Launch HN: Constellation Space (YC W26) – AI for satellite mission assurance

Summary

Hi HN! We're Kamran, Raaid, Laith, and Omeed from Constellation Space (https://constellation-io.com/). We built an AI system that predicts satellite link failures before they happen. Here's a video walkthrough: https://www.youtube.com/watch?v=069V9fADAtM.Between us, we've spent years working on satellite operations at SpaceX, Blue Origin, and NASA. At SpaceX, we managed constellation health for Starlink. At Blue, we worked on next-gen test infra for New Glenn. At NASA, we dealt with deep space communications. The same problem kept coming up: by the time you notice a link is degrading, you've often already lost data.The core issue is that satellite RF links are affected by dozens of interacting variables. A satellite passes overhead, and you need to predict whether the link will hold for the next few minutes. That depends on: the orbital geometry (elevation angle changes constantly), tropospheric attenuation (humidity affects signal loss via ITU-R P.676), rain fade (calculated via ITU-R P.618 - rain rates in mm/hr translate directly to dB of loss at Ka-band and above), ionospheric scintillation (we track the KP index from magnetometer networks), and network congestion on top of all that.The traditional approach is reactive. Operators watch dashboards, and when SNR drops below a threshold, they manually reroute traffic or switch to a backup link. With 10,000 satellites in orbit today and 70,000+ projected by 2030, this doesn't scale. Our system ingests telemetry at around 100,000 messages per second from satellites, ground stations, weather radar, IoT humidity sensors, and space weather monitors. We run physics-based models in real-time - the full link budget equations, ITU atmospheric standards, orbital propagation - to compute what should be happening. Then we layer ML models on top, trained on billions of data points from actual multi-orbit operations.The ML piece is where it gets interesting. We use federated learning because constellation operators (understandabl...