Automate WooCommerce Support: AI Personal Shopper & RAG Integration
Deliver instant, personalized product recommendations and store information, reducing customer service response times by over 90% and boosting conversion rates.
Manually answering product queries and general store questions consumes valuable staff time and delays customer responses, impacting sales. This n8n workflow deploys an AI personal shopper that intelligently responds to product searches via WooCommerce and provides instant answers to store-related queries using RAG, significantly improving customer experience and operational efficiency.
Documentation
AI Personal Shopper & RAG for WooCommerce
This powerful n8n workflow transforms your e-commerce customer support into an intelligent, always-on AI assistant. It seamlessly integrates a sophisticated AI agent with your WooCommerce store and a RAG (Retrieval Augmented Generation) system to provide instant, accurate responses to customer queries, enhancing shopping experiences and streamlining operations.
Key Features
- Intelligent Product Discovery: AI-powered extraction of search parameters (keywords, price, SKU) to find products directly from your WooCommerce catalog.
- Instant Store Information: Leverages RAG to answer common questions about store hours, address, contact details, and general policies.
- Personalized Chat Experience: Maintains chat memory to provide continuous, context-aware conversations.
- Automated RAG Database Setup: Easily initialize your RAG system by ingesting documents from Google Drive into Qdrant.
- Scalable & Efficient Customer Support: Reduces manual support workload, allowing staff to focus on complex issues.
How It Works
The workflow initiates when a customer sends a chat message. An 'Information Extractor' first determines if the query is product-related, pulling out specific details like keywords or price ranges. An AI Agent then orchestrates the response: if it's a product search, it uses the 'personal_shopper' tool to query WooCommerce; if it's a general inquiry, it consults the 'RAG' system (powered by Google Drive documents and Qdrant vector store) to retrieve relevant information. Chat history is maintained for a personalized interaction. A separate initial setup flow allows you to populate your Qdrant vector store with knowledge documents from Google Drive.