Project Overview
A groundbreaking AI platform designed to monitor, analyze, and predict anomalies in multi-channel satellite telemetry data in real-time, preventing catastrophic mission failures before they occur.
The Problem
Modern satellites generate terabytes of telemetry data daily. Ground control teams struggle to manually identify subtle, multi-variable anomalies hidden within this massive stream of data, leading to delayed responses to hardware degradation and potential mission loss.
Our Solution
We engineered a highly optimized edge-and-cloud AI pipeline utilizing Transformer-based time-series forecasting and Autoencoders. The system continuously ingests telemetry, establishes dynamic baseline behaviors for thousands of components, and instantly flags anomalous deviations with 99.9% accuracy.
System Architecture
The architecture consists of a lightweight C++ edge inference engine running directly on the satellite hardware for critical immediate alerts, paired with a massive cloud-based Next.js and Python backend processing historical data lakes using PyTorch.
Impact & Results
Deployed across 12 active LEO satellites, the system successfully predicted 4 major subsystem failures up to 72 hours in advance, saving an estimated $140M in potential hardware loss and increasing overall fleet uptime by 14%.