Predict attribute evolution

We create robust simulation models that predict how vehicle attributes like energy efficiency, CO₂ emissions, mass distribution, and performance evolve throughout development. These forecasts allow teams to anticipate issues, assess design trade-offs, and maintain alignment with product targets. Our models also support gateway reviews by providing future-looking insights based on current development trajectories, ensuring data-driven adjustments happen early in the cycle.

Trace attribute readiness

We equip engineering and program teams with structured KPIs and attribute scorecards designed for milestone gateway reviews. Our metrics focus on traceability, completeness, and risk exposure—helping stakeholders quickly assess whether driveability, CO₂, performance, and energy goals are on track. This improves cross-functional collaboration and ensures consistent communication from engineering to executive level.

Plan and interpret tests

Marklytics enhances your physical testing strategy by aligning test plans with attribute forecasts, ensuring that validation covers key risk areas. We assist in defining test matrices, selecting drive cycles, and analyzing deviations between simulated and actual results. This alignment reduces wasted effort, improves model accuracy, and ensures that physical validation directly supports gateway readiness and attribute sign-off.

Preempt failures early

Using historical project data and predictive algorithms, we identify likely failure modes or validation risks before they manifest in late-stage development. These insights empower teams to redirect resources, revisit simulation assumptions, or run additional test cases—reducing costly delays and supporting smoother engineering gate approvals.

Close the validation gap

Our feedback systems capture discrepancies between physical test results and model predictions, using this information to iteratively improve simulation accuracy. This creates a continuously evolving validation environment where digital twins stay relevant and reliable, reducing the margin of error and shortening the time between design iteration and approval.