Technology
From Earth Observation to Grid Operations.
Grid Sentinel integrates satellite intelligence, climate science, geospatial analysis, and AI to transform environmental complexity into operational priorities for electric distribution networks.
EARTH
Satellite · Climate · Vegetation · Wind · Fire
GRIDSENTINEL
AI Risk Intelligence
GRID
Inspection · Maintenance · Operations · Reliability
Intelligence Layers
Six layers of intelligence, one operational picture.
01
Earth Observation
Satellite imagery provides a continuous view of territorial conditions around distribution infrastructure — enabling analysis at scales impossible with manual inspection.
CAPABILITIES
Multispectral satellite imagery
Vegetation index analysis (NDVI)
Land cover classification
Change detection over time
Sentinel-2 / Planet / commercial satellite integration
DATA SOURCES / PARTNERS
Sentinel-2PlanetCopernicusUSGS
Data Fusion
Multiple sources. One operational picture.
Grid Sentinel fuses eight distinct data streams into a single, unified risk intelligence layer for your distribution network.
Satellite
Weather
Wind
Vegetation
Wildfire
Terrain
Network
Historical Events
Grid Sentinel
Intelligence Engine
Risk Map
Maintenance Priority
Inspection Plan
Reliability Intelligence
AI Risk Engine
From environmental signals to operational priorities.
Grid Sentinel uses AI to integrate environmental variables, model their interactions, and explain which factors determine risk in each segment of the network.
INPUTS
Multi-source environmental & network data
HAZARD MODELS
Vegetation · Wind · Wildfire analysis
AI ANALYSIS
Multi-variable risk integration
RISK SCORE
Segment-level composite risk value
MAINTENANCE PRIORITY
Ordered operational action plan
MODEL INPUTS
Satellite observations
Weather data
Vegetation information
Wind models
Wildfire information
Terrain
Seasonality
Network geometry
Historical events
Maintenance information
AI PRINCIPLES IN GRID SENTINEL
Explainable: Each risk score is explainable and traceable: Grid Sentinel shows what factors drive the result and why.
Multi-variable: Models integrate environmental, spatial, temporal, and network signals simultaneously.
Segment-level: Risk is computed for each individual network segment, not aggregated zones.
Operational: The model's outputs translate into an operational prioritization of maintenance, focusing on where to intervene first.