AI-powered data analysis in real time
Diversify your portfolio with accurate predictive models. From the flood of data to the investment decision in under 60 seconds.
Start your portfolio nowWhy classical analysis reaches its limits
Manual analysis costs time and precision
Information overload and emotional wrong decisions cost time and returns. Anyone who evaluates market data by hand loses hours that can be crucial for short-term movements.
Predictive analytics as an alternative
Schilhermut uses predictive analytics to validate billions of data points and translate them into clear strategies. The result is a structured basis for decision-making instead of a gut feeling.
A tool for data-driven decisions
Schilhermut is designed for professionals who want to build capital without spending hours immersed in market reports. The platform combines risk management, real-time intelligence and individual portfolio logic in one interface.
The focus is on a simple principle: complex models should deliver understandable results. Each recommendation can be traced back to the underlying data sources so that users can understand why a suggestion was made.
Technical depth, easy operation
Each function is aimed at one goal: quick, comprehensible decisions without additional specialist effort.
60-second setup
Intuitive integration of your data sources. Connect depot, confirm parameters, start analysis – without manually configuring individual key figures.
Predictive Modeling
Risk minimization through AI-based scenario analysis. The model simulates several market trends before making a recommendation.
Real-time insights
Immediate notification of market changes. Relevant deviations are marked without you having to manually monitor prices.
Scalable recommendations
Tailored strategies for growing capital structures. The logic adapts if the investment amount or risk appetite changes.
How the analysis takes place in the background
Four steps that together form the basis of every recommendation.
Data aggregation
Automated collection of global market indicators from public and licensed sources.
AI validation
Filtering noise through neural networks that separate reliable patterns from short-term outliers.
Optimization
Comparison with your individual risk parameters before a strategy is proposed.
Execution
Providing action-oriented dashboards with concrete, comprehensible action steps.
Transparency through methodology
We rely on mathematical evidence instead of vague promises. The following illustration shows an example of how the risk profile of a simulated portfolio can develop over a backtesting period of twelve months if predictive analytics is incorporated into the allocation.
Every metric in the dashboard can be traced back to the underlying data source. This is what distinguishes a model-based assessment from a pure forecast.
Simplified representation of a volatility reduction in the simulated backtesting period. Serves as an illustration of the methodology, not as a return forecast.
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