Technology
Every technical choice on this page is made against one constraint: will it still work in dust, heat, vibration, mud and patchy connectivity, and can a local technician repair it in a day?
Architecture
Prototypes stay cheap and replaceable. Anything that survives a season graduates into the shared autonomy core, where the next machine gets it for free.
Compute
Raspberry Pi and microcontrollers for prototypes; Jetson-class for vision; offline-first by default
Robot software
ROS 2 with C++ and Python; simulation in Gazebo or Isaac Sim
Localization
GNSS with RTK where available, IMU, wheel odometry, visual cues
Perception
Camera-based detection and segmentation with ONNX or TensorRT on the edge
Control & safety
PID and path following; hardware e-stop, geofence, watchdog, remote kill, black-box logging
Connectivity
Offline-first; sync when connected; LoRa, 4G or satellite where useful
Fleet & data
FastAPI, PostgreSQL and PostGIS, object storage, OTA updates, telemetry dashboards
Hardware
Modular frames, standard connectors, swappable battery packs, sealed enclosures, findable spares
Robot testing ladder
No field release without a written test report. Every failure and near-miss is recorded, because the failure log is what makes the second robot cheaper than the first.
No motion. Power, sensors and wiring verified on the bench before anything moves.
Software runs, motors spin, logs are written — with the machine unable to travel.
First motion. Kill switch tested before every run. Every fault and near-miss recorded.
Real terrain and real crops, still contained, still with a spotter.
Agreed safe area, trained operator, kill switch, no people or animals in the test zone.
Unsupervised only in segments, with geofence and remote monitoring. Never without a written test report.
Simulation regression tests run on every software change. A change that breaks a simulated row pass never reaches a real field. The safety supervisor — e-stop, geofence, watchdog, remote kill — is written first, not bolted on afterwards.
Engineering roadmap
Building robots is how Ifenri delivers, so hands-on hardware skill matters from the start. The rule is simple: build the first version of every new skill by hand, then use AI agents to extend it.
Months 0–3
Learn
Python, Git, Linux, SQL, FastAPI; basic electronics, Arduino or ESP32, soldering, multimeter; Raspberry Pi
Build
The Assist intake agent and farm database; a small robot kit that drives, reads sensors and stops safely
Months 3–6
Learn
ROS 2 nodes, topics and services; Gazebo; camera, IMU and GNSS; motor control and PID; GIS; NDVI; computer-vision basics
Build
Simulated field robot; Rover R1 following a row; drone-image to crop-report pipeline
Months 6–12
Learn
Embedded C and C++; real-time constraints; GNSS with RTK; localization and basic SLAM; power systems and battery management; CAD; spraying fundamentals
Build
R1 field tests; a paid scouting run; maintenance tracker; offline pilot and operator app; first sensor node
Months 12–18
Learn
Mechanical design for dust, heat and vibration; 3D printing; fabrication and welding basics; safety engineering; fleet software; OTA updates
Build
Rugged R1 v2; solar charging dock; safety supervisor with hardware kill; small-batch test jigs
Months 18–24
Learn
Design for manufacture and assembly; supply chain and bill-of-materials management; testing and quality processes; reliability engineering; basic compliance
Build
Assembly guide; test checklists; 5 to 20 pre-series units; supplier scorecards
Years 3+
Learn
Hiring, code review, architecture, roadmaps; food-safety, quality-system and data-governance basics
Build
A team, and the systems that let it work without you in every loop
Ten projects
Every project below exists because the business needs it. None of them are exercises.
Farm job intake agent
Turning messages into jobs
Small wheeled robot with safe stop
Robotics foundation
NDVI crop-health map
Sight product
Row-following rover in simulation
Navigation core
Rover R1 in a real plot
First robot
Pest and weed detector on edge hardware
Scouting and spraying
Maintenance and battery-health tracker
Uptime and spares
Solar field sensor node
Node product
Fleet dashboard with remote kill and telemetry
Platform core
Rugged R1 v2 and a small production run
Manufacturing readiness
Data governance
Every drone flight, robot run, spray job, storage reading and sale creates data. Over years that becomes a rare dataset about food production in Africa. These rules decide what we are allowed to do with it.
No data product launches without a consent framework, a named partner and a written purpose.
Farmers give consent in plain language and local languages. Consent can be withdrawn.
Ifenri may use it for agreed purposes, and shares the benefit where the data creates value.
Anonymise and aggregate before any sharing with partners. Never sell personal data.
Secure storage, access control, audit logs and a breach plan.
A short data charter, reviewed yearly with farmers and partners.
Nigerian data protection law, and any rules for health-related data, confirmed with a lawyer.
Start the conversation
Tell us your crop, your hectares and where you lose money today. We will tell you honestly whether Ifenri can improve that hectare yet — and if we cannot, we will say so.
Book mapping, scouting or machinery for your cluster. Pilots are paid and measured from the first hectare.
Book a field serviceTalk to us about reliable, traceable supply and where the loss actually happens in your crop.
Discuss supplyRead the working plan, the gates, and the unit economics before you commit a naira.
Investor brief
