AI Customer Service Bot in Practice: Building Intelligent CS with Chinese Models
Build an enterprise-grade AI customer service system based on ChinaWHAPI, supporting multi-turn conversation, intent detection, FAQ matching, and human handoff.
Customer Service Bot Architecture
AI CS = intent detection (determines what the user wants) + FAQ matching (finds answers from knowledge base) + answer generation (integrates retrieval results) + human handoff decision (determines when human intervention is needed).
Intent Detection
{"model":"qwen3.5-flash","messages":[{"role":"user","content":"Classify the following user question, output JSON: {"intent": "refund/logistics/product inquiry/complaint/other"}
User question: {question}"}]}FAQ Matching
Vectorize enterprise FAQs into a vector database. When a user asks a question, do semantic retrieval to find the 3 most relevant FAQs, then pass the question and FAQs together to the model for answer generation.
Human Handoff Decision
Trigger human handoff when user sentiment is negative, legal issues are involved, or the problem remains unsolved after multiple attempts. Use a classification model to detect user sentiment and satisfaction.
Cost Control
Customer service is a high-concurrency scenario — use Qwen3.5 Flash for intent detection and simple Q&A, only calling stronger models when deep answers are needed. This keeps 80% of simple questions at very low cost.