InGrosz combines predictive models with current market data analysis to limit the influence of emotions on investment decisions. The capital remains liquid - without periods of funds freezing.
InGrosz was created as a tool combining predictive analytics with capital management practice. Instead of single, one-off analyses, the system works continuously - it updates recommendations based on changing market data and the situation of the client's portfolio.
Our goal is to support decisions, not make them automatically. The final decision always remains with the investor, and the system provides analytical justification and alternative scenarios.
Many financial decisions are made under the pressure of time or emotions, especially when the data is scattered and outdated.
InGrosz continuously processes available financial and market data, updating recommendations as conditions change. The role of the system is to limit the number of decisions made solely on the basis of intuition, without taking away the client's control over the capital.
Below we describe how the system transforms data into specific decision recommendations.
Machine learning models analyze patterns in historical and current data, creating a series of probability scenarios rather than a single, rigid forecast. Each scenario contains a description of the conditions under which it could be realized.
The system continuously assesses the risk profile of the portfolio, taking into account correlations between assets and changes in market volatility. When the exposure exceeds the limits set for the client, a rebalancing or diversification suggestion is generated.
Market data and portfolio information are downloaded in automatic cycles and analysis results are updated without the need to manually refresh reports. This allows you to respond to changes in conditions faster than in the periodic inspection model.
The analytical process takes place in three repeatable stages, regardless of the size of the portfolio.
The system connects to a client's existing sources of financial data - transaction history, portfolio structure and selected market data - using secure, permission-restricted connections.
The model processes data in regular cycles, updating scenarios and risk assessment as new market information arrives.
The result of the analysis is presented as a recommendation with justification. The client reads the argument and decides to act on his own.
The scenarios below show how different customer groups use the system.
Middle-income families often need a financial reserve that both works and remains available in emergencies. The system helps establish a portfolio structure that maintains high liquidity while limiting the impact of inflation on the value of accumulated funds.
Entrepreneurs with surpluses of working capital can use the analysis to assess what part of the funds can be engaged without the risk of losing the company's operating liquidity. The recommendations take into account seasonality and variability of flows.
Private investors with a longer time horizon receive capital allocation scenarios with periodic rebalancing, based on changing market conditions, and not on a one-time strategy established at the beginning.
The full list of questions and answers can be found on the website FAQ.
Financial data is sent in encrypted form, and access to individual system modules is distributed in accordance with the principle of limited permissions. The client's funds remain in accounts linked to the investment profile, and the system operates on analytical data, not on direct access to capital without the client's knowledge.
The minimum contribution is determined individually during the initial consultation, as it depends on the risk profile, financial goal and time horizon. We do not use one universal entry threshold for all clients.
The system analyzes the input data against predictive models and generates several possible scenarios with an assigned probability level. The recommendation presented to the client includes justification and the conditions under which it could change - the system serves as a digital decision assistant, not an autonomous capital manager.
NO. The system provides analytical support and recommendations based on data, while the final decision and the responsibility for making it remain with the client.
There is no commitment to starting the analysis - the first step is to review available data and present initial scenarios.
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