WaterCopilot: An AI-Driven Virtual Assistant for Water Management

TL;DR

WaterCopilot is an AI-powered virtual assistant designed to improve water management in transboundary basins by integrating diverse data sources, providing real-time insights, and supporting decision-making through an interactive, multilingual platform.

Contribution

The paper introduces WaterCopilot, a novel AI-driven platform that combines retrieval-augmented generation and custom plugins to unify static and dynamic water data for improved governance.

Findings

Achieved high answer relevancy and context precision scores.

Enabled automated alerts and integration with digital twin technology.

Demonstrated potential to enhance water governance in data-scarce regions.

Abstract

Sustainable water resource management in transboundary river basins is challenged by fragmented data, limited real-time access, and the complexity of integrating diverse information sources. This paper presents WaterCopilot-an AI-driven virtual assistant developed through collaboration between the International Water Management Institute (IWMI) and Microsoft Research for the Limpopo River Basin (LRB) to bridge these gaps through a unified, interactive platform. Built on Retrieval-Augmented Generation (RAG) and tool-calling architectures, WaterCopilot integrates static policy documents and real-time hydrological data via two custom plugins: the iwmi-doc-plugin, which enables semantic search over indexed documents using Azure AI Search, and the iwmi-api-plugin, which queries live databases to deliver dynamic insights such as environmental-flow alerts, rainfall trends, reservoir levels, water accounting, and irrigation data. The system features guided multilingual interactions (English, Portuguese, French), transparent source referencing, automated calculations, and visualization capabilities. Evaluated using the RAGAS framework, WaterCopilot achieves an overall score of 0.8043, with high answer relevancy (0.8571) and context precision (0.8009). Key innovations include automated threshold-based alerts, integration with the LRB Digital Twin, and a scalable deployment pipeline hosted on AWS. While limitations in processing non-English technical documents and API latency remain, WaterCopilot establishes a replicable AI-augmented framework for enhancing water governance in data-scarce, transboundary contexts. The study demonstrates the potential of this AI assistant to support informed, timely decision-making and strengthen water security in complex river basins.