Scaling Worker Protection Through GeoAI and Conversational AI: A Case Study from India's Brick Manufacturing Sector

India's brick manufacturing industry presents a compelling use case for applied AI at scale. As the world's second-largest brick producer, the country generates 240–260 billion bricks annually through a predominantly manual, seasonal production system employing approximately 10 million workers. The technical challenge? Complete lack of visibility into the operational landscape and workforce conditions across thousands of dispersed production sites.

The Mapping Problem

Government and NGO stakeholders faced a fundamental data infrastructure gap: no authoritative registry of brick kiln locations or operational counts existed. Without baseline geospatial data, coordinating regulatory compliance checks or labour rights interventions was essentially impossible. Traditional ground surveys were cost-prohibitive and couldn't maintain currency given the industry's seasonal dynamics (kilns typically operate October/January through June).

GeoAI Platform Architecture

The Rights Lab and UNDP co-developed a geospatial intelligence solution leveraging satellite Earth observation data to create a comprehensive, updateable kiln mapping layer for India. The GeoAI platform processes this foundational dataset through a dual-purpose compliance engine:

Environmental compliance module: Monitors operational status against emissions regulations

Labor conditions module: Drives inspection targeting and supports anti-trafficking interventions

The platform's backend ingests field data from inspections, creating a feedback loop that continuously refines the detection model and improves targeting algorithms. This virtuous cycle enables unprecedented systematic oversight of an industry previously operating largely in the shadows.

Technical Impact

The geospatial layer fundamentally changed the operational calculus for both state agencies conducting inspections and NGOs executing worker liberation and education programs. By reducing the search space and providing actionable intelligence on kiln locations and operational windows, the platform makes previously infeasible intervention campaigns viable at scale.

Bottom-Up Worker Empowerment Layer

User research revealed significant mobile device penetration among the brick kiln workforce, despite precarious employment conditions and limited formal education. This finding prompted development of a complementary bottom-up intervention: LabourShield, a conversational AI chatbot designed for direct worker engagement.

LabourShield Chatbot

The chatbot addresses a critical gap identified in human rights technology frameworks—the lack of bidirectional communication tools that actively empower vulnerable populations rather than simply monitoring them. LabourShield functions as an always-available advisory system, providing workers with:

Rights awareness information tailored to their employment context

Guidance on identifying exploitative conditions

Pathways to assistance and intervention resources

Pilot Deployment and Scaling Considerations

Initial deployment focused on brick kiln workers in India, with evaluation metrics examining both direct impact and technical feasibility for horizontal scaling. The project assessed adaptation requirements for deployment in alternative contexts (specifically Malaysia) and different labor sectors.

Sustainability Engineering

A key technical deliverable involves architecting for long-term operational sustainability beyond the pilot funding cycle. This requires solving for:

Low-cost hosting and maintenance infrastructure

Content update workflows that don't require continuous developer involvement

Localization pipelines for multi-language, multi-context deployment

Integration patterns with existing NGO and government systems

This dual-layer approach—top-down GeoAI surveillance combined with bottom-up conversational AI empowerment—demonstrates how modern AI capabilities can address systemic labour rights challenges when properly architected around real user needs and operational constraints.