AI Rent & Arrears Follow-Up Automation: Property Management Dashboard
A working demo of an arrears management dashboard for property managers. It detects overdue tenants, ranks who to follow up first, and drafts safe, factual tenant messages from stored data.
Chasing late rent is slow and inconsistent. Managers often don't know whom to contact first, and messages are written from scratch each time. I built a demo system that turns rent and payment data into a clear daily workflow:
Dashboard: total rent due, collected, outstanding, overdue tenants, high-risk accounts and follow-ups due today.
Arrears queue: every overdue tenant with property, amount, days overdue, priority and a suggested action.
AI Arrears Prioritization: automatically ranks who the manager should contact first, based on days overdue, amount owed and past payment behaviour.
Account analysis: shows the amount outstanding, days overdue, payment history, communication log and a recommended next action for any tenant.
Tenant message generation: drafts a polite reminder, follow-up or escalation message matched to the account's stage.
Manual stage control: the manager moves each account through Reminder, Follow-up, Escalation and Resolved.
Tech stack: Python, Flask, SQLite, HTML, CSS, JavaScript, with optional local AI through Ollama and a rule-based fallback. It needs no paid APIs and runs fully offline.
Demo data: 5 properties, 20 units and tenants, six months of payment history and prior follow-up records, so the dashboard looks like a live system.
Honest framing: I'd label it a "working demo" or "prototype" rather than a production system. It uses seeded sample data and has no real SMS or email sending or login yet. Those would be the natural next steps for a client.
What if every new lead could be handled before you even open your inbox? ⚡
This AI-powered workflow turns a new email inquiry into a structured, ready-to-handle lead — automatically.
A new inquiry arrives → AI extracts the important details → Airtable/CRM is updated → the response goes through optional human approval → a personalized email is sent → Notion is updated → the team gets notified in Slack → performance data is collected for reporting.
The goal isn't to remove people from the process. It's to remove the repetitive work around them.
This type of workflow can be customized for B2B companies, SaaS businesses, agencies, real estate, e-commerce, recruitment, consulting, customer support, healthcare, finance, education, and other service businesses.
Already know what you want to automate? → Send me your current workflow and I’ll map out how we can automate it.
Still doing repetitive work manually? → Tell me the task that consumes your team’s time, and I’ll help identify what can be automated.
I'd use the human approval step to capture corrections, not just a yes/no decision. If a reviewer changes the extracted lead details, does the workflow update Airtable and the reporting record before sending the email?
AI Property Maintenance Automation
AI-powered property maintenance automation designed to streamline how property management teams handle tenant maintenance requests.
The system takes a maintenance request, analyzes the issue, determines its priority and category, recommends a suitable vendor, and automatically creates a structured work order.
Workflow:
Tenant request → AI analysis → Priority & category → Vendor matching → Work order
Built with: Python, Flask, SQLite, HTML, CSS, JavaScript, and AI-assisted request classification.
This project was built as a portfolio demonstration of AI automation for property management operations.