From insight to outlook. From reporting to decision-making.
The Fortis AI AI Simulation Engine is an advanced forecasting and simulation platform that helps organizations to to understand, test, and steer future outcomes.
Instead of only analyzing what has happened, this engine shows what is likely to happen – and why.
At the core of the product is a combination of Reinforcement Learning (self-learning models), scenario simulation and a evaluation engine that continuously validates predictions against reality.
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What makes this simulation engine unique?
1. Reinforcement Learning at the core
Unlike classical statistical models, the engine learns by interacting with data:
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The model explores scenarios
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Evaluates outcomes
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Adjusts itself iteratively
This does not produce a static predictive model, but an adaptive system that learns from change, noise and unexpected events.
2. Simulation instead of extrapolation
The engine does not simply predict “more of the same.”
It simulates what-if scenarios, such as:
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Market fluctuations
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Changes in policy or regulation
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Weather conditions, seasonal effects or macroeconomic pressure
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News, sentiment and external disruptions
This makes it possible to to test decisions in advance, before they are implemented in reality.
3. Evaluation engine: prediction vs. reality
A unique part of the product is the evaluation engine:
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Systematically compares forecasts with actual outcomes
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Measures deviations, bias and reliability
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Feeds these insights back into the model
Outcome: transparency, learning capability and demonstrable reliability.
Not a “black box,” but a verifiable system.
Application areas
The AI Simulation Engine is generally designed, but specifically trained per client and context.
Financial & commercial
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Revenue and profit forecasts
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Order and demand development
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Inventory and supply-chain scenarios
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Impact analysis of price changes or market shifts
Insurers & risk models
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Claim volumes under different scenarios
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Effect of weather, season and climate
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Policy changes and external shocks
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Long-term risks vs. short-term impact
General
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Scenario planning for management
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Strategic decision-making
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Stress-testing assumptions
Why not just BI or dashboards?
Business intelligence and dashboards have their value — but also clear limits.
Limitations of BI:
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Focused on the past ("rear-view mirror")
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At best insight into the current state
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Hardly suitable for complex scenarios
Advantages of the AI Simulation Engine:
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🔮 Forward-looking: predictions instead of reports
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🌍 Real-time context: internal data combined with external influences
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🧠 Learning system: adapts to new conditions
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🎯 Decision support: not just insight, but actionable guidance
In short:
BI tells you what happened.
The AI Simulation Engine helps determine what you should do now.
Implementation & customization
The engine is not a plug-and-play box, and that is a deliberate choice.
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Models are specifically trained on customer data
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External data sources are carefully selected
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Assumptions, objectives and evaluation criteria are explicitly recorded
This prevents generic predictions and ensures relevant, defensible outcomes.
Who is this product intended for?
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Organizations that want to base decisions on foresight, not just history
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Companies with complex dynamics and external dependencies
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Management teams that want to calculate scenarios before taking risks
From data to control
The Fortis AI AI Simulation Engine takes the step from analysis to anticipation.
Not as a theoretical experiment, but as a practical tool for strategic decision-making.
👉 Curious what this could mean for your organization?
Contact us for an exploratory session where we focus on your data, scenarios and objectives.
