AI platform for investment decisions
Kalvena Záruketa replaces manual searches of charts, reports and stock terminals with a single source of machine learning-based analysis. The recommendations that the platform generates remain searchable in the public performance log, so you can verify their quality for yourself.
The reality of remote decision making
For professionals who manage their portfolio from anywhere, this issue is all the more pressing. Without constant access to an internal team of analysts, they rely on their own judgment, often under time pressure. Kalvena Záruketa replaces this intermediate step with an analytics layer accessible from anywhere with an Internet connection.
How the platform works
The system is not based on a single model, but on a combination of three interconnected layers that together cover the path from raw data to concrete recommendations. Each layer has a clearly defined role and results can be audited.
A machine learning model processes historical and current market data to identify recurring patterns. The output is not an unequivocal forecast, but a distribution of probabilities that can be further worked with when making decisions.
Watched markets, instruments and indicators are checked at regular intervals throughout the day. A deviation from expected behavior will trigger an alert before it becomes visible in regular messages.
The algorithm compares the current portfolio distribution with a defined level of risk and suggests adjustments that keep the ratio of return and risk within the set limits, instead of one-time tips for a specific purchase.
Verifiability of results
Each recommendation that the model generates is written to the log at the time of creation, not retrospectively. The result is then compared with the original prediction and the deviation remains traceable together with the date and context of the decision.
This procedure prevents the subsequent modification of the history and gives the user community a tool to verify whether the long-term quality of the recommendations corresponds to the communicated methodology.
Internally audited logging methodologyInvolvement in the work process
You connect stock exchange terminal accounts, bank exports or your own tables via a secure API. The platform unifies the data into one format without the need for manual rewriting.
The model processes unified data, compares it to historical patterns, and generates recommendations along with a degree of confidence. The result is available from the web interface from anywhere.
You approve, modify, or reject recommendations at your own discretion. The platform serves as a basis for decisions, the user always has the final say.
Frequently Asked Questions
All communication between your resources and the platform takes place over an encrypted connection (TLS 1.2 and higher). Access data to external accounts are not stored in readable form, but through tokenized access, which can be revoked at any time on the data provider's side.
The price depends on the volume of processed data and the number of monitored markets. You will get an exact calculation for your case after filling out the request form, when we will discuss the range of connected sources together.
Individual client data is logically separated and is not used to train other users' models. Only anonymized and aggregated market data are used for training general prediction models, not the contents of individual portfolios.
The processing of personal data complies with GDPR requirements, including the right to erasure and data portability. Details on the specific categories of data processed can be found in the personal data protection policy.
Experience what your portfolio analysis looks like without committing to a long-term setup. You can revoke access at any time, the public performance log remains available regardless of whether you are actively using the platform.
Try the platform