From a problem we faced directly to a live national platform
The problem, first-hand
After repeatedly seeing food sit as surplus in one state while it was scarce and costly in another, the team set out to close the price-information gap in Nigeria's inter-state agricultural trade — treating it as a real market failure to solve, not a theory to study.
First working prototype
Prophet forecasting model and PuLP optimizer built in Python. Initial Streamlit dashboards deployed on cloud infrastructure. PostgreSQL database schema designed and live on Supabase.
Field operations begin
First field agents onboarded across Oyo, Kwara, and Kogi. KoboToolbox submission pipeline live. Weekly price data flowing into the system for the first time.
Scale to 19 agents across 8 states
Agent network expanded across the full north-south corridor, with weekly price submissions flowing in from across our 8 target states. GitHub Actions scheduler automates the full pipeline daily at 06:00 WAT. 32 Prophet models trained weekly across all state-commodity pairs.
Primary research + platform rebuild
Consumer demand survey completed across 16 Nigerian states, confirming the distribution paradox. Platform rebuilt from Streamlit to Next.js + FastAPI production stack. Team of 10 now active.
Phase 2 — Revenue · Phase 3 — Consumer Layer
First paying subscribers, brokerage commissions, and self-trading on system recommendations. Phase 3 adds a public-facing consumer intelligence feed — powered by the same agent data, delivered via web and WhatsApp.