Smart Real Estate Case Studies
See how AI streamlines real estate operations and reduces manual work.
Select Your Case Study
Case Study #01
AI-Powered Listing Agents: Extract and standardize property data from portals, feeds, and emails automatically.
Use-Case Description
Real estate teams manage hundreds of listings across portals, each with different formats and update rules.
Price, size, and location must be manually entered into CRMs or systems, leading to errors, duplicates, and delays.
Problem
❌ Listing formats vary, making comparison and consolidation difficult
❌ Teams spend time manually copying data between systems
❌ Language, currency, and platform differences cause confusion
AI Handles Following
🧩 Extracts property information (price, size, location, amenities) from listings
🧩 Identifies offer type (rent/sale), language
🧩 Syncs standardized property data with CRMs, PMS, and business systems
🧩 Alerts teams to price changes, duplicates, or incomplete records
The Results
✅ 70% less time spent on data tasks
✅ 2× faster updates and pricing reaction
✅ 98% accuracy after training
✅ Higher listing quality across all channels and platforms
Case Study #02
AI Lease Monitor: Extract key terms from rental contracts and get proactive alerts on renewals, expiries, and compliance gaps across your portfolio.
Contract Parsing, Renewal Alerts, Compliance Tracking
Use-Case Description
Leasing teams manually reviewed rental agreements to identify key dates and terms.
There was no centralized tracking, and upcoming renewals or compliance risks were often missed across portfolios.
Problem
❌ Managers spent hours extracting lease info from PDFs
❌ Missed renewals led to tenant churn and legal issues
❌ No centralized view of lease terms across assets
AI Handles Following
🌿 Automatically extracts key data from contracts in any format
🌿 Flags missing information or inconsistencies in lease terms
🌿 Sends alerts 30/60/90 days ahead of key dates or expirations
🌿 Integrates with existing property or lease management tools
🌿 Matches extracted data against internal records to ensure accuracy and compliance
The Results
✅ Gained full visibility into lease terms and timelines
✅ Prevented 12 missed renewals in just 3 months
✅ Reduced manual contract review time by 80%
✅ Improved data accuracy across lease records by 90%
Case Study #03
AI Maintenance Coordination: Understand tenant emails and auto-dispatches the right technician with zero manual sorting or delays.
Property Management, Complaint Recognition, Automated Dispatch
Use-Case Description
Tenants send emails to report maintenance issues like leaks, broken appliances, or heating failures.
Property managers read messages manually, determine the issue, and contact the appropriate technician or vendor.
Problem
❌ Tenants use unstructured language in emails
❌ Property managers manually interpret and forward each request
❌ Some complaints get delayed or overlooked
❌ Slow response increases frustration and liability
AI Handles Following
🧩 Reads and understands tenant emails in natural language
🧩 Identifies the issue type using intent classification
🧩 Matches issue to the right technician or vendor
🧩 Sends task summary with contact info and issue details
🧩 Alerts manager if no action is taken within 4 hours
The Results
✅ No tenant emails missed or ignored
✅ 10× faster routing to correct technician or vendor
✅ 87% reduction in manual message handling
