AquaSight deploys a network of IoT sensors across water distribution systems, which continuously collect data on key water quality parameters like pH, turbidity, and contaminant levels.

This data is transmitted in real-time to a cloud-based platform where AI algorithms analyze it to detect anomalies, predict future trends, and generate compliance reports.

Utilities access these insights through a customizable dashboard, which offers visual analytics and alerts.

The system integrates with existing infrastructure, using machine learning to adapt its protocols as new contaminants and regulatory changes arise.

AquaSight enables utilities to achieve reliable, real-time monitoring without costly infrastructure overhauls, offering scalable and adaptive technology at a fraction of the competitive cost.

The platform improves safety with real-time anomaly detection and ensures compliance with predictive analytics, while its flexible integration reduces transition barriers.

The feasibility relies on existing IoT technologies and AI-driven analytics, which have matured significantly.

Costs are mitigated by leveraging cloud computing and modular IoT devices, ensuring scalability.

Initial high-growth regions would be OECD countries with stringent regulations.

Competitively, the field is cluttered yet offers differentiation potential through AI adaptability and flexible integrations.