ORBITA: AI-Powered Space Intelligence Assistant

Orbit Parameters & Simulation
Mission Setup: Orbit Selection
Mission Operations Center: Command & Control
Mission Objectives Configuration
Live Mission Dashboard: Satellite Health & Status
Landing Interface: ORBITA Command Entry
About ORBITA: Planetary Intelligence Layer

Inspiration

Space missions generate massive amounts of complex data, but extracting meaningful insights still requires significant manual effort and domain expertise. ORBITA was inspired by the idea of using advanced AI reasoning to make space intelligence more accessible, faster, and smarter for researchers, students, and space agencies.

What it does

ORBITA is an AI-powered space intelligence assistant that analyzes space-related data, missions, and anomalies. It provides real-time insights, explanations, and intelligent responses to space-related queries, helping users understand satellite behavior, mission data, and potential space events more efficiently.

How we built it

ORBITA was built using Python as the core language, with Google Gemini API powering the reasoning and intelligence layer. Streamlit was used to create an interactive and user-friendly interface, while LangChain helped manage prompt orchestration and context handling. The system integrates APIs and structured data to deliver accurate, context-aware responses.

Challenges we ran into

One of the main challenges was designing prompts that could consistently guide Gemini to produce reliable, domain-specific insights. Handling context retention, reducing hallucinations, and structuring space-related information in a clear and actionable format also required multiple iterations and testing

Accomplishments that we're proud of

-Successfully built a functional AI assistant tailored specifically for space intelligence -Integrated Gemini’s advanced reasoning capabilities for complex scientific queries -Designed a clean, interactive interface suitable for both technical and non-technical users -Created a scalable foundation that can be extended for real-world space applications

What we learned

We gained hands-on experience in prompt engineering, LLM orchestration, and building AI systems for high-stakes domains like space research. We also learned how crucial clear system design and user-centric interfaces are when working with advanced AI models

What's next for ORBITA: AI-Powered Space Intelligence Assistant

Future plans include integrating real-time satellite feeds, anomaly detection using multimodal inputs, mission simulation support, and deployment on cloud infrastructure for large-scale usage. ORBITA aims to evolve into a powerful decision-support system for space agencies, researchers, and educators.