✅ What problem does this workflow solve?
✅ What problem does this workflow solve? Most e-commerce chatbots are transactional; they answer one question at a time and forget your context right after. This workflow changes that. It introduces a smart, memory-enabled shopping assistant that remembers user preferences, past orders, and previous queries to offer deeply personalized, natural conversations. ⚙️ What does this workflow do? Accepts real-time chat messages from users. Uses Zep Memory to store and recall personalized context. Integrates with: 🛒 Product Inventory 📦 Order History 📜 Return Policy Answers complex queries based on historical context. Provides: Personalized product recommendations Context-aware order lookups Seamless return processing Policy discussions with minimal user input 🧠 Why Context & Memory Matter Traditional bots: ❌ Forget what the user said 2 messages ago ❌ Ask repetitive questions (name, order ID, etc.) ❌ Can’t personalize beyond basic filters With Zep-powered memory, your bot: ✅ Remembers preferences (e.g., favorite categories, past questions) ✅ Builds persistent context across sessions ✅ Gives dynamic, user-specific replies (e.g., "You ordered this last week…") ✅ Offers a frictionless support experience 🔧 Setup Instructions 🧠 Zep Memory Setup Create a Zep instance and connect it via the Zep Memory node. It will automatically store user conversations and summarize facts. 💬 Chat Trigger Use the "When chat message received" trigger to initiate the conversation workflow. 🤖 AI Agent Configuration Connect: Chat Model → OpenAI GPT-4 or GPT-3.5 Memory → Zep Tools: Get_Orders – Fetch user order history from Google Sheets Get_Inventory – Recommend products based on stock and preferences Get_ReturnPolicy – Answer policy-related questions 📄 Google Sheets Store orders, inventory, and return policies in structured sheets. Use read access nodes to fetch data dynamically during conversations. 🧠 How it Works – Step-by-Step Chat Trigger – User sends a message. AI Agent (w/ Zep Memory): Reads past interactions to build context. Pulls memory facts (e.g., "User prefers men's sneakers"). Uses External Tools: Looks up orders, return policies, or available products. Generates Personalized Response using OpenAI. Reply Sent Back to the user through chat. 🧩 What the Bot Can Do 🛍 Suggest products based on past browsing or purchase behavior. 📦 Check order status and history without requiring the user to provide order IDs. 📃 Explain return policies in detail, adapting answers based on context. 🤖 Engage in more human-like conversations across multiple sessions. 👤 Who can use this? This is ideal for: 🛒 E-commerce store owners 🤖 Product-focused AI startups 📦 Customer service teams 🧠 Developers building intelligent commerce bots If you're building a chatbot that goes beyond canned responses, this memory-first shopping assistant is the upgrade you need. 🛠 Customization Ideas Connect with Shopify, WooCommerce, or Notion instead of Google Sheets. Add payment processing or shipping tracking integrations. Customize the memory expiration or fact-summarization rules in Zep. Integrate with voice AI to make it work as a phone-based shopping assistant. 🚀 Ready to Launch? Just connect: ✅ OpenAI Chat Model ✅ Zep Memory Engine ✅ Your Product/Order/Policy Sheets And you’re ready to deliver truly personalized shopping conversations. An n8n automation workflow template by InfyOm Technologies.
InfyOm Technologies
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