Short answer. Measuring AI search visibility in China means tracking the domestic assistants — Doubao, DeepSeek, Qwen, Yuanbao and Baidu's generative results — with prompt sets written in Simplified Chinese by native speakers, not translated from English. Most Western visibility platforms do not reach these engines, so a China programme usually needs separate tooling, in-market reviewers and a plan for where data may physically sit.
Key takeaways
- The assistants Chinese buyers use are not the assistants most AI-visibility platforms measure, so a global dashboard showing full coverage is usually showing coverage of engines that are not used in China.
- Prompt sets translated from English measure the wrong question, because buyers phrase problems differently in Simplified Chinese and engines retrieve against the phrasing actually used.
- Baidu, Doubao, DeepSeek, Qwen and Yuanbao each surface sources differently, so a single blended visibility number across them hides which surface moved.
- Cross-border data rules shape where measurement work can physically happen, which is a procurement question before it is a technical one.
- Mainland results differ from Hong Kong and Taiwan results for the same brand, so a programme covering Greater China needs prompts and reviewers per market rather than one Chinese-language set.
Which AI assistants do buyers in China actually use?
Chinese buyers use domestic assistants — principally Doubao, DeepSeek, Qwen and Yuanbao — alongside Baidu's generative search results, rather than ChatGPT, Gemini or Perplexity.
This is the first thing that breaks an imported programme. A visibility platform built for Western engines can report a brand's position with real precision and still be describing a market the brand is not selling into. The engines differ in origin: some sit inside a search product, some inside a super-app, some are standalone assistants with their own retrieval stack, and each has its own idea of what counts as an authoritative source.
The practical consequence is that coverage has to be enumerated per engine rather than assumed. Ask any prospective provider which of the domestic engines it queries, how often, and whether it stores the raw output. A provider that answers with a count of "AI engines tracked" without naming them is usually counting the global ones. Our guide to AEO in the markets Google does not own covers the same problem across Korea and Japan.
Why do translated prompt sets produce the wrong numbers?
A prompt set translated from English measures the question an English-speaking buyer would ask, not the question a Chinese-speaking buyer actually types.
Buyers in different markets frame the same commercial need differently. An English prompt might ask which vendor is "best for enterprise data annotation"; the Chinese equivalent a real buyer types is often narrower, naming an industry, a delivery model or a compliance constraint instead. Translating the English phrasing preserves the words and loses the intent, and because retrieval runs against the phrasing used, the measurement lands on queries nobody fires.
Native authorship fixes both ends of this. The prompt set is written by someone who works in the market, reviewed against how buyers there actually describe the problem, and then kept stable so period-on-period comparison means something. The same logic governs the content that answers those prompts, which is why English-language bias in AI search is an execution problem rather than a translation budget.
What a market-native prompt set contains
- Commercial prompts naming the category the way local buyers name it, not the way the head office names it
- Comparison prompts that pit the brand against the competitors who actually appear in that market
- Problem-first prompts describing the pain rather than the product category
- A fixed subset repeated unchanged every period, so movement is measurable rather than an artefact of rewording
How should you separate memory from retrieval in a China programme?
Report them separately: retrieval is what the engine fetches and cites at answer time, and memory is what the model already believes about the brand from training.
The distinction matters more in China than elsewhere because the two move on different clocks and through different mechanisms. Retrieval responds to what is published, structured and reachable now, so it can shift inside a measurement period. Memory follows model training cycles, which for domestic models are not published on a schedule anyone outside can plan around.
A blended visibility score hides which of the two produced a change. A brand can win every retrieval surface and still be described incorrectly from memory when an assistant answers without fetching anything, and the fix for each is different — the first is a content and structured-data problem, the second an entity and third-party-authority problem that takes longer. Reporting them apart is the difference between a number that explains itself and one that does not. Our explainer on measuring AI visibility by share of answer sets out the metric itself.
What does cross-border data mean for where the work happens?
Cross-border data rules determine where measurement runs, where raw outputs are stored, and which team can handle them, so the operating model has to be settled before tooling is chosen.
This is the part most often discovered late. A programme that queries domestic engines, stores the raw responses and shares them with a head-office team is moving data across a border, and whether that is routine or restricted depends on what the data contains and who is handling it. The answer shapes practical things: whether reviewers sit in-market, whether raw run files can be exported for audit, and what a procurement team will accept in a contract.
The workable pattern is usually to run and store in-market, then share a reviewed reporting layer outward. That keeps raw evidence available to the people who can act on it while giving head office a figure it can compare with other regions. It also means the in-market team has to be real rather than a routing step to a translation vendor.
How do you build a reporting baseline that survives scrutiny?
Fix the prompt set, the engines and the measurement window first, record a baseline before any content changes, and keep the raw run files.
Enterprise buyers discount AI-visibility reporting for a good reason: much of it is unauditable. The number arrives without the queries behind it, the engine list shifts between periods, and nothing can be re-run. A baseline is what makes later movement a claim rather than an assertion.
Three things make it hold up. The prompt set is written down and frozen, with additions tracked separately from the fixed core. Every run keeps its raw output, so a disputed figure can be inspected rather than argued about. And the reporting states citation rate, entity correctness and share of answer per engine and per market, rather than one blended figure across a region that contains several different search markets. A provider unwilling to show a raw run file from a live client period is asking for trust in a number that cannot be checked — one of the questions worth asking before hiring AEO and GEO help.
Lifewood runs this as one programme: measurement and the native-speaker content that answers it, under two independent review passes with timestamped approval records, across 100+ languages and 40+ delivery centres in 30+ countries. For the provider landscape across the region, see our comparison of the 8 best AEO and GEO firms in Asia for enterprise brands and our generative engine optimisation service.