Key takeaways

Support bots need retrieval + guardrails more than raw chat IQ. Ground answers in your help center (/en/articles/china-llm-embedding-rag-setup-2026), force citations, and escalate billing/legal. Pick models by language mix and tool needs—not demos.

Model roles

Qwen/GLM: strong CN helpdesk tone and enterprise flows. DeepSeek: technical troubleshooting and log reading. Kimi: long policy PDFs. Keep JSON for ticket fields (/en/articles/china-llm-json-structured-output-2026).

Launch checklist

FAQ coverage → refusal list → human handoff SLA → latency budget (/en/articles/china-llm-latency-failover-2026) → cost caps (/en/articles/china-llm-optimization-guide-2026) → compliance review (/en/articles/china-llm-compliance-overseas-2026).

Next steps on Swift Horse

RAG selection /en/articles/china-llm-rag-selection-guide → agents /en/articles/china-llm-agent-tool-calling-2026 → /en/match.

FAQ

Best Chinese LLM for customer support?

Depends on CN vs EN mix and tools—shortlist Qwen/GLM and measure deflection rate.

Do I need RAG for a support bot?

Almost always—un-grounded chat invents policies.

Can one bot serve CN and EN?

Yes with bilingual prompts and locale routing; evaluate both languages.

Is this a chatbot SaaS offer?

No—independent Swift Horse build guide for China LLM stacks.