Sentinel AI processes large volumes of market data, macroeconomic indicators and historical series to support institutional and private investors in Portugal in assessing risk and building more informed investment theses.
Strategies validated by backtesting with more than a decade of historical market data, before any practical application.
The system combines multiple sources of information to identify relevant patterns and signal risk changes as early as possible, without replacing human assessment.
Natural language processing applied to financial news, reports and announcements, to identify variations in sentiment before they are fully reflected in prices.
Comparison of current behavior with extensive historical series, including periods of high volatility and significant corrections.
Integration of macroeconomic data — inflation, interest rates, employment indicators — as context variables in predictive models.
Permanent monitoring of metrics such as drawdown and realized volatility, with alerts when defined parameters are exceeded.
Each recommendation generated by Sentinel AI is accompanied by a record of the data and factors that contributed to the result. The objective is not to predict with absolute certainty, but