This company provides a cloud-native platform that integrates satellite, climate, and in situ data with AI and modeling for water and land management. The platform transforms environmental data into predictive intelligence, enabling users to forecast groundwater recharge, monitor land surface dynamics, and simulate complex water systems. This digital twin approach supports optimized resource allocation, risk mitigation, and science-based ESG reporting for various sectors.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations managing water resources often rely on fragmented datasets—satellite imagery, climate records, and field measurements—that are stored in disparate systems and require manual processing. This limits the ability to generate timely, predictive insights for groundwater recharge, surface water dynamics, and land‑surface processes, hindering operational decisions and ESG reporting.
Solution
HydroGeoTwin delivers a cloud‑native platform that ingests satellite, climate, and in‑situ observations, applies AI‑enhanced hydrological models, and produces a digital twin of water and land systems. The service provides historical, current, and forecasted estimates of groundwater storage, surface water balance, and land‑surface conditions through an interactive web dashboard. Scenario analysis tools enable users to evaluate the impact of climate variability, extraction policies, or land‑use changes on water availability. All results are stored in a secure, version‑controlled data lake and can be exported via APIs for integration with enterprise workflows or ESG reporting pipelines. The modular architecture scales from data‑scarce regions with satellite‑only inputs to fully integrated models that combine remote sensing, IoT sensors, and climate projections.
Target Audience
Primary customers include public water utilities, mining and energy operators, agribusiness firms, and ESG/compliance teams that require real‑time water intelligence and traceable sustainability metrics.
Features
- Automated ingestion pipeline for multi‑source Earth observation data (NASA, ESA) and real‑time IoT sensor streams
- AI‑driven estimation of groundwater recharge, storage, and surface water fluxes using physics‑informed machine‑learning models
- Land‑surface analytics covering vegetation index, soil moisture, subsidence, and erosion risk assessment
- Interactive dashboards with geospatial visualizations, time‑series charts, and anomaly detection alerts
- Scenario simulation engine for “what‑if” analyses of climate scenarios, extraction rates, and land‑use changes
- Modular analysis levels allowing progressive refinement from satellite‑only estimates to fully integrated digital twins
- FHIR‑compatible and RESTful APIs for data export, integration with GIS, ERP, or ESG reporting systems
- Enterprise‑grade security with role‑based access control, encryption at rest and in transit, and audit‑ready data lineage