Building the operating system for African agriculture
A field-tested robot, a shipped app, and a clear path to scale.
Why now
Sub-Saharan Africa's agricultural sector accounts for 15–20% of GDP and employs over half the workforce, yet remains almost entirely non-digital. Global precision-agriculture platforms weren't built for 2–20 hectare farms, patchy connectivity, or mobile-money billing — which leaves the fastest-growing segment of world agriculture essentially unserved.
Traction
- AgriBot's ground robot is built and field-tested, not a concept render
- A production Android app is already shipped, with seven working feature tabs
- Live cloud services in production today: Supabase, HiveMQ Cloud MQTT/TLS
- A rule-based agronomic AI engine and computer-vision camera control are already live
- Two pilot farms (Centre and West regions) are scheduled within the current roadmap
Our moat
- Offline-first engineering, not a bolt-on feature — the platform works with zero connectivity
- Local build and service capability, versus imported hardware with no local support
- Mobile-money billing (MTN MoMo / Orange Money) fitted to how Cameroonian farms actually pay
- A shared-asset model — one robot and drone serving many farms through a cooperative — that lowers entry cost in a way imported products can't match
Roadmap snapshot
M0–M3
Fixed Sensor Grid
M3–M6
Drone Integration
M6–M9
Closed-Loop Automation
M9–M12
Productization
Team
Software engineering graduate of ICT University, Yaoundé, and founder of the ICTU Robotics Society. Built AgriBot from a final-year capstone into a field-tested robotics platform — spanning embedded systems, full-stack and mobile development, and DevOps.
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Financial projections, unit economics, and our funding ask are shared under NDA on request.
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