1. Universal ingestion
Satellite, AIS, weather, IoT, vehicle, GIS, and external stream connectors with format normalization.
ARRPSAT / ThotAI
ThotAI is a foundation architecture based on spatio-temporal vectorization. It integrates available geospatial data to deliver predictions, classifications, and decision recommendations in complex environments.
Data overview
Core
ThotAI
Spatio-temporal vectorization, multimodal fusion, and continuous learning for operational decisions.
Satellite imagery
Optical, SAR, multispectral, hyperspectral
AIS / Maritime
Vessel tracks, behavior, anomalies
Weather
Wind, rain, pressure, extreme events
Field IoT
Fixed, mobile sensors, in-situ stations
Vehicles
Ground fleets, logistics, telemetry
Agriculture
Parcels, stress, yield, soils
Socio-economic data
Activity flows, density, areas of interest
GIS and maps
Vector layers, raster, topology
Open-source intelligence
Open sources, contextual signals
Satellite imagery
Optical, SAR, multispectral, hyperspectral
AIS / Maritime
Vessel tracks, behavior, anomalies
Weather
Wind, rain, pressure, extreme events
Field IoT
Fixed, mobile sensors, in-situ stations
Vehicles
Ground fleets, logistics, telemetry
Agriculture
Parcels, stress, yield, soils
Socio-economic data
Activity flows, density, areas of interest
GIS and maps
Vector layers, raster, topology
Open-source intelligence
Open sources, contextual signals
Platform architecture
Satellite, AIS, weather, IoT, vehicle, GIS, and external stream connectors with format normalization.
Observations projected into a unified vector space indexed by position, scale, time, and context.
Domain-trainable multimodal backbone with transfer learning, few-shot, and continuous adaptation.
Prediction, classification, risk scoring, alert prioritization, and business indicator production.
Key capabilities
Business outputs
Next step
We build a roadmap adapted to your data, field constraints, and prediction or classification goals.