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:

Rent data → arrears detection → priority → follow-up message → escalation → resolution

What it does:

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.

E-commerce Revenue Recovery & Retention Automation System

I designed an e-commerce revenue recovery and retention system to show how online stores can recover abandoned sales and bring more customers back to purchase again.

The system automates key customer journeys including abandoned-cart recovery, post-purchase follow-up, win-back campaigns, repeat-purchase reminders, and customer segmentation.

Instead of relying on manual follow-ups, the workflow uses customer behavior and purchase data to trigger the right action at the right time.

The system follows a simple journey:

Customer Visits → Adds to Cart → Abandons → Automated Follow-up → Purchase → Retention Workflow → Repeat Purchase

I also structured the system to track how customers move through each stage, so the business can see where revenue is being recovered and where customers are dropping off.

The project demonstrates how automation can help an e-commerce brand:

Recover lost sales • Increase repeat purchases • Improve retention • Reduce manual follow-up • Increase customer lifetime value

Tools & technologies: Shopify, Klaviyo/Zoho Marketing Automation, n8n, Make, Zapier, CRM systems, email/SMS automation, and customer-data workflows.

What caught my attention was the stage by stage tracking you built, it should pinpoint exactly where the drop offs happen and where the recovered revenue comes from