As satellite deployments grow from roughly 10,000 today toward 100,000+, the electromagnetic noise between Earth and space is set to increase — making interference and outages more likely, and threatening the world's growing dependence on space-enabled services: telecommunications, GPS, financial transactions, Earth imagery.

Magnestar was founded in December 2021, in Toronto, to solve that problem directly — aggregating spectrum data and applying both physics-based modeling and machine learning to keep communication pathways clear. It was the proving ground for everything that followed: fragmented, high-volume, highly constrained data that has to be trusted in real time.

The underlying problem proved broader. Across operational environments, critical information is fragmented across sources, systems, sensors, and organizations. Magnestar now connects ISR, imagery, visual, spectrum, and other operational data; integrates, classifies, and governs it with provenance intact; reasons across it with physics-based and AI models; and enables trusted decisions and action.

That same foundation — the 24/7x platform — has grown into a unified intelligence system, built to compound. What started as spectrum interference prediction now aggregates ISR, imagery, visual, and spectrum data across both space and Earth, governs what can move where as it comes in, links and cross-references it into one connected view, reasons over that view using a dynamic knowledge graph to generate trusted recommendations, and can act on those recommendations — from routing communications automatically to whatever action an operator configures.

The underlying expertise — physics-grade modeling of contested, noisy, high-volume environments — hasn't changed. What it's applied to has expanded.