Building intelligence for the world's hardest problems.
Five research sectors. Agriculture, autonomous vehicles, mining, geo mapping, and internet satellites. The lab works on problems where AI can change the operational baseline — not just automate the obvious.
Active Scan — 5 sectors
Active Research Sectors — 05
Five domains. One mission. Build what doesn't exist yet.
Agriculture
AI systems that read the land. Satellite crop stress analysis, disease detection, yield forecasting, and field-level decision intelligence built for African conditions.
Research directions
- →Multi-spectral satellite crop stress analysis
- →Yield prediction from historical field and climate data
- →Pest and disease early-warning detection
- →Irrigation optimisation from weather and soil models
Autonomous Vehicles
Fleet intelligence and adaptive route AI for mixed-infrastructure environments. Built for roads that don't behave the way textbooks expect.
Research directions
- →Road condition classification from onboard camera feeds
- →Predictive fleet maintenance via sensor telemetry
- →Dynamic route optimisation for rural logistics
- →Driver behaviour scoring and safety AI
Mining
Deep-ground intelligence. AI that surfaces exceptions, optimises extraction paths, and keeps human operators in control of high-risk decisions.
Research directions
- →Ore body detection from seismic and drill data
- →Autonomous drill path optimisation
- →Real-time safety anomaly detection
- →Equipment downtime prediction from operational logs
Geo Mapping
AI that reads the earth at scale. Topographic change detection, land-use classification, and terrain modelling for infrastructure, agriculture, and resource planning.
Research directions
- →Terrain change detection from satellite time series
- →Land-use and vegetation classification at scale
- →Infrastructure site suitability modelling
- →Flood risk and erosion prediction from DEM data
Internet Satellites
Orbital intelligence for connectivity. AI that predicts signal coverage, optimises ground station routing, and maps last-mile access gaps for underserved regions.
Research directions
- →Coverage prediction from orbital mechanics and terrain models
- →Ground station signal routing optimisation
- →Last-mile connectivity gap mapping
- →Interference and weather impact forecasting
Research Infrastructure
The stack behind the research.
Every sector runs on a combination of domain-specific data pipelines, modern ML tooling, and lightweight inference runtimes built for low-connectivity environments.
AI / ML
Data & Imagery
Infrastructure
From Question to Product
How lab ideas move into operations.
Operational question
Research starts with the task, delay, or decision that needs improvement. The lab begins with workflow pressure, not model novelty.
Validation
Ideas are tested against real constraints, available data, and live tools to see whether AI can improve speed, quality, or visibility.
Pilot path
Promising experiments move into a narrow pilot with approval points, measurable outcomes, and limited operational risk.
Delivery route
From there, the work becomes a product capability, a service engagement, or a longer research track depending on fit.