I Built an AI Receptionist for a Luxury Mechanic Shop – Part 1

5 hours ago

The author built an AI receptionist named Axle for their brother's luxury mechanic shop to handle missed calls and prevent lost business.
The AI receptionist uses Retrieval-Augmented Generation (RAG) to provide accurate answers based on a scraped knowledge base from the shop's website.
The knowledge base includes service types, pricing, policies, and other details, stored in MongoDB Atlas with vector embeddings for semantic search.
Claude (Anthropic) generates responses strictly from the knowledge base to avoid hallucinations and maintain accuracy.
Vapi was chosen as the voice platform to handle telephony, speech-to-text, and text-to-speech, with a FastAPI webhook server for processing queries.
The system logs all calls and callbacks in MongoDB for tracking and analysis, turning phone interactions into valuable data.
Voice tuning was critical, with careful selection of a natural-sounding voice (Christopher from ElevenLabs) and rewriting prompts for conversational delivery.
The stack includes Vapi, Ngrok, FastAPI, MongoDB Atlas, Voyage AI, Claude, and Python for integration.
Future enhancements include calendar integration for booking appointments, SMS notifications, and a dashboard for managing callbacks.
The key insight is to ground the AI in a real knowledge base and design a robust fallback flow for unanswered questions.

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