Picture this: you’re a busy realtor, juggling calls while showing homes, and a buyer rings your office. You miss the call - and just like that, you miss a lead.
Now imagine this instead: an AI voice agent picks up instantly, talks to the caller, figures out exactly what they want - even if they ramble, interrupt, or drop five preferences in one sentence - and schedules a showing, all while you’re on the road.
This isn’t a demo. It’s real. It’s built. And I’m giving away the full code.
The Problem with Most Voice AI
Most off-the-shelf bots sound good - until you put them in a real conversation.
- The moment a user talks over them? They freeze.
- The second someone answers multiple questions at once? They glitch.
- If the user is vague or changes their mind mid-sentence? Good luck.
I wanted to build something better - an AI voice agent that doesn’t crumble when things get messy.
So I built one from scratch. Here’s what it does:
- Handles impatient or interrupting callers
- Understands multiple preferences in one go
- Recommends properties based on actual user intent
- Books appointments directly into a Google Calendar
- Sends SMS or WhatsApp confirmations automatically
- Runs 24/7 - even while you sleep
It’s not just conversational - it’s context-aware, fast, and production-ready.
👉 Watch the full video on YouTube
👉 Book a free AI consultation call with us
How It All Works: Tools That Make the Magic Happen
This project isn’t powered by some fancy, monolithic platform. It’s stitched together using real tools, each chosen carefully to solve specific problems.
Here’s the high-level flow:
Buyer Call
↓
VAPI (Voice Capture & STT)
↓
PydanticAI Agent (Conversational Brain)
↓
ChromaDB (Semantic Property Search)
↓
n8n (Automation: Calendar + SMS)
↓
Google Calendar + Twilio
Let’s break down why I picked each one.
🔊 VAPI - Voice Call Handling
VAPI manages the incoming and outgoing calls. It:
- Converts speech to text
- Sends user input to my external agent
- Converts agent replies back to speech
- Lets me use my own LLM, hosted on my infra
That last point is critical. I didn’t want some black-box bot - I needed full control. VAPI acts as the voice shell, not the brain.
🧠 PydanticAI - The Conversational Core
I used PydanticAI to build the actual agent logic. It gave me:
- Full control over prompt engineering, memory, and user context
- Built-in validation + parsing to keep things clean
- Clear separation between agent behavior and business logic
You might ask: Why not just use n8n or CrewAI?
Because when you’re building a voice bot that reacts in real time, you can’t afford vague control. With Python + PydanticAI, I control every response, every condition, and every fallback.
🏠 ChromaDB - Property Recommendations That Make Sense
This isn’t just filtering a CSV file. When someone says:
“Looking for a 3 bed, 2 bath in Chicago around $500k”
…I want the agent to understand that. Not keyword match it.
That’s why I used semantic search via ChromaDB - an open-source vector database. It lets the AI match user preferences to real listings based on meaning, not exact words.
🔄 n8n - Scheduling and Messaging, Made Easy
I use n8n for two specific things:
- Checking Google Calendar availability
- Sending SMS or WhatsApp confirmations via Twilio
And that’s it.
All the logic - like time parsing, date constraints, fallback slots - that stays in Python. n8n is just the connector. This way, I keep all logic centralized, and avoid brittle workflows in n8n.
Instant Results - Even When You’re Offline
The end result is a voice agent that runs 24/7, understands natural language, and moves deals forward without human input. It doesn’t just survive real-world calls - it thrives in them.
👉 Watch the full video on YouTube
👉 Book a free AI consultation call with us
In Part 2, I’ll walk through the exact agent design, prompt engineering, and how I built a custom vector database of property listings for ultra-fast recommendations.
Follow along - because we’re just getting started.