Build an AI Powered Phone Agent - with Retell, Google Calendar, and RAG

This Workflow simulates an AI-powered phone agent with two main functions: 1. **Appointment Booking** - It can schedule appointments directly into Google Calendar. 2. **RAG-based Information Retrieval** - It provides answers using a Retrieval-Augmented Generation (RAG) system. For example, it can respond to questions such as store opening hours, return policies, or product details. The guide also explains **how to purchase a dedicated phone number** (with a +1 prefix) and link it to the AI agent. This setup is cost-effective, as it uses a **FREE $10 credit** to operate without additional charges in the beginning.  --- ### **Advantages** - **24/7 Availability** - The AI agent can answer calls and assist customers at any time. - **Automation** - It reduces the workload on human staff by handling repetitive tasks like appointment scheduling and FAQ responses. - **Easy Integration** - Built with n8n, it's flexible and customizable for various platforms and tools. - **Low-cost Setup** - Using the free credit, businesses can get started without an upfront investment. --- ### **Use Cases** - **E-commerce** - Answer common product questions or order inquiries. - **Retail Stores** - Provide store hours, address info, and return policies. - **Restaurants** - Make reservations or share menu information. - **Service Providers** - Book appointments or consultations. - **Any Local Business** - Offer phone support without needing a live operator. --- ### **How It Works** This Workflow simulates an AI-powered phone agent with two primary functions: 1. **Appointment Booking** - The workflow captures call events (e.g., `call_ended` or `call_analyzed`) and extracts key details (transcript, caller info, duration, etc.). - Using OpenAI, it summarizes the conversation and parses structured data (e.g., names, contact info, dates). - For scheduling, it converts user-provided dates into Google Calendar-compatible formats and creates events automatically. 2. **RAG-Based Information Retrieval** - When a query is received (e.g., store hours, product details), the workflow retrieves relevant information from a Qdrant vector store. - An AI agent processes the query using the retrieved data and responds via a webhook, ensuring accurate, context-aware answers. --- ### **Set Up Steps** 1. **Prepare Qdrant Vector Store** - Create/refresh a Qdrant collection (via HTTP requests). - Upload and vectorize documents (e.g., from Google Drive) using OpenAI embeddings. 2. **Configure RetellAI Agent** - Sign up for RetellAI, create an agent, and set the webhook URLs (`n8n_call` for call events, `n8n_rag_function` for RAG queries). - Purchase a Twilio phone number and link it to the agent. 3. **n8n Workflow Setup** - Connect OpenAI, Qdrant, Google Calendar, and Telegram nodes with credentials. - Customize prompts for summarization, date parsing, and RAG responses. - Test the workflow to ensure data flows from call events to processing to actions (e.g., calendar bookings, Telegram alerts). 4. **Deploy** - Trigger the workflow via RetellAI webhooks during calls. - Monitor outputs (e.g., call summaries in Telegram, calendar events). **Note**: Replace placeholders (e.g., `QDRANT_URL`, `COLLECTION`, `CHAT_ID`) with actual values. --- ### **Need help customizing?** [Contact me](mailto:info@n3w.it) for consulting and support or add me on [Linkedin](https://www.linkedin.com/in/davideboizza/).