DigitalTwin
Your fleet, mirrored in physics and learned from telemetry.
Provide your hardware specifications, constraints, and telemetry. We build a high-fidelity digital twin that combines physics-based simulation with our ML models — for forecasting, optimization, anomaly detection, and what-if analysis you can trust before acting on the real fleet.
What it does
Physics simulation
Orbit, attitude, power, thermal, and RF modeled deterministically from your specs.
ML forecasting
Learned models trained on your telemetry, bounded by what physics allows.
Anomaly detection
Twin-vs-actual divergence surfaces problems before threshold alarms fire.
What-if analysis
Test maneuvers, schedules, and failures against the twin — not the fleet.
Hardware fidelity
Your buses, panels, batteries, radios, and constraints — not idealized placeholders.
Continuous calibration
Live telemetry replays into the twin, so the model tracks the fleet as it ages.
Who it's for
- Operators who need to test decisions against physics before committing them
- Hardware teams that want their specific spacecraft modeled, not a generic bus
- Fleets using telemetry to continuously calibrate models against reality
How it fits
Twins are the modeling substrate of the platform. The forecasts you see in the Console and the Predictions API are backed by the same physics-plus-ML machinery, calibrated per fleet.
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For mission operations teams
Review your ground data architecture on a discovery call.