Miškas Zeltova processes real-time data and generates specific recommendations for freelancers and independent investors in Lithuania. The system assesses risk before every decision, and data is protected by military-grade encryption.
Start the assessmentThe basis of the platform consists of four components, which together form the decision optimization cycle — from data collection to providing a protected recommendation.
Models analyze historical and current data flows to estimate expected results before making a financial or operational decision.
Input data is processed continuously, so recommendations are updated as market or operational conditions change, rather than on a fixed schedule.
Each recommendation is accompanied by a risk indicator that shows the probability of possible losses or fluctuations in income during a specific period.
User data, including financial information, is stored and transmitted using industry-standard encryption.
The system architecture separates data processing, analytics and user interface layers. This separation allows updating predictive models without disrupting data security protocols and without changing the user's workflow.
The process is divided into four stages. Each stage is recorded and can be checked, so the user always understands why the system made a specific recommendation.
The system accepts activity data provided by the user and links them with publicly available market indicators related to the selected activity area.
A predictive model evaluates possible scenarios based on historical patterns and current conditions, discarding unlikely outcomes.
Each scenario receives a risk score calculated based on the size of the potential loss and the probability distribution.
The user is presented with a specific offer of action with a specified level of risk, so that the final decision is made by the user himself.
Data security at every stage. Data is encrypted during upload, processed in an isolated environment and is not passed on to third parties for analytics purposes.
The illustrative use cases below show how predictive analytics can be applied to different sources of additional revenue.
The system analyzes the density of orders by time and place, suggesting working hours with the lowest risk of running out of orders.
The model assesses project pricing fluctuations in the market and warns when the proposed price does not correspond to the risk associated with the duration of the project.
The platform monitors the sensitivity of the portfolio to market fluctuations and provides recommendations for reallocation of positions based on risk limits.
The system compares occupancy rates with seasonal fluctuations and identifies periods when price adjustments reduce the risk of revenue loss.
Return assessment is based on a comparison of two quantities: the potential additional income stream and the associated risk indicator. The system does not provide a fixed number of income — it shows the expected range and the probability that the result will meet the risk tolerance set by the user.
Data security on the Miškas Zeltova platform is not an additional feature — it is a core feature of the system. Each data transaction is processed in accordance with the requirements of the General Data Protection Regulation (GDPR) applicable in the European Union.
Answers to technical and financial questions that users ask before starting to use the platform.
All data is encrypted with AES-256 standard during storage and TLS 1.3 protocol during transmission. Access to the account is additionally protected by multi-level authentication.
No. The platform provides an expected range of results and a risk assessment based on available data. The final decision is always made by the user.
Recommendations are revised automatically when inputs or market conditions change, rather than at a fixed time interval.
Yes. Data processing processes are aligned with the provisions of the General Data Protection Regulation applicable in the European Union and Lithuania.
Activity or investment data related to the selected area is required — for example, order history or portfolio composition. Additional personal data is not required.
Yes. The user can submit a request for the removal of data at any time, in accordance with the procedure and deadlines established by the GDPR.
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