Agricultural biotech
Turning large volumes of experimental and sensor data into models that guide real crop-treatment decisions.
An agricultural biotechnology company was running controlled-environment and field experiments, generating imaging, sensor, and trial data faster than its scientists could analyse it.
The data was spread across instruments, spreadsheets, and storage systems with inconsistent formats. Scientists needed results they could trust and explain, not black-box predictions.
Led the technical direction for a data and machine-learning platform: unifying data capture, building analysis pipelines, and working closely with scientists so models answered the questions they were actually asking.
Analysis that used to take weeks of manual work became a repeatable pipeline. Scientists could compare experiments consistently and focus on the results most likely to matter commercially.
A 30-minute conversation. No pitch, just an honest view of whether and how I can help. Or talk to Maya, my AI guide, right now.