Buenia AI

Inspiration

Space is full of dynamic challenges—satellites experience anomalies (power glitches, orbit deviations, sensor failures) that require timely intervention. We wanted to bring that excitement to developers and learners: an autonomous mission-control system that not only monitors satellites in real time but also lets you “talk” to a virtual astronaut to troubleshoot issues.

What it does

Real-time monitoring & anomaly detection: Continuously ingests simulated telemetry (e.g., orbit parameters, power/thermal readings) and flags anomalies as they occur.

Interactive astronaut assistance: Offers a chat interface with a simulated astronaut persona—powered by an LLM—that explains the situation and guides you through diagnostic steps or fixes.

Optimization engine: Recommends adjustments (e.g., attitude tweaks, power rebalancing) based on the detected anomaly, simulating how mission control would respond.

Visualization dashboard: Live graphs/maps showing satellite status, alerts, and the current “fix plan” in progress.

How we built it

Frontend: Next.js app with WebSocket or SSE for streaming telemetry and alert updates; React components to render live charts (e.g., using a chart library) and chat UI for astronaut dialogue.

Backend: Fastify

Runs the simulation engine: produces synthetic real-time telemetry streams and injects anomalies at random or preset times.

Hosts an anomaly-detection module: simple rules or lightweight ML model that watches incoming data and emits alerts.

Manages LLM integration: when an anomaly is detected or the user asks, sends context (telemetry summary + anomaly details) to the LLM with a prompt framing the “astronaut” persona, then relays responses back to the frontend.

Real-time plumbing: WebSocket-based channel for pushing telemetry and chat messages immediately to the UI.

Simulation logic: A simplified orbital/thermal/power simulation coded in the backend; can be extended later but currently generates plausible data and anomalies using cesiumjs.

Environment & APIs: No external APIs needed for core simulation; optionally fetch basic space weather data (e.g., real solar activity) if connectivity allowed. All hosted locally or on a hackathon-friendly cloud.

Challenges we ran into

Time constraints: Tight hackathon schedule meant we prioritized core real-time streaming and chat interface, leaving deeper physics models for later.

Frontend–backend integration: Setting up WebSocket/SSE so telemetry updates and chat replies flow smoothly; handling reconnection and UI state when streams drop.

Prompt engineering for astronaut persona: Crafting prompts so the LLM reliably explains anomalies in an accessible way and suggests plausible troubleshooting steps without going off-topic.

Simulating believable telemetry: Designing a simple engine that feels realistic enough but remains lightweight to run in real time.

Accomplishments that we're proud of

Working real-time data pipeline: Live telemetry stream in the UI with anomaly alerts popping up as soon as they occur.

Interactive simulation: The user can click on an alert and immediately chat with the “astronaut” to diagnose and decide corrective actions.

End-to-end flow: From simulated sensor data → anomaly detection → LLM-driven guidance → user action → updated simulation state, all in one seamless loop.

Demo-ready visualization: Clean charts and status indicators that make it easy to see how the satellite behaves over time and how fixes resolve issues.

What we learned

Building real-time streams: Hands-on with WebSocket/SSE integration in Next.js and backend, managing state updates and reconnections.

Prompt design for domain-specific personas: How to frame context (telemetry summary, anomaly details) so the LLM acts like an informed astronaut rather than generic chat.

Simulation basics: Creating a lightweight model that balances realism with performance—injecting anomalies in a controllable way.

What's next for Buenia AI

Gamified educational mode: Turn this into a game for kids or learners: present missions with increasing difficulty, track “mission success” scores, unlock new modules (e.g., multi-satellite coordination).

Enhanced simulation fidelity: Integrate more accurate orbital mechanics or space-weather data via public APIs when available, so scenarios reflect real-world conditions.

Team collaboration mode: Let multiple users join a mission control session, each with a role (e.g., telemetry analyst, comms officer, power engineer), collaborating via chat with the astronaut.

Offline/remote astronaut training: Package scenarios that educators can use in classroom settings to teach systems thinking and problem-solving under pressure.

Voice interface: Add speech-to-text for user commands and text-to-speech for astronaut responses, making the experience more immersive.

Analytics & feedback: Track how users respond to anomalies, which fixes they choose, and refine the simulation or astronaut guidance based on common mistakes.

Deploy for real data hookup: Eventually connect to live (or near-live) satellite telemetry for demonstration satellites or cubesats, so users can practice on actual missions in a sandbox.

Built With

• cesiumjs
• fastify
• nextjs
• postgresql