We build machine learning and deep learning models on satellite, sensor and GIS data. They forecast hazards, score risk and answer questions in plain language, and they run in the cloud or on your own servers.
Imagery: Sentinel-2 cloudless 2023 by EOX IT Services, modified Copernicus Sentinel data (CC BY 4.0). Terrain: SRTM via AWS Terrain Tiles. Roads and buildings: © OpenStreetMap contributors (ODbL).
30+
projects delivered across climate, GIS, AI and ESG
3
multi-hazard risk assessments, including AI driven risk modeling for Astore
17
alliances and university partners, AI for Good by ITU among them
5.0
rating on Google reviews
Hire a full service for forecasting, risk analytics or deployment. Image classification, plain language queries and early warning can be built into any of them.
Custom models that predict floods, drought and climate hazards, trained on satellite and sensor data and tested before use.
Risk scores, rankings and dashboards that tell leaders where losses are most likely and which actions come first.
AI and GIS systems set up inside infrastructure you control, for teams whose data cannot sit on a shared cloud.
Deep learning that picks out water, buildings, crops and land cover in satellite scenes, without tracing each feature by hand.
AI Geo GPT, our Geo-LLM, lets your team ask spatial data questions in plain language instead of writing queries.
Models that watch rainfall, river levels and hazard layers, then flag rising risk early so teams have time to prepare.
AI earns its place when a decision is urgent, the area is large or the data is too much for people to read alone.
Models that read rainfall, river and glacier signals together and raise a flag before a hazard reaches people.
Image classification that tracks forest, cropland and built up area across whole regions, year after year.
Satellite AI that maps new buildings and roads as a city spreads, keeping planning maps current between surveys.
Reports, maps and sensor logs made searchable, so staff ask a plain question instead of hunting through folders.
AI projects fail when the data is weak or the users are forgotten. Our five stages check both before any model goes live.
We agree the decision the model must support and how we will judge success.
Problem statement and metrics
We check what you hold, fill gaps from satellite and open sources, and flag weak spots.
Data audit and gap list
Models trained, compared and tested on data they have never seen.
Tested model and scores
The model goes into a dashboard, app or your own systems, in the cloud or on-premise.
Working tool for your team
Your staff learn to read, question and rerun the model, and we stay on hand.
Training and model notes
Describe the decision, your data and the area. We will tell you whether AI is the right tool and what it would take.
