¿Quién es propietario de los datos que se analizan?

Los datos financieros que usted introduce o conecta siguen siendo de su propiedad en todo momento. Zhibruntur los procesa únicamente para generar el análisis solicitado y no los cede, vende ni utiliza con fines distintos a los definidos en el contrato de servicio.

¿Cómo se garantiza la precisión de las recomendaciones de la inteligencia artificial?

El modelo se entrena con datos históricos verificables y se revisa periódicamente para detectar desviaciones. Ninguna recomendación se presenta como una certeza absoluta: cada sugerencia incluye el nivel de confianza estimado y los supuestos utilizados, de forma que el usuario pueda evaluarla con criterio propio.

¿Qué implica el problema de la "caja negra" en este sistema?

Zhibruntur documenta las variables que influyen en cada recomendación y permite consultar el razonamiento general detrás de una alerta o sugerencia. No se revelan los algoritmos propietarios, pero sí los factores de entrada y el peso relativo de cada uno, evitando decisiones basadas en un resultado inexplicado.

¿Qué requiere la implementación inicial?

El proceso comienza con la conexión segura de las cuentas y activos que desea analizar, seguido de una sesión de configuración de objetivos y horizonte temporal. La mayoría de los usuarios ven las primeras recomendaciones dentro de los primeros días tras la activación.

Zhibruntur data analysis and artificial intelligence platform for financial decisions

Predictive analysis for family assets

Accurate financial decisions, protected with military-grade encryption

Zhibruntur unifies your financial data in an encrypted environment and applies predictive artificial intelligence models to anticipate risks and propose investment strategies tailored to your wealth horizon.

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The problem

Long-term estate planning generates more data than a family can process alone

Bank accounts, investment funds, insurance and real estate assets produce dispersed information in different formats and entities. When that data is not consolidated, opportunities for tax optimization or portfolio rebalancing often go unnoticed.

At the same time, exposing sensitive financial information to multiple platforms increases security risk. Zhibruntur centralizes that analysis in a single encrypted environment, so data complexity no longer results in late or poorly informed decisions.

Zhibruntur team analyzing financial data with predictive models

How it works

A three-stage process, without a black box

The system is designed so that you understand each phase of the analysis, without needing to know the internal details of the model.

Data ingestion

Your accounts, assets and liabilities are imported and consolidated in an encrypted environment, without exposing credentials to third parties or storing information outside of Zhibruntur certified systems.

Predictive processing

Machine learning models analyze historical trends and macroeconomic variables to estimate probable profitability and risk scenarios in different time frames.

Strategic recommendation

The system translates the analysis into concrete asset allocation recommendations. The final decision always remains in the hands of the user or their advisor.

Security and compliance

Military-grade encryption applied to every layer of the system

Protecting a family's savings requires the same level of rigor as critical infrastructure. Therefore, Zhibruntur builds its architecture on military-grade encryption standards in transit and at rest.

AES-256

End-to-end encryption

All financial data is encrypted with the AES-256 standard, both during transmission and storage, following protocols equivalent to those used in defense and institutional banking environments.

GDPR

European regulatory compliance

The processing of personal data complies with the General Data Protection Regulation, with explicit user control over what information is stored and for how long.

CNMV

Alignment with financial supervision

The analysis and recommendation processes operate in accordance with the transparency criteria required by Spanish securities market regulations, without replacing regulated advice when this is mandatory.

Practical applications

Common scenarios in family wealth management

Each case is supported by the same analysis engine, adjusted to the user's specific objective.

Diversification

Identification of trends to rebalance the portfolio

The system compares the current composition of your investments with historical patterns of correlation between assets and suggests gradual adjustments to reduce risk concentration, without recommending abrupt or speculative movements.

Risk mitigation

Real-time alerts for significant changes

When a relevant variable deviates from the expected ranges, for example an abrupt change in volatility, the system generates a notification explaining the reason and the available options, allowing action to be taken before the impact accumulates.

Tax optimization

Long-term heritage preservation

The analysis identifies temporary windows and investment structures that can reduce the effective tax burden on capital gains and transfers, within the current legal framework, prioritizing the stability of family assets over short-term gains.

Frequently asked questions

Technical and financial issues that usually arise before deciding

Who owns the data being analyzed?

The financial data you enter or connect remains your property at all times. Zhibruntur processes them solely to generate the requested analysis and does not transfer, sell or use them for purposes other than those defined in the service contract.

How is the accuracy of artificial intelligence recommendations guaranteed?

The model is trained with verifiable historical data and is periodically reviewed for deviations. No recommendation is presented as absolute certainty: each suggestion includes the estimated confidence level and the assumptions used, so that the user can evaluate it with their own criteria.

What does the "black box" problem imply in this system?

Zhibruntur documents the variables that influence each recommendation and allows you to see the general reasoning behind an alert or suggestion. The proprietary algorithms are not revealed, but the input factors and the relative weight of each one are, avoiding decisions based on an unexplained result.

What does the initial implementation require?

The process begins with securely connecting the accounts and assets you want to analyze, followed by a goal and time horizon setting session. Most users see the first recommendations within the first few days after activation.

Protect your legacy with cutting-edge intelligence

A technical demo allows you to see how Zhibruntur processes your own financial scenarios, under the same military-grade encryption that protects the system in production, before making any decisions.

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