The orbital compute stack.
Four layers - planning, intelligence, runtime, and distributed compute - that take a workload from a feasibility check to running on the satellite and coordinated across Earth and space.
Orbital Runtime
CAE
Constraint-aware execution planning. POST a satellite and a workload, get a feasible plan in under two seconds.
Explore →Operator Agent
The execution layer. A Rust SDK that runs the plan on the spacecraft and survives intermittent links.
Explore →Orbital Sim
Propagate positions, detect eclipses, and compute ground passes - a stateless API for orbital digital twins.
Explore →Distributed Compute
Federated Learning
Train across Earth and orbital nodes with gradient compression for high-latency links.
Explore →Model Partitioning
Split neural networks across Earth and space to run large models on heterogeneous nodes.
Explore →Sync & Space Mesh
Bandwidth-aware synchronization across ground passes and dynamic inter-satellite-link routing.
See the demo →Orbital Intelligence
Real-time tracking
Track satellites at scale from the public catalog with high-performance SGP4 propagation.
Explore →Conjunctions
Forecast close approaches with calibrated uncertainty, and flag maneuvers and anomalies.
Explore →Live Tracker
Watch the catalog move in real time - the situational picture every plan depends on.
Open tracker →Planning Tools
Feasibility analysis
Know whether a workload can run on a given satellite - and what it costs - before it leaves the ground.
Explore →Thermal modeling
Model duty cycle, attitude, and radiator capacity so plans stay inside the thermal budget.
Explore →Orbit Scheduler demo
Watch the scheduler make real-time placement decisions across a live constellation.
Try the simulator →Put your first workload in orbit.
Tell us what you want to run and the satellite you want to run it on. We'll come back with a feasible plan - or an honest reason it can't be done yet.