I asked six Chinese AI engines to recommend CRM software. HubSpot never appeared.

A developer tested six Chinese AI engines on CRM recommendations and found that HubSpot never appeared in open-ended queries, despite being mentioned in branded queries. Salesforce dominated the open-question mention rate at 87.5%, while five of eight global CRM brands were never surfaced. The developer warns that branded checks return false positives and advises running open-category tests in local languages.

I spend my working life on how Chinese search and AI systems describe international brands, and I got tired of arguing about it from screenshots. So I built a harness and recorded 4,704 answers across six Chinese AI engines. This post is about one slice of it: the CRM category, where the result was more extreme than I expected. 8 international CRM brands Salesforce, HubSpot, Zoho CRM, Pipedrive, Microsoft Dynamics 365, Freshsales, SugarCRM, Monday CRM × 42 Chinese-language buyer questions × 2 runs = 672 recorded answers . The questions come in five groups: branded checks, open category discovery, comparison, decision intent, and risk. That grouping turned out to matter more than anything else in the study. Every Chinese engine I tested speaks the OpenAI chat-completions dialect, so the collector is boring on purpose: js async function ask prompt { const body = { model: cfg.model, messages: { role: "user", content: prompt } , temperature: 0.3, max tokens: 1200, }; // Ark reasoning models bury the answer in reasoning content unless you do this if process.env.BENCH THINKING === "disabled" body.thinking = { type: "disabled" }; const res = await fetch ${cfg.baseUrl}/chat/completions , { method: "POST", headers: { "content-type": "application/json", authorization: Bearer ${cfg.apiKey} , }, body: JSON.stringify body , signal: AbortSignal.timeout Number process.env.BENCH TIMEOUT MS || 90000 , } ; if res.ok throw new Error ${res.status} ${ await res.text .slice 0, 200 } ; const j = await res.json ; return j.choices?. 0 ?.message?.content || ""; } One env-var swap per engine — base URL, model ID, platform label — and the same panel runs everywhere. Total API spend for the whole study was under $5. Mention rate on open category questions "which CRM should I consider in China?", brand name never mentioned in the prompt : | Brand | Open-question mention | |---|---| | Salesforce | 87.5% | | Microsoft Dynamics 365 | 50.0% | | Zoho CRM | 12.5% | | HubSpot | 0.0% | | Pipedrive | 0.0% | | Freshsales | 0.0% | | SugarCRM | 0.0% | | Monday CRM | 0.0% | Five of eight global brands never surfaced. Not once, across every phrasing and both runs. The engine's own framing, repeated almost verbatim across answers: "若企业有全球化需求，优先考虑 Salesforce 或 Microsoft Dynamics" — if you need global reach, consider Salesforce or Dynamics. The "international option" isn't a shelf with several slots. It's one seat, and Salesforce is in it. Everything else in the list goes to domestic vendors 销售易, 纷享销客, Kingdee . Ask the same engines about HubSpot by name and you get a competent, fair, well-structured paragraph. Branded-question mention rate was ~100% for every brand, on every engine. So the check most teams run — "I asked DeepSeek about us and it looked fine" — returns a false positive by construction. The prompt hands the model the answer. The number that predicts whether a buyer ever meets you is the open one, and for this sample it was 23% across all categories. Reading HubSpot's 84 branded answers, three things stood out: Things I'd want to know if I were reading someone else's version of this: content: "" with 2,000 characters in reasoning content until I forced answer-only mode. deepseek-chat entirely and forced a rename. These products move faster than the analysis does.Aggregate results and the reusable 42-question bilingual panel are CC BY 4.0: https://github.com/David88666/china-ai-visibility-benchmark https://github.com/David88666/china-ai-visibility-benchmark Swap in your own brands and category — the panel has {brand} / {category} / {competitor} slots — and you can reproduce the whole thing in an afternoon. If you work on something with international reach, the cheap version of this test is worth running once: three engines, five open category questions in the local language, twice each. It takes an hour, and it's a very different picture from asking the model about yourself by name.