I build the spatial systems that
map reality for institutions.

I grew up in Zimbabwe, studied Geoinformatics at the University of Zimbabwe, and got obsessed with a simple idea: almost every problem — land rights, flood mapping, infrastructure planning, mortgage lending — has a geography to it. If you can see it spatially, you can solve it faster.

That obsession turned into a career building the GIS systems that Zimbabwe's government and financial institutions now run on. The eCadastre system that digitized national land records. The mortgage platform that lets CBZ evaluate properties spatially. The land information system that replaced paper-based ministerial approval chains. These aren't prototypes — they're live, national-scale production systems.

Why Drones

Drones close the gap between satellite imagery and ground truth. In places like Zimbabwe where addresses are informal and land records are paper-based, a drone can capture reality in hours. That fascinates me. I'm actively building drone-to-GIS pipelines — from raw imagery to point clouds to queryable PostGIS layers — and working toward my CAAZ piloting certification.

Why AI

Manual digitization is slow. Pattern recognition in spatial data — identifying illegal mining, detecting crop stress, classifying land use — is where machine learning changes everything. The next leap in GIS isn't about better maps. It's about maps that think — detecting patterns, classifying land use, flagging anomalies without a human reviewing every pixel. I'm building toward that.