Scenario pain points
Customer service usually needs to deal with the multi-step process of consultation → classification → answer → follow up, manual inefficiency and difficult to respond 7 × 24.
Solution Architecture
- Layer 1: Categorical Intelligentsia: Parsing documents uploaded by customers with PDF Intelligent Body
- Tier 2: Professional Answer Intelligence Body: Integration of RAG technology to call the product knowledge base
- Layer 3: Sentiment Analysis Intelligence: Recognize customer emotions through multimodal capabilities
- Layer 4: Callback Intelligentsia: Set up regular follow-up reminders
carry out in practice
- Defining Workflow Sequences and Trigger Conditions with YAML Configuration
- Add customized dictionaries of industry terms for each intelligence
- Enable long-term memory to save customer interaction history
- Setting threshold rules for exception to manual
Effectiveness evaluation
KPIs such as: response time, resolution rate, customer satisfaction, etc. can be monitored through built-in analytics tools to continuously optimize the workflow.
This answer comes from the articlePraisonAI: A Low-Code Multi-Intelligent Body Framework to Simplify Automation Solutions for Complex TasksThe
May not be reproduced without permission:AI productivity tools " How to apply PraisonAI in customer service scenarios to create efficient workflows of AI intelligences?
