Attach trajectories
Register datasets, splits, model versions, and the conditions each trajectory represents.
Use case / World models
Test whether learned dynamics hold beyond the trajectories that trained them.
Evaluate rollout fidelity across horizons, conditions, and trajectory families, then preserve where prediction error becomes operationally meaningful.
Who it is for
For teams building learned simulators, dynamics models, trajectory predictors, and model-based control systems.
A low average validation loss can hide compounding rollout error and narrow support. Reliability Studio connects training provenance, matched trajectories, evaluation conditions, and downstream failure evidence.
Compare rollouts
Pair predicted and reference trajectories across matched seeds and horizons.
Locate error growth
Measure where fidelity degrades and keep in-support results separate from extrapolation.
Guard downstream behavior
Turn consequential rollout failures into repeatable evaluation and regression assets.
What you leave with