{"slug": "i-asked-six-chinese-ai-engines-to-recommend-crm-software-hubspot-never-appeared", "title": "I asked six Chinese AI engines to recommend CRM software. HubSpot never appeared.", "summary": "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.", "body_md": "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.\n\nThis post is about one slice of it: the CRM category, where the result was more extreme than I expected.\n\n8 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**.\n\nThe 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.\n\nEvery Chinese engine I tested speaks the OpenAI chat-completions dialect, so the collector is boring on purpose:\n\n``` js\nasync function ask(prompt) {\n  const body = {\n    model: cfg.model,\n    messages: [{ role: \"user\", content: prompt }],\n    temperature: 0.3,\n    max_tokens: 1200,\n  };\n  // Ark reasoning models bury the answer in reasoning_content unless you do this\n  if (process.env.BENCH_THINKING === \"disabled\") body.thinking = { type: \"disabled\" };\n\n  const res = await fetch(`${cfg.baseUrl}/chat/completions`, {\n    method: \"POST\",\n    headers: {\n      \"content-type\": \"application/json\",\n      authorization: `Bearer ${cfg.apiKey}`,\n    },\n    body: JSON.stringify(body),\n    signal: AbortSignal.timeout(Number(process.env.BENCH_TIMEOUT_MS || 90000)),\n  });\n  if (!res.ok) throw new Error(`${res.status} ${(await res.text()).slice(0, 200)}`);\n  const j = await res.json();\n  return j.choices?.[0]?.message?.content || \"\";\n}\n```\n\nOne 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.\n\nMention rate on **open** category questions (\"which CRM should I consider in China?\", brand name never mentioned in the prompt):\n\n| Brand | Open-question mention |\n|---|---|\n| Salesforce | 87.5% |\n| Microsoft Dynamics 365 | 50.0% |\n| Zoho CRM | 12.5% |\n| HubSpot | 0.0% |\n| Pipedrive | 0.0% |\n| Freshsales | 0.0% |\n| SugarCRM | 0.0% |\n| Monday CRM | 0.0% |\n\nFive of eight global brands never surfaced. Not once, across every phrasing and both runs.\n\nThe 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).\n\nAsk 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.\n\nSo 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.\n\nReading HubSpot's 84 branded answers, three things stood out:\n\nThings I'd want to know if I were reading someone else's version of this:\n\n`content: \"\"`\n\nwith 2,000 characters in `reasoning_content`\n\nuntil I forced answer-only mode.`deepseek-chat`\n\nentirely 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:\n\n[https://github.com/David88666/china-ai-visibility-benchmark](https://github.com/David88666/china-ai-visibility-benchmark)\n\nSwap in your own brands and category — the panel has `{brand}`\n\n/ `{category}`\n\n/ `{competitor}`\n\nslots — and you can reproduce the whole thing in an afternoon.\n\nIf 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.", "url": "https://wpnews.pro/news/i-asked-six-chinese-ai-engines-to-recommend-crm-software-hubspot-never-appeared", "canonical_source": "https://dev.to/visibilityatlas/i-asked-six-chinese-ai-engines-to-recommend-crm-software-hubspot-never-appeared-pj4", "published_at": "2026-07-29 12:58:44+00:00", "updated_at": "2026-07-29 13:07:08.339546+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-products", "ai-tools", "ai-research"], "entities": ["Salesforce", "HubSpot", "Microsoft Dynamics 365", "Zoho CRM", "Pipedrive", "Freshsales", "SugarCRM", "Monday CRM"], "alternates": {"html": "https://wpnews.pro/news/i-asked-six-chinese-ai-engines-to-recommend-crm-software-hubspot-never-appeared", "markdown": "https://wpnews.pro/news/i-asked-six-chinese-ai-engines-to-recommend-crm-software-hubspot-never-appeared.md", "text": "https://wpnews.pro/news/i-asked-six-chinese-ai-engines-to-recommend-crm-software-hubspot-never-appeared.txt", "jsonld": "https://wpnews.pro/news/i-asked-six-chinese-ai-engines-to-recommend-crm-software-hubspot-never-appeared.jsonld"}}