Aegis

As satellite constellations continue to grow, space operations are becoming increasingly complex. Most existing mission control systems focus on displaying telemetry, leaving engineers to manually interpret spacecraft health and make operational decisions. We wanted to explore how artificial intelligence and Digital Twin technology could transform raw mission data into actionable engineering intelligence.

AEGIS was inspired by modern aerospace organizations such as NASA, ESA, SpaceX, Palantir, and Anduril, with a vision of creating a next-generation mission control platform that not only monitors satellites but also assists engineers with predictive analysis, risk assessment, and mission planning.

What We Built

AEGIS is an AI-powered Mission Intelligence Platform designed to support spacecraft operations throughout the satellite lifecycle.

The platform includes:

🌍 Interactive mission dashboard 🛰️ Satellite Digital Twin 📊 Mission health monitoring 🤖 AI Mission Commander for engineering analysis 🔧 Predictive maintenance recommendations 📈 Live telemetry simulation 📅 Mission planning and maintenance scheduling 📄 AI-generated engineering reports 📡 Orbital visualization architecture for CesiumJS integration

Rather than simply displaying telemetry values, AEGIS analyzes spacecraft conditions and produces engineering summaries, mission risk assessments, and operational recommendations.

How We Built It

The frontend was developed using React, TypeScript, Vite, Tailwind CSS, Zustand, and Recharts to create a responsive mission control interface.

The backend was built with Node.js and Express, with integration planned for the Google Gemini API to generate AI-powered engineering assessments. Mock telemetry services simulate spacecraft subsystem data, enabling realistic demonstrations of satellite health monitoring and predictive maintenance workflows.

The application follows a modular architecture, separating Digital Twin logic, AI services, telemetry processing, mission planning, reporting, and dashboard components for scalability.

Challenges We Faced

One of the biggest challenges was designing a realistic spacecraft operations workflow using publicly available information. Unlike terrestrial systems, real satellite telemetry is generally not publicly accessible, so we developed a hybrid approach that combines simulated engineering telemetry with real-world orbital data integration plans.

Another challenge was architecting the platform to support future capabilities—such as autonomous robotic satellite servicing—while still delivering a functional hackathon MVP. We also focused on creating an interface that feels like a professional aerospace mission control system rather than a conventional analytics dashboard.

What We Learned

Building AEGIS strengthened our understanding of:

Digital Twin architecture AI-assisted engineering decision support Satellite mission operations Telemetry processing Predictive maintenance concepts Aerospace-inspired UI/UX design Modular full-stack application architecture Integrating AI into engineering workflows Future Vision

AEGIS is designed as the foundation of a broader orbital operations ecosystem. Future versions will integrate live satellite data, orbital debris intelligence, space weather analysis, autonomous mission optimization, and robotic satellite servicing.

Our long-term vision is to transform AEGIS into an intelligent mission control platform capable of helping engineers monitor, maintain, and extend the operational life of spacecraft, contributing to safer and more sustainable space infrastructure.

Built With

• react
• recharts
• tailwind-css
• typescript
• vite
• zustand