Agricare AI.
AI crop-health diagnostics for smallholder farmers via mobile image capture.
Agricare AI diagnoses crop health from a phone photo. The hard part was never the model — it was making it usable for a field agent on a 3G connection standing in a field, not a researcher at a desk.
The challenge
Agricare AI needed a platform that scaled from launch day through enterprise contracts — without a rewrite at every inflection point. Early bets on architecture and observability had to hold.
Our approach
We wrapped their model in an offline-first mobile app: capture and queue diagnoses with no signal, sync when a connection returns. The on-device path stayed simple and the heavy inference ran server-side, with graceful handling when the network drops mid-upload. The offline flow took longer than we quoted — it's also the part farmers depend on, so we ate the overrun and got it right.
What we built
A production-grade stack designed around Flutter + FastAPI, wired with CI/CD, automated tests, and cloud-native infra. Domain-specific flows were co-designed with stakeholders each sprint.
Impact
94% detection accuracy across 12 major crops.
They're not an AI lab and didn't pretend to be. What they were good at was wiring our model into something a field agent can actually use on a patchy 3G connection. The offline capture flow took longer than planned, but it's the part our users live in.
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