China AI visibility for global brands.

What is China AI visibility?

China AI visibility is the category that covers how Mainland-Chinese AI assistants — primarily DeepSeek, Qwen and Doubao — surface, cite and recommend brands when answering Mainland-CN consumer questions. It is distinct from generic LLM visibility (which is dominated by ChatGPT and Google AI Overview), distinct from Baidu SEO (which is rank-in-search-results, not generative-answer participation), and distinct from Western GEO (which operates on an English-corpus, English-source-graph footing).

The category has two measurement stages, not one. Citation selection determines whether your domain enters an engine's source pool when a user asks a category-relevant question. Citation absorption determines whether the page actually shapes the answer language — provides the words, structure or facts the engine reuses — versus sitting in the source pool unused. The user-visible mention is a third stage downstream of both. A page can be selected often but absorbed weakly. A brand can be absorbed but mentioned only in a long-tail position.

For global brands operating in or expanding to Mainland China, China AI visibility is increasingly a discrete strategic question, separate from the rest of the digital marketing stack. The reason follows. (For the broader category definition independent of geography, see what is generative engine optimization; for the diagnostic ladder a brand walks through when it can't find itself on a Chinese AI today, see why isn't my brand on DeepSeek.)

Why China AI visibility is distinct from generic AI visibility

The three Chinese-trained engines do not draw from the same source ecosystem as Western engines, and they do not draw from each other's source ecosystems either. Across a 540-call panel (30 prompts × 3 LLMs × 3 reps × 2 turns) we ran in May 2026, the three engines cited Mainland-CN sources at materially different rates:

Top-15 source overlap (Jaccard) between the three engines was 0.20–0.30. These are descriptive observations from one panel — they do not tell us what is in any engine's training corpus, only what each engine self-attributes when answering. But the implication for measurement is structural: an AI-visibility framework built on ChatGPT or Gemini output cannot be ported to the Chinese engines without rebuilding the source-substrate model. The engines look at materially different sets of websites.

The pattern replicates. On a re-run of the identical 30-prompt panel one week later, source mention rates correlated at Pearson r 0.97–0.99 across all three LLMs (ICC 0.97–0.99). Top-5 source membership was perfectly stable (κ = 1.00). One caveat we publish loudly: Doubao top-15 κ = 0.46 — long-tail source ranking is less stable than the top-5 for Doubao specifically (a granular-tag normalisation issue documented in our reliability report). We treat top-5 with high confidence and the long tail with the appropriate hedge.

For more on the engine-by-engine differences, see our research briefing Why your brand looks different on every Chinese AI.

The three engines that matter — DeepSeek, Qwen and Doubao

The three engines share a Mainland-CN consumer audience but surface different evidence to answer the same prompt. Each is best understood by its source-association pattern, not by raw mention count. The differences are not "DeepSeek is better than Qwen is better than Doubao" — they are different reading patterns, suited to different categories.

The Mainland source graph

The single highest-leverage signal in published GEO research is third-party citation: brands cited by 3rd parties are referenced roughly 6.5× more often than brands cited only on their own domain. For Mainland-Chinese AI visibility, this means the source-graph layer is often more decisive than the on-site layer. The Mainland source graph — what it is, what each platform contributes, and which engines weight which platforms most heavily — is what separates a strong China AI visibility position from a weak one.

A working priority list of Mainland sources, ordered by leverage observed across our panels:

• 百度百科 (Baidu Baike) — encyclopedia entry; survives across LLM training cycles; cited consistently by all three engines.
• 知乎 (Zhihu) — long-form Q&A; high absorption rate in DeepSeek and Doubao; in our handbag panel DeepSeek surfaced Zhihu in 97% of responses.
• 小红书 (Xiaohongshu / RED) — strong for B2C lifestyle, beauty, FMCG; weaker for B2B; Doubao surfaced Xiaohongshu in 64% of handbag-panel responses.
• 什么值得买 (SMZDM) — deal aggregator; very high weight on Doubao for commerce-related queries (72% in handbag panel) but weight collapses at the ultra-luxury price tier — the replacement stack at that tier is The Purse Forum, Vogue Business, WWD, and auction-house archives (Sotheby's, Christie's, Baghunter).
• 36氪 / 虎嗅 / 钛媒体 — tech / business vertical media; matters for B2B SaaS, China tech market entry, and any narrative-driven category.
• 微信公众号 (WeChat official accounts) — well-indexed by Tencent Yuanbao especially; relevance varies by category.
• Bilibili / Douyin — video transcripts; growing fast in DeepSeek and Doubao corpora.

