Readiness Control Tower

Readiness Control Tower is a public-safe mission operations demo that turns messy operational data into a commander-facing readiness picture. It uses only synthetic data and is designed to show end-to-end product judgment: data modeling, API design, root-cause analysis, frontend workflow, and deployable local infrastructure.

Why This Exists

Mission teams often do not need another static dashboard. They need to know why readiness is degrading, what is blocking execution, and which action has the best operational return. This repo models that workflow with synthetic sortie, maintenance, parts, personnel, and system outage data.

The project is intentionally relevant to roles that combine engineering with operational problem solving, including forward deployed engineering, security engineering, mission analytics, and government deployment work.

What It Does

• Ingests synthetic operational data from CSV files.
• Scores fleet and mission readiness.
• Explains root causes behind delays and degraded assets.
• Produces action recommendations with evidence.
• Exposes a FastAPI backend for readiness, timeline, root-cause, and what-if endpoints.
• Provides a React/TypeScript command dashboard for operational scanning.
• Runs locally through Docker Compose.

Engineering Scope

This repo is an operational software artifact, not just a dashboard. It clarifies ambiguous user workflows, models messy cross-domain data, exposes tested backend logic, and provides a frontend that supports decisions instead of static reporting.

Relevant areas:

• Mission and deployment engineering: operational workflows, root-cause analysis, recommendation surfaces, and public-safe mission framing.
• Security engineering: degraded systems, audit-aware workflows, controlled data handling, and clear boundaries around sensitive information.
• Platform/product engineering: API design, Docker-based local deployment, frontend/backend integration, and reviewer-friendly tests.

Reviewer Fast Path

• Start with backend/app/analytics.py for the readiness model.
• Check backend/tests/test_analytics.py for behavior-focused tests.
• Open frontend/src/App.tsx for the command dashboard workflow.
• Read docs/PORTFOLIO_REVIEW.md for the technical review notes.

Tech Stack

• Backend: Python, FastAPI, standard-library analytics
• Frontend: React, TypeScript, Vite
• Data: synthetic CSV datasets with a warehouse-friendly schema
• Infra: Docker Compose
• Tests: Python unittest

Repository Layout

.
├── backend/              # FastAPI service and analytics engine
├── frontend/             # React/TypeScript dashboard
├── data/synthetic/       # Public-safe synthetic readiness datasets
├── ARCHITECTURE.md       # System design notes
├── PRD.md                # Product requirements and user workflows
└── TODO.md               # Roadmap for production-grade extensions

Run Locally

Backend

cd backend
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install -r requirements.txt
uvicorn app.main:app --reload

The API runs at http://localhost:8000.

Frontend

cd frontend
npm install
npm run dev

The app runs at http://localhost:5173.

Docker

docker compose up --build

API Examples

curl http://localhost:8000/api/readiness
curl http://localhost:8000/api/root-causes
curl -X POST http://localhost:8000/api/what-if \
  -H "Content-Type: application/json" \
  -d "{\"expedite_parts_days\": 2, \"restore_systems\": [\"maintenance-scheduler\"]}"

Public-Safe Data Statement

All datasets are synthetic and created for demonstration purposes. This repository does not include operational, classified, export-controlled, proprietary, or government-furnished information.

Engineering Notes

This repo covers:

• Translate ambiguous operations into a usable software workflow.
• Build across backend, frontend, data, and deployment surfaces.
• Explain technical decisions in a mission-first way.
• Keep public portfolio work clear of sensitive information.

See SECURITY.md before adding data or integrations.