Intelligent Chatbot for Personalized Insurance Quoting
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Automate personalized insurance quoting with this n8n workflow. It leverages AI to understand customer needs and directly queries your database for the best plan options.
About This Workflow
This n8n workflow, named 'modelo do chatbot,' is designed to power an intelligent chatbot that seamlessly guides users through the insurance quoting process. It begins by capturing user conversation data through a Chat Trigger. The workflow then intelligently processes this information, distinguishing between lead data and conversational inputs. Crucially, it utilizes OpenAI to interpret user queries and extract key details like location, age, profession, and desired insurance type. This extracted information is then used to query a PostgreSQL database for relevant product information, specifically tailoring results based on user-provided criteria and age-based pricing. The workflow ensures a rich conversational memory using Postgres Chat Memory nodes, enhancing the user experience by remembering past interactions. Finally, it can fetch product details from your MySQL database to present personalized insurance plan options.
Key Features
- AI-Powered Conversation Understanding: Leverages OpenAI to interpret and extract crucial user data from conversations.
- Personalized Lead Data Capture: Gathers detailed information about potential leads, including demographics, profession, and insurance needs.
- Dynamic Database Querying: Connects to MySQL to retrieve specific product information based on extracted user criteria.
- Contextual Chat Memory: Utilizes PostgreSQL to maintain a conversational history for a more engaging user experience.
- Conditional Logic for Quoting: Employs an 'If' node to route conversations and data processing based on lead data availability.
How To Use
- Configure Chat Trigger: Set up the
@n8n/n8n-nodes-langchain.chatTriggerwith your initial greeting and webhook settings. - Integrate OpenAI: Connect your OpenAI account via credentials and select the appropriate assistant ID for processing user queries.
- Set up Postgres Chat Memory: Configure the
@n8n/n8n-nodes-langchain.memoryPostgresChatnodes to store and retrieve conversation history, ensuring you have appropriatesessionKeyandtableNameconfigured. - Map Lead Data: Utilize the
n8n-nodes-base.setnodes (e.g., 'Edit Fields1') to map and transform incoming lead data into a format suitable for AI processing and database queries, especially forchatInputandsession_id. - Implement Database Queries: Configure the
n8n-nodes-base.mySqlToolnode (e.g., 'Products in Daatabase') with your SQL query to fetch product details, ensuring you use$fromAI()to dynamically populate query parameters likecityQuery,state,holderAge, etc. - Conditional Routing: Use the
n8n-nodes-base.ifnode to implement logic for handling different conversation paths, such as when lead data is available or when further clarification is needed.
Apps Used
Workflow JSON
{
"id": "c895623a-22cd-48b3-923e-a01874a374cb",
"name": "Intelligent Chatbot for Personalized Insurance Quoting",
"nodes": 26,
"category": "Sales",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
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ID: c895623a-22cd...
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