Connected Things provides a hardware-agnostic platform that serves as the intelligence backbone for modern water management systems. This platform delivers usable insights across the entire water cycle, including drinking water, wastewater, and industrial applications. The service enables water managers to achieve greater efficiency, automation, and resilience compared to legacy infrastructure.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional Geographic Information Systems (GIS) workflows often struggle to integrate and analyze diverse datasets, such as socioeconomic systems, weather patterns, and environmental factors, hindering comprehensive understanding and accurate forecasting. The complexity of linking and interpreting location-based data from disparate sources poses a significant challenge for effective geospatial analysis.
Solution
Connected Things offers geoGPT, a geospatial machine learning platform that creates a geographically enriched graph representation of the Earth. This platform seamlessly connects and analyzes diverse datasets through a geographic lens, emphasizing socioeconomic systems, people, places, companies, weather, and the environment. By linking these datasets, geoGPT facilitates the creation of advanced models, enables deeper analysis, and empowers forecasting in both traditional and non-traditional GIS workflows. The platform's geo-forward approach creates highly connected, geographically rich graph networks, providing a fact-based AI representation of the Earth and human activity.
Target Audience
The primary audience includes GIS professionals, data scientists, and organizations requiring advanced geospatial analysis and forecasting capabilities.
Features
- Geographically enriched graph representation of the Earth
- Seamless integration and analysis of diverse datasets (socioeconomic, weather, environmental)
- Advanced modeling capabilities for geospatial data
- Forecasting tools for traditional and non-traditional GIS workflows
- Highly connected, geographically rich graph networks