SNG Case Study: Digitization of Property Tax Collection
The Excise, Taxation, and Narcotics Control (ET&NC) Department of Punjab, Pakistan, responsible for property tax collection. Despite property taxes contributing 28% of departmental revenue, collections only accounted for 6% of the province’s total tax revenue due to manual detection, inefficiencies, and limited public engagement.
Outcome
The digitization initiative delivered measurable impact across property tax operations. From smarter detection to faster processes, the outcomes highlight improved efficiency, transparency, and revenue gains
Automated detection reduced underestimation and boosted revenue collection.
Enhanced Operational Efficiency
Field staff captured accurate data faster, with minimal manual errors.
Greater Transparency
Supervisors accessed real-time reports and performance tracking dashboards.
Streamlined Tax Collection
Automated assignments and workflows cut down processing time and improved accuracy.
ROLE
Designed and implemented AI-driven property detection, mobile field app, and real-time reporting system
TOOL
Detectron 2, Mapbox, Custom IOU, Python, ML models, Mobile App (Android), Geo-location APIs, Web Dashboard, Analytics
DURATION
Multi-phase engagement, initial deployment within 6 months with continuous updates every 6 months
How We Did It
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AI-Powered Property Detection
Trained a Detectron 2 instance segmentation model on Mapbox images with custom IOU logic to identify residential, commercial, and combined properties.
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Mobile Field Application
Built a geo-enabled mobile app for field staff to capture property data, images, and locations in real time, reducing redundancy and errors.
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Workflow Automation
Developed an ML-powered task assignment system that automatically allocates detected properties to Excise Tax Officers (ETIs) based on geography and property type.
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Real-Time Reporting & Monitoring
Integrated a web-based dashboard providing analytical reports, property detection insights, and ETI performance tracking for better decision-making and accountability.
What We Did
Red Buffer collaborated with SNG to build a robust AI-driven solution that digitized the property tax collection process. By combining satellite-based computer vision with a mobile field app and real-time dashboards, the system modernized workflows, reduced manual inefficiencies, and created transparency. The result was a scalable solution that significantly increased property detection and improved revenue collection for the ET&NC Department.
AI-Powered Property Inspection MVP
A cloud-native MVP integrating GPT-4 and computer vision workflows to analyze inspection videos, extract property condition details, and generate structured inspection notes.
Automated Cost Estimation & Reporting System
A reporting engine that predicts repair costs, compiles asset information, and produces standardized inspection reports with narrative-style summaries.
Secure, Scalable Delivery Platform
A Next.js + FastAPI-based dashboard with RBAC access controls, integrated with SendGrid for secure report distribution, and designed for subscription-based scaling.
”The digitization of property tax collection has been a game-changer for our department. Automated property detection and real-time monitoring have not only increased our revenue but also made our operations more transparent and efficient. This has laid the foundation for a modern, scalable tax system. service
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