A common mistake is treating these platforms as universally must-have. The handbag-panel finding above shows the SMZDM weight collapses at the ultra-luxury tier; the source priority for Hermès / Chanel / Patek Philippe / Audemars Piguet looks materially different from the priority for an aspirational handbag brand at ¥3,000–10,000 RMB. Source-graph strategy must be category- and tier-specific.

For a deeper read on the off-site substrate question, see Traditional SEO alone won't get you into Chinese AI answers. For the luxury-specific case study, see The 5 websites Chinese AI reads before recommending a luxury brand.

How measurement works

Eastbound measures China AI visibility in two stages, mirroring the citation-selection-vs-citation-absorption framework introduced in Zhang Kai & Yao Jingang's 2026 GEO measurement paper:

1. Stratified zh-CN prompt panel. Mainland-CN consumer-voice prompts are run against the live API endpoints of each engine (DeepSeek, Qwen on DashScope international, Doubao on BytePlus ModelArk international), at both broad-category (L1) and positioning-niche (L2) levels, with multiple reps per prompt to control for run-to-run variance.
2. Source attribution and absorption analysis. Each response is decomposed into selection (which sources surface) and absorption (which sources shape the answer language). Source attributions are normalised to canonical platform IDs; long-tail sources are flagged separately because their stability is lower than top-5.

Reliability discipline matters because the noisiest layer of any AI-visibility report is the long-tail sources. We report top-5 κ alongside top-15 κ, Pearson r and ICC for each engine in our reliability documentation. A measurement framework that reports only the headline κ and hides long-tail noise is reporting selectively. We disclose that Doubao top-15 κ is 0.46 even though the top-5 number (1.00) is the easier story to tell.

Every recommendation in our public output is labelled as one of three states: measured evidence (we observed this in a panel; n and panel structure stated), prior-knowledge hypothesis (consistent with published research; Eastbound has not measured directly), or planned intervention test (we expect this to help; before/after measurement required to confirm). We do not collapse these three categories into a single "best practice" score, because the evidence cost of each is materially different.

Run the audit on your own URL →

What changes a brand can make

Improvement in China AI visibility groups into three layers, by effort. Each layer compounds with the next; the highest-leverage layer is also the slowest. We organise client work this way:

1-hour layer — technical hygiene

Clear robots.txt across the five bot buckets (training, retrieval, user-triggered, opt-out, undeclared). Ship llms.txt at root with Links + About sections. Submit sitemap to Google Search Console and Bing Webmaster Tools. Add IndexNow API key. Add <link rel="alternate" type="text/markdown"> on top-10 pages. None of this requires content changes; all of it can be done in a single working morning. See the crawler-readiness page for the operational checklist.

Multi-week layer — content design

Content design changes are absorption-optimisation work. The length sweet spot for AI-cited pages is 1,000–3,000 words; pages under 500 words rarely absorb. Specificity beats fluff: pages with real numbers, dated comparisons and named entities are cited 50%+ more often than vague pages. Pure FAQ-format pages underperform — they add noise without new information. Encyclopedia / explainer pages outperform news pages by roughly 3× per citation in published GEO research, which is why this pillar is built as a reference document rather than a news post.

Multi-quarter layer — third-party source-graph publishing

The compounding work is off-site. Building presence on Baike, Zhihu, Xiaohongshu, SMZDM, Bilibili, vertical media — each platform with its own editorial standards, account-aging requirements and trust-building cadence. This layer is where brand-visibility moats actually live. The 6.5× third-party-vs-self-citation amplification is observed across multiple research streams; on-site work alone has a hard ceiling.

Layers 1 and 2 are testable on your own site; layer 3 is the moat. Treating any layer as a standalone solution is a known failure mode.