Simulation Engine
Turn Risk Uncertainty IntoDecision Confidence
Generate 100,000 correlated risk scenarios in milliseconds. No installation. No complexity. Just an API call.
From Estimates to Evidence
In Germany and across Europe, expectations are moving toward a more connected view of risk that reflects dependencies and aggregated exposure rather than isolated ratings. Your existing assessments already contain the expertise. They just need the right engine.
Define your risks with precision
Model each risk with the right distribution, from financial losses to operational events. Move beyond low, medium, high and capture the full range of possible outcomes.
Capture how risks interact
Model dependence with curated sandbox presets for balanced, clustered, and extreme stress behavior. Choose how connected your risks are, from ordinary business overlap to severe scenarios where several losses hit at once.
Quantify your exposure in milliseconds
Run large-scale portfolio simulations in milliseconds and see the loss range, severe-case thresholds, and overall exposure for the full portfolio. Integrate the API into Python, Excel, or any BI tool without installing new software.
See It in Action
Explore the API through a guided supply chain risk sandbox. Adjust the preset scenario, run the simulation, and see how the same API can be adapted to your own portfolio, assumptions, and risk model.
Step 1
Your Risks
Start with a realistic supply-chain portfolio. Rename risks, change the loss shape for each one, or keep the defaults and continue straight through.
Why this shape fits
A supplier issue is usually manageable, but a plant outage, insolvency, or quality failure can create a much larger loss. Lognormal keeps typical losses common while still allowing rare severe events.
Illustrative loss profile
Why this shape fits
Logistics disruption is often estimated as a low case, likely case, and stressed case. Triangular works well when the team can anchor those three points even if hard data is limited.
Illustrative loss profile
Why this shape fits
Raw material costs usually rise through repeated price pressure rather than a single binary shock. Gamma captures strictly positive losses with room for a longer, more expensive squeeze.
Illustrative loss profile
Why this shape fits
For fines, teams usually know a plausible range and a most likely outcome. PERT puts most scenarios near that expected case while still allowing a materially worse penalty.
Illustrative loss profile
Want to Test Your Own Portfolio?
If you want to compare dependency choices on your own risk data or discuss enterprise deployment, talk to us directly.
Built for Enterprise Risk Quantification
Everything you need to move from qualitative assessments to production-grade Monte Carlo simulation.
15+ Distributions
Normal, Lognormal, PERT, Gamma, Weibull, Triangular, Poisson, and more. Including compound distributions for frequency-severity modeling.
Dependency Patterns
Model whether risks stay mostly independent, move together in stress, or spike at the same time in severe scenarios.
100k Simulations in < 500ms
Vectorized engine built for speed. Run full portfolio simulations in real-time, not overnight batches.
Stress Testing
Identify which risks drive extreme scenarios. See contribution breakdowns beyond a configurable stress threshold.
API-First
Single REST endpoint. Works with Python, R, JavaScript, Excel — anything that can make an HTTP call.
Compound Distributions
Model "event probability × severity" or "N events per year × loss per event" natively. Binary event and frequency-severity built in.
Ready to Quantify Your Risk?
Tell us about your risk management needs. We'll show you how the Simulation Engine fits.