This page describes contributions made as part of my employment, not a project delivered independently by EdgeConn. Details are kept high level, without client or infrastructure specifics.

TQC web dashboard, view 1
The Challenge

A handheld terminal for industrial quality control

TQC needed a backend and a Flutter Android app for a handheld terminal used in industrial quality control processes, with an AI model built by a teammate integrated into the app's workflow.

The Approach

My role: backend and app development

As part of my role, I built the FastAPI backend and the Flutter Android app for the handheld terminal, and integrated a teammate's AI model into the app so quality-control results could be assisted by that model in the field. The system is deployed on the company's cloud infrastructure.

  • FastAPI backend supporting the handheld terminal
  • Flutter Android app for field use in quality control
  • Integrated a teammate's AI model into the app's QC workflow
  • Deployed on the company's cloud infrastructure
Tech Stack

What I worked with

FastAPI for the backend and Flutter for the Android app, deployed on cloud infrastructure, with an AI model integrated into the QC workflow.

FastAPI Flutter Cloud
The Outcome

A working tool for field quality control

The app and backend are in use for industrial quality control, giving field users a handheld tool with AI-assisted results, backed by cloud infrastructure.

  • Handheld QC terminal app in field use
  • AI-assisted results integrated into the day-to-day workflow
  • Backend and app deployed on cloud infrastructure

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