¿El servicio cumple con normativa financiera aplicable en Argentina?

Valérion Deloquaire opera como herramienta de análisis e inteligencia de datos, no como entidad que custodia fondos ni ejecuta órdenes directamente. Cada usuario es responsable de operar a través de brókers o entidades reguladas habilitadas en su jurisdicción.

¿Qué implica el onboarding de 60 segundos?

El proceso cubre la definición de perfil de riesgo, horizonte temporal y capital de referencia. No incluye la verificación de identidad ante terceros regulados, que sigue el flujo estándar de cada entidad financiera conectada.

¿Cómo se garantiza la precisión de los datos utilizados?

Las fuentes se validan por consistencia cruzada entre proveedores antes de alimentar el modelo. Cuando se detectan discrepancias superiores a un umbral definido, el dato se marca y se excluye del cálculo hasta su revisión.

¿Puedo escalar el uso si trabajo con varios portafolios de clientes?

Sí. La arquitectura permite gestionar múltiples perfiles de riesgo en paralelo, cada uno con su propio historial de recomendaciones y parámetros independientes.

Valérion Deloquaire — AI platform for remote investing

Real-time predictive analytics for remote investment decisions

Valérion Deloquaire processes volumes of market data and translates them into actionable recommendations, without the need for its own infrastructure or physical presence on a trading desk.

No commitment to permanence. Architecture based on auditable risk models.

How decision optimization works

The engine combines historical series, live market signals and adjustable risk models to generate a portfolio structure without constant manual intervention.

STEP 1

Data ingestion and normalization

The system connects sources of price, volume and macro indicators, and normalizes them into a single structure before any predictive calculations. This reduces the noise produced by heterogeneous formats between markets.

Connected sources14 markets
Refresh Rateintraday
Ingestion latency< 4 sec

STEP 2

Predictive modeling with risk adjustment

Predictive models weigh likely scenarios based on recent volatility and correlation between assets, prioritizing stability over speculative performance. The result is a recommendation with an explicit confidence range.

STEP 3

Portfolio setup in 60 seconds

Once the user's risk parameters are defined, the system creates the initial assignment without the need for prior manual analysis. The setup process is designed to be completed in less than a minute, including profile selection.

Setup time60 sec
Required parameters3
manual interventionOptional

Data Intelligence Engine Logic

Each recommendation arises from a four-stage, documented and reproducible process that prioritizes risk reduction over aggressive return maximization.

Collection

Market data, volume and macroeconomic indicators from public and private sources with traceability of origin.

Normalization

Homogenization of units, time zones and sampling frequencies between different assets and markets.

Risk modeling

Calculation of expected volatility, correlation between assets and maximum tolerated exposure according to the configured profile.

Recommendation

Delivery of a portfolio allocation with confidence range and justification for each weighting.

Use cases for remote investors and analysts

The same data engine is applied to different profiles, from short-term tactical decisions to medium-term strategic planning.

Fintech

Risk management in financial products

Fintech teams in Argentina use the engine to calibrate exposure limits in retail investment products, adjusting parameters without depending on a full-time internal risk area.

Strategic planning

Medium and long term scenarios

Independent analysts build comparative capital allocation scenarios for clients, based on projections generated with historical series of up to five years.

Arbitration in real time

Detection of differentials between markets

Investors operating remotely identify price differentials between local and international markets through engine-generated alerts, with decision windows of minutes.

How we evaluate model accuracy

Instead of generic testimonials, we publish the validation logic that we apply on each version of the engine before its production deployment.

The algorithm is backtested against periods of high volatility before being enabled for new users. Data is stored under encryption in transit and at rest, and operational access is limited to auditable automated processes.

60 sec Initial portfolio deployment time

5 years Historical data horizon used in backtesting

Encryption In transit and at rest for all user data

Auditable Model decision log available by query

Common technical and logistical questions

Does the service comply with financial regulations applicable in Argentina?

Valérion Deloquaire operates as a data analysis and intelligence tool, not as an entity that custody funds or directly execute orders. Each user is responsible for operating through brokers or regulated entities authorized in their jurisdiction.

What does 60-second onboarding entail?

The process covers the definition of risk profile, time horizon and reference capital. It does not include identity verification before regulated third parties, which follows the standard flow of each connected financial entity.

How is the accuracy of the data used guaranteed?

Sources are validated for cross-vendor consistency before feeding the model. When discrepancies greater than a defined threshold are detected, the data is flagged and excluded from the calculation until reviewed.

Can I scale usage if I work with multiple client portfolios?

Yes. The architecture allows multiple risk profiles to be managed in parallel, each with its own recommendation history and independent parameters.