Intelligence
Constellation is an operational learning system. Every simulation, prediction, decision, and observed outcome feeds the digital twin — continuously improving how future missions are planned and executed, with every forecast bounded by physics and every model promoted with the discipline of flight software.
Philosophy
Physics decides what is possible. ML decides what is likely.
Orbital mechanics, link budgets, and power systems are deterministic — we simulate them rigorously, with ITU-R propagation models and full orbit propagation. What physics can't tell you is how a specific radio degrades in rain, how demand shifts on a Tuesday evening, or which station to pick when a weather cell is moving in.
That's where learned models earn their place: scoped to exactly the gap physics leaves open, trained on fleet telemetry, and never allowed to contradict the link budget — because they never see anything else to learn. A prediction that violates physics isn't filtered out; it can't be produced.
Residual learning
The model only learns what physics can't explain
The clear-sky link budget is computed identically in training and serving from ephemeris and RF configuration. Models are trained on the residual — the difference between observed link quality and what physics predicts — and forecasts are reconstructed by adding the physics back at each horizon.
The model portfolio
Forecasters for links, demand, and decisions
Link-quality forecasting
ForecastingWhere every link is heading over the next minutes — as a full distribution with explicit fade and usability probabilities, not a point estimate.
Demand & capacity forecasting
ForecastingWhere traffic is going, not where it was — so capacity decisions run ahead of demand instead of behind it.
Station selection
Decision supportWhich ground station to use for every pass, scored across the entire federated network against mission intent.
Physics baseline
DeterministicA pure-physics forecaster is always on duty. Learned models ship only when they demonstrably beat it — and the system degrades toward physics, never toward silence.
Uncertainty
Distributions, not point estimates
Every forecast ships as an envelope — p10 / p50 / p90 with empirically calibrated coverage — plus explicit probabilities against operational thresholds. A scheduler doesn't need the future; it needs an envelope it can trust. Models are calibrated per site and per fleet, and continuously validated against live operations.
Learning across fleets
Why the platform gets smarter than any single operator
Every fleet is different. Every radio ages differently, every modem has its own personality in rain, every optical terminal its own weather sensitivity, every constellation its own geometry and scheduling habits. No single fleet observes every operational regime — which means no single operator's data can teach a model everything that matters.
Physics is shared; residuals are not. The deterministic models are universal. What compounds across the platform is the infrastructure of learning: the residual-learning framework, the feature engineering, the simulation regimes, and the evaluation gates — each made richer by every climate, hardware generation, and failure mode the platform encounters. Every new deployment expands the platform's understanding of where reality consistently diverges from theory.
Every operational decision — successful or unsuccessful — becomes another labeled example inside the digital twin.
The isolation guarantee
Fleet-specific models remain calibrated on each customer's telemetry, inside that customer's enclave. Statistical improvements to the residual-learning framework, feature engineering, and simulation regimes benefit the platform as a whole — while preserving customer isolation. Shared infrastructure, never shared telemetry.
Where rare events come from
Deep rain fades, gateway outages, solar events, mass conjunctions, hardware failures — the events operators care about most are the ones fleets almost never experience. The simulation engine generates these regimes at ITU-R fidelity, so models learn from thousands of physically realistic worst cases before meeting a real one.
Constellation doesn't become more valuable because it stores more telemetry. It becomes more valuable because every mission expands the operational knowledge encoded in its physics, simulation, and decision engines — the learning layer for space operations.
Why both
Deterministic simulation and learned models are stronger together
Physics alone
Exact about geometry, power, and links
Blind to weather dynamics, hardware aging, and real traffic
ML alone
Adapts to messy, real-world data
Unbounded — free to predict what physics forbids
Together
Learned models constrained to the residual physics leaves open
Predictions stay inside physical reality — and improve every pass
From prediction to action
A forecast that doesn't change the schedule is just trivia
Predictions flow into the same control plane that schedules contacts, selects ground stations, and routes traffic. Operator policy defines the envelope; inside it, actions execute automatically and every outcome feeds back into the models. This is the loop behind every product in the suite.
The full architecture — training pipelines, serving topology, and the security model around them — is documented in the technical whitepaper.
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For mission operations teams
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