Building an AI Customer Support Agent for Ecommerce business
To build our AI Customer Support Agent for Ecommerce, we need the following components:
A Large Language Model (LLM): We will use Anthropic’s Claude 3.5 Sonnet model to power our AI agent. This requires an API key from Anthropic, which can be obtained with a small initial credit.
A Database: We need a database to store and retrieve customer and order information. Our setup includes two tables:
Customer Table: Contains details such as ID, name, email, phone number, and username.
Orders Table: Stores order ID, customer ID, product details, quantity, price, and order status.
Tools for Database Interaction: The AI agent should be able to interact with the database to fetch and update customer order details.
A Chat Interface: A front-end chat interface will allow users to communicate with the AI agent smoothly.
We first install and import the Anthropic API, set up our API key, and configure the model (Claude 3.5 Sonnet) for use in our AI system.
Step 2: Creating a Dummy Database
For demonstration purposes, we create a dummy database with two tables:
Customer Table: Contains user details such as ID, name, email, and phone number.
Orders Table: Stores order ID, customer ID, product name, quantity, price, and order status.
Step 3: Defining Functions to Retrieve Information
To interact with the database, we define four key functions:
Get User: Retrieves user details based on email, phone number, or username.
Get Order by ID: Fetches order details using an order ID.
Get Customer Orders: Retrieves all orders associated with a specific customer ID.
Cancel Order: Cancels an order if it is still in processing and not yet shipped.
Step 4: Creating Tool Schemas
We define schemas for our tools to allow the AI agent to understand and use them effectively. These include:
Get User Tool: Looks up a user by email, phone, or username.
Get Order by ID Tool: Fetches order details using an order ID.
Get Customer Orders Tool: Retrieves all orders placed by a specific user.
Cancel Order Tool: Cancels an order if the status allows it.
Step 5: Integrating the Tools with Claude AI
We integrate these tools into our AI agent, allowing it to process user queries effectively. The AI determines the appropriate tool to use based on the user’s request.
Step 6: Setting Up the User Interaction Flow
The AI agent receives a request (e.g., “Show me my orders, my username is ria123”).
It decides which tool to use (e.g., the Get User tool to fetch customer details first).
The response is processed and displayed to the user.
If the user requests cancellation, the Cancel Order tool checks if the order can be canceled and processes it accordingly.
Testing the AI Chatbot
After implementing the chatbot, we run test cases where users:
Check order status.
Retrieve order details using customer ID.
Cancel an order if it is still in the processing stage.
When tested in an interactive chat, the AI successfully identifies the appropriate tools to retrieve order details and cancel orders when possible. We also optimize performance by switching from CPU to GPU processing to reduce lag.
Final Thoughts
Building an AI-powered customer support agent for e-commerce simplifies order management, enhances customer satisfaction, and reduces manual workload.
By integrating AI with a well-structured database and predefined functions, businesses can automate repetitive support tasks and focus on improving the overall customer experience.
We hope this guide helps you in building your own AI-driven customer support system. Stay tuned for our next tutorial, where we’ll explore further enhancements to this AI agent!
