Application Flow Tracker

Fri, 22 May 2026 00:00:00 +0000

The problem

Teams reviewing grants, job applications, or admissions often lose context across spreadsheets, messages, and manual status updates. They need a clear record of each application, its review, the decision, and the comments behind it.

What I built

Application Flow Tracker separates a Django Ninja backend from a React and TypeScript frontend. It tracks applications from draft through submission, review, approval, rejection, or requests for more information. UUID-based tracking keeps references stable, while typed schemas define the API contract.

The demo frontend is deployed on Vercel and the API has a Google Cloud Run endpoint, showing the path from local code to independently deployed services.

Engineering decisions

Typed API schemas make request and response contracts explicit for both frontend and backend work.
Separate services allow the interface and API to evolve and deploy independently.
Structured review states turn an informal process into a traceable workflow.
Anonymous throttling and validation reduce abuse and malformed input at the boundary.
REST client examples and tests provide quick, repeatable verification paths.

What this demonstrates

Workflow modelling, Python API design, TypeScript integration, cloud deployment, defensive API practices, and the ability to deliver a usable system across the stack.

Scope note: The repository transparently documents follow-up work such as RBAC, Redis caching, Docker, and fuller CI/CD. These are improvement targets, not current-feature claims.

Cloud Run | Joseph Wathika — Backend Engineer

Application Flow Tracker

The problem

What I built

Engineering decisions

What this demonstrates