Field teams were collecting animal-level data that genuinely mattered, but it arrived inconsistently and late, so strategy got decided on partial context. Whatever we built had to work in low-connectivity conditions without adding a reporting burden to people already busy. We built the capture, planning, and analytics layer that made the data usable at both ends.
Field teams collected valuable animal-level data, but records were inconsistent and often delayed, so strategic decisions were made with partial context. The organisation needed reliable operational visibility across low-connectivity environments without adding reporting overhead.
First a field data model and decision framework that survives low connectivity — the hard constraint everything else had to work inside, not a detail to solve later.
Then mobile-first workflows for event capture, cycle planning, and reminders, syncing into centralised analytics whenever a connection appears rather than requiring one.
Then operating playbooks and review cadences, so regional teams hold data quality over time instead of drifting back to their own formats.