G-Sim
Turn hazard models into the scenarios that expose unsafe behavior, run them against digital twins at scale, and measure exactly how much of your operational domain you have covered. Stop sampling at random and start testing what matters.
Capabilities
From hazard to validated behavior
AI scenario generation
Generate scenarios from hazard models and operational design domain definitions, not random sampling.
Digital-twin validation
Evaluate system behavior against high-fidelity twins across environments and conditions.
Simulation orchestration
Launch and manage large campaigns across your compute, with automated result triage.
Edge-case discovery
Search for rare, high-risk conditions that traditional test suites overlook.
Coverage analysis
Quantify coverage of the operational domain and expose residual gaps.
Scenario analytics
Prioritize the next test by risk, novelty, and failure likelihood.
Risk-driven testing
Test where the risk actually is
G-Sim reads the hazards and safety goals from your analysis and concentrates simulation effort on the conditions most likely to violate them. The result is more safety-relevant coverage per compute hour and earlier discovery of the failures that matter.
- Hazard-guided scenario parameterization
- Rare-event and adversarial sampling
- Regression tracking across software releases
- Coverage reporting mapped to the operational domain
Outcomes
More coverage, fewer surprises
Representative target outcomes. Results vary by program and compute.
See coverage on your operational domain
We will generate risk-driven scenarios from a hazard model and show you where your current testing leaves gaps.