AI search optimization for China.

What is AI search optimization for China?

AI search optimization for China is the practice of making your brand findable, citable and recommendable inside the answers that DeepSeek, Qwen (Alibaba's Tongyi Qianwen) and Doubao (ByteDance) return to Mainland-Chinese consumers. It overlaps with Baidu SEO at the index layer (Baidu's index feeds ERNIE Bot, for instance) but the optimisation work is materially different: AI search is generative-answer participation, not blue-link ranking.

For US and UK brands, the relevant question is rarely "are we on page one of Baidu" anymore — it is "when a Mainland-Chinese consumer asks DeepSeek for a recommendation in our category, does our brand get named, and how prominently?". Different question, different optimisation work.

The two-stage measurement framework

AI search has two stages, not one — and treating them as one is the most common mistake we see in early China AI visibility work:

Stage 1 — Citation selection

Does your domain enter the engine's source pool when a user asks a category-relevant question? This is the most binary signal: either the engine fetched, indexed and considered your page, or it did not. Failures at this layer trace back to robots.txt misconfigurations (frequently: blocking training bots also accidentally blocking OAI-SearchBot-style retrieval bots), missing llms.txt, weak Bing or Baidu index coverage, or geo-blocking at the CDN edge that hides your site from Mainland-CN traffic entirely.

Stage 2 — Citation absorption

Even if your page is in the source pool, does it actually shape the answer language — provide the words, structure or facts the engine reuses — or does it sit in the pool unused? This is where content design matters: pages with real numbers, dated comparisons and named entities are cited 50%+ more than vague pages (per the published GEO measurement framework). Pages under 500 words rarely absorb. Pure FAQ-formatted pages underperform. Encyclopedia / explainer pages outperform news-style content by ~3× per citation.

Stage 3 — User-visible mention

Downstream of both selection and absorption: does the final answer the user reads name your brand, and how prominently? A page can be selected often but absorbed weakly; a brand can be absorbed but mentioned only in a long-tail position. Generic AI-visibility tools collapse all three stages into one number. Eastbound reports each separately because the fix for each is different.

Per-engine differences (DeepSeek, Qwen, Doubao)

Across our 540-call source-influence panel (May 2026, 30 prompts × 3 LLMs × 3 reps × 2 turns), the three engines cited Mainland-CN sources at materially different rates and drew on different secondary surfaces:

• DeepSeek — 72.3% Mainland-CN sources, 21% Wikipedia, 20% YouTube, Reddit secondary. The most Western-balanced of the three; off-site encyclopedic presence (Wikipedia / Wikidata) was the strongest predictor of brand mention rate among the signals we tested. Read the full DeepSeek playbook →
• Qwen — 85.0% Mainland-CN sources, with overrepresentation of regulatory and professional-association content (regulators, ministry-level bodies, vertical industry associations, academic institutions). Best engine for regulated categories. Runs on DashScope international, NOT BytePlus. Read the full Qwen playbook →
• Doubao — 88.6% Mainland-CN sources, with strong commerce/lifestyle-aggregator lean (SMZDM 72%, Xiaohongshu 64% in our handbag panel). The category-level surfacing engine for FMCG and aspirational consumer goods. Runs on BytePlus ModelArk international, NOT DashScope. Read the full Doubao playbook →

Top-15 source overlap (Jaccard) between the three engines was 0.20–0.30 — they are not interchangeable, and AI search optimization for one does not transfer to the others. For the full reference on engine differences, see China AI visibility.

What changes a brand can make

Improvement in AI search optimization for China groups into three layers, by effort and by leverage:

1. 1-hour layer — technical hygiene. Granular robots.txt across the five bot buckets (training, retrieval, user-triggered, opt-out tokens, undeclared). llms.txt at root with Links + About sections. llms-full.txt for any site with depth. Submit sitemap to Google Search Console + Bing Webmaster Tools. IndexNow API key + post-on-publish webhook. Markdown alternates on top-10 pages. None of this requires content rewrites.
2. Multi-week layer — content design. Page-length sweet spot 1,000–3,000 words; specificity beats fluff; encyclopedia / explainer pages outperform news pages by ~3× per citation. Pure FAQ-format pages underperform — do not pad with redundant FAQ sections. Sites that publish multi-week reference-style content rather than weekly news posts tend to absorb better in our panels.
3. Multi-quarter layer — third-party source-graph publishing. The compounding moat. Baike, Zhihu, Xiaohongshu, SMZDM, Bilibili, Mainland vertical media. Brands cited by 3rd parties are referenced ~6.5× more often than brands cited only on their own domain (published GEO research). Most US/UK brands need regional partners for execution at this layer; we work with partners where local accounts are required.

Layers 1 and 2 are testable on your own site. Layer 3 is where moats live. Treating any single layer as the whole strategy is a known failure mode.