Work2022infrastructure, completed
Adaviv Mantis
Production backend and field-operations software for a crop-scanning robot deployed across distributed agricultural sites.
- Role
- Software Engineer and Solution Architect
- Evidence
- Reduced backend latency by approximately 30% while improving recovery paths for field-deployed production systems.
- Built with
- Python, Flask, AWS, React Native, Robotics
Run it
Cut the network. Knock the robot over. Nothing it photographed is lost.
Click a plant to make it run hot. Drag to turn the house.
- Waiting on the robot
- —
- Photographed
- —
- Delivered
- —
- Hot plants found
- —
A small model written for this page. The production system is private, and what a hot plant means here is invented.
Problem
Mantis combined robotics, RGB and thermal imaging, cloud services, and field operators across distributed agricultural sites. Networks disappear, devices enter inconsistent states, and remote operators still need a safe recovery path.
My role
As a Software Engineer and Solution Architect at Adaviv, an MIT spinoff, I carried work from operational requirements through implementation, deployment, diagnosis, and recovery.
System ownership
- Python and Flask APIs for device control and state-driven camera workflows
- RGB and thermal capture, streaming, and AWS-backed media handling
- Structured logging, remote diagnostics, and recovery paths for unattended hardware
- React Native features for live feeds, QR scanning, geotagging, uploads, and reports
Outcome
Backend workflow improvements reduced latency by approximately 30% and supported production uptime. The implementation is private, so this page describes responsibilities and system boundaries without exposing proprietary details.
Claims on this page are limited to work I owned and outcomes I can defend. Private systems are described without proprietary implementation details.
Next on the sheet
2026
Healthbridge
Backend engineer and system designer