Research Data Platform

Mission & Constraints

1. 01.

   The starting condition was a research team losing seven days to every data cycle. The design began with user research rather than architecture — what researchers actually did with field data, and where the days were going.

2. 02.

   The resulting system became the standard for field data across research teams, which is the part that mattered: an internal tool nobody adopts has no measurement to report.

3. 03.

   Later work moved down the stack — Jenkins pipelines and dual environments, Jest and CI groundwork, Git hooks across departments — and then into the AI servers themselves, where batch processing took out half the CPU and most of the cost.