Operational:
Eliminated manual document processing
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NDA project
ML platform converting unstructured property documents into structured investment signals using automated OCR
Real Estate
USA
Web
Dedicated
Development Team
In the U.S. real estate market, crucial investment data — foreclosure notices, REOs, tax liens, NODs — are often buried inside scanned images and government-issued PDFs.
Manually parsing these documents is slow, labor-intensive, and leads to missed opportunities.
Unstructured Data Sources: PDFs and scanned images
Manual Processing Bottlenecks: Slow data extraction
Opportunity Detection Speed: Rapid market signal identification required
Data Quality Issues: Poor document quality hindered OCR accuracy
We developed an intelligent document processing pipeline that automates ingestion, OCR, ML-driven extraction, and signal generation, converting raw property documents into structured investment intelligence.
2x
Python Developers
1x
Project Manager
2x
Data Engineers
1x
DevOps Engineer
1x
QA Engineer
2x
Python Developers
1x
Project Manager
2x
Data Engineers
1x
DevOps Engineer
1x
QA Engineer
Automated document ingestion system
Web-based investor interface
OCR & image recognition pipeline
ML-powered opportunity detection
Structured text extraction engine
Full breakdown is available under NDA
Discovery & Planning: 3–4 weeks
Architecture & Design: 4–6 weeks
Development: 4–6 months
Total Timeline: ~6–8 months
Discovery Phase: $25K–$40K
Design & Architecture: $40K–$70K
Development: $180K–$320K
Total Investment: $300K–$450K
Share a few details — we’ll map scope, timeline, and cost
Eliminated manual document processing
Scalable document intelligence infrastructure
Accelerated real estate opportunity discovery