Overview

🏡 VacancyFill AI — Property Management Leasing Follow-Up Business

The Business

VacancyFill AI is a ready-to-run AI-powered leasing automation platform for property managers, apartment operators, landlords, and leasing agents. It automates the entire rental prospect pipeline — from the first inquiry to lease signing — so property managers can cut vacancy days in half without hiring more staff.

Property management is a game of margins. Every day a unit sits vacant, the owner loses $50–$150 in rent. The #1 cause of extended vacancies? Slow or inconsistent prospect follow-up. VacancyFill AI solves this by acting as an instant-response leasing team that works 24/7.

What You're Buying

A complete, deployable SaaS platform with:

6 AI Agents

Agent Function
Rental Inquiry Agent Collects budget, move-in date, unit type, pets, household size, showing preferences — validates inputs and confirms instantly
Showing Scheduler Agent Generates SMS invitations and confirmation texts with property details, directions, and reschedule links
Prospect Follow-Up Agent Three automated touches: 24h no-response nudge, 1h post-showing thank-you, 48h application-incomplete reminder
Listing Copy Agent Writes MLS-ready rental descriptions in Professional, Warm, or Luxury tones
Vacancy Campaign Agent Creates weekly social media posts and email blasts for vacant units with urgency messaging
Business Model Agent ROI calculator, pricing tier generator, tailored property manager pitch engine
Compliance Built In

Fair Housing compliance module that checks all copy for discriminatory language (flags phrases like "perfect for families", "no section 8", "students only")
Medium-appropriate EHO disclaimers appended to every communication (SMS, email, web, social)
Technical Stack

Backend: Python 3.12, FastAPI, Pydantic, Jinja2
Frontend: Responsive HTML dashboard with interactive tools
Deployment: Docker + Docker Compose — one command to run
CLI: Full terminal interface for testing each agent
Full Assets Included

35 files, 3,184 lines of Python/HTML 13 content templates (SMS, email, social, listing, inquiry form) Sales pitch deck, fair housing compliance bible, vacancy report template Sample property data (pre-loaded for demos) Dockerfile and docker-compose.yml Why Buy This Business?

Value Proposition

An AI-powered leasing follow-up system that automates the entire rental prospect pipeline — from inquiry intake to showing scheduling, follow-up nurturing, and listing copy — so property managers reduce vacancy time without hiring more staff. Compliance-aware (Fair Housing) by default.

Target Customer

Property managers, apartment operators, landlords, and leasing agents managing 50+ rental units who lose weeks of rentable time due to slow or inconsistent prospect follow-up.

Revenue Model

Monthly SaaS subscription: $500–$1,500/month per property management client.
$100K/year path: 12 property managers at ~$700/month = $100,800/year.

KPIs

Avg vacancy days (client metric — target: <15 days)
Inquiry-to-showing conversion rate
Showing-to-lease conversion rate
Follow-up response rate
Monthly active clients & MRR

Setup

Setup Instructions for the Buyer

Prerequisites

Python 3.12+ or Docker
A server (or just your laptop for demos)
Quick Start (5 minutes)

bash

Option 1: Docker (easiest)

docker-compose up --build

→ Opens at http://localhost:8000

Option 2: Local Python

pip install -r requirements.txt
python run.py run web

→ Opens at http://localhost:8000

Test the Agents

bash

List all 6 agents

python run.py list-agents

Test each one

python run.py run inquiry # Rental inquiry intake
python run.py run scheduler # Showing invitation
python run.py run followup # Follow-up messages
python run.py run listing # Listing copy
python run.py run campaign # Social + email campaign
python run.py run bizmodel # ROI calculator + pitch
Run the Sales Demo

Start the web app: python run.py run web
Open http://localhost:8000 — shows the agent dashboard
Click "📊 Calculate Your ROI" — plug in a PM's portfolio numbers
Click "📋 Try the Inquiry Flow" — shows instant response
Navigate to /listing — generate a listing in 3 different tones
Show /compliance/check — prove Fair Housing compliance
Deploy to Production

bash

On your production server:

git clone <your-repo-url>
cd vacancyfill-ai
docker-compose up --build -d
Then point a domain to it, add HTTPS (Caddy or Nginx Proxy Manager), and start selling.

Recommended Next Steps

Add Stripe/payment integration
Add SQLite/Postgres for multi-tenant data
Connect Twilio for SMS sending
Connect SendGrid/Mailgun for email
Set up a simple sales CRM
Files at a Glance

vacancyfill-ai/
├── agents/ # 6 AI agent modules (inquiry, scheduler, followup, listing, campaign, bizmodel)
├── web/app.py # FastAPI web application — 9 API endpoints
├── templates/ # 4 HTML pages + 9 content templates
├── fair_housing.py # Compliance checker — 12 discriminatory patterns
├── data/sample_data.py # Sample property, inquiry, and 3 vacant units
├── outputs/ # Sales pitch, compliance bible, vacancy report
├── config.py # Settings
├── run.py # CLI — test all agents, start web server
├── Dockerfile # Production container
└── docker-compose.yml # One-command deploy

VacancyFill AI

Overview

Mission

Value Proposition

Target Customer

Revenue Model

KPIs

The team

Setup

Option 1: Docker (easiest)

→ Opens at http://localhost:8000

Option 2: Local Python

→ Opens at http://localhost:8000

List all 6 agents

Test each one

On your production server: