Model and test alignment

We validate simulation models by comparing them directly with real-world test results, ensuring alignment across driveability, CO₂, energy consumption, and performance. By identifying mismatches between virtual predictions and physical data, we refine models and improve accuracy. This correlation process enhances engineering confidence, supports faster decision-making, and minimizes risks of late-stage surprises during validation or regulatory testing.

Visualize attribute behavior

Our dynamic dashboards offer clear visualizations of attribute behavior across simulations, vehicle tests, and benchmarking data. Engineers can filter, track, and analyze trends for specific metrics—such as mass, acceleration, fuel usage, and energy loss—under different conditions. These tools improve transparency, highlight underperforming areas, and help cross-functional teams stay aligned during reviews, validation phases, and decision gates.

Data-driven investigation

When attributes underperform, we use simulation-test overlays and anomaly detection to trace the root cause. Whether it’s a software calibration issue, hardware mismatch, or environmental variable, our analytics framework helps engineers isolate the problem faster. This reduces testing cycles, prevents misdiagnosis, and enables teams to act decisively with confidence.

Monitor progress continuously

We implement KPI frameworks that monitor attribute health at every development milestone. Whether it’s CO₂ per km, energy efficiency in urban cycles, or peak torque delivery under load, our tools compare actual results to targets. Teams gain continuous performance feedback, enabling timely course corrections, clearer gateway assessments, and traceable compliance documentation.

Feedback into development

We enable near real-time integration of test data into simulation environments. Engineers can rapidly update models and retest scenarios based on current findings. This tight feedback loop shortens development cycles, increases model precision, and strengthens the reliability of digital twins as they evolve alongside physical prototypes.