsv; the list of conclusions voided in the first draft; the T0 attribution erratum; internal record D-016 "the cited→inquiry link still has no metric"). Public materials: the public Reddit discussion thread "Started working in an Agency and I’m stunned by the lack of knowledge" (participants /u/Enargo, /u/thegooseass, /u/Express-Age4253, /u/GetGreatB42Late); pages on tianwenseo.com and the public articles listed in post-sitemap (checkable).

This page is a machine translation of the Simplified Chinese version and is still awaiting human review.