GoKawiilMy brother is a luxury mechanic shop owner, and he’s losing thousands of dollars per month because he misses hundreds of calls per week. He’s under the hood all day. The phone rings, he can’t answer, the customer hangs up and calls someone else. That’s a lost job — sometimes a $450 brake service, sometimes a $2,000 engine repair — just gone because no one picked up.
So I’m building him an AI receptionist. I named it Axle — like a car axle — because of course I did. 😏
This isn’t a generic chatbot. It’s a custom-built voice agent that answers his phone, knows his exact prices, his hours, his policies, and can collect a callback when it doesn’t know something. To get this right requires a custom build, so first I scraped his website data, created a product requirements document (PRD), and scoped the project into a 3-part build.
Step 1: Building the Brain (RAG Pipeline)
The first step was making sure the AI could actually answer questions accurately — without hallucinating prices or making things up.
A raw LLM is dangerous here. If a customer asks “how much for brakes?” and the AI guesses $200 when the real answer is $450, that’s a broken expectation and a frustrated customer. The fix is Retrieval-Augmented Generation (RAG): instead of letting the model guess, you give it a knowledge base of real information and make it answer only from that.
Here’s what I did:
Scraped Dane’s website — I pulled his service pages and pricing into markdown files. From there I built a structured knowledge base covering 21+ documents: every service type, pricing, turnaround times, hours, payment methods, cancellation policies, warranty info, loaner vehicles, and what car makes he specializes in.
Embedded the knowledge base into MongoDB Atlas — Each document gets converted into a 1024-dimensional vector using Voyage AI ( voyage-3-large ). These vectors capture the semantic meaning of each document, not just the keywords. They’re stored in MongoDB Atlas alongside the raw text, with an Atlas Vector Search index on the embedding field.
Built the retrieval pipeline — When a customer asks a question, the query gets embedded using the same Voyage AI model and then run against the Atlas Vector Search index. It returns the top 3 most semantically similar documents — so “how much for a brake job?” correctly retrieves the brake service pricing doc even if those exact words don’t appear together.
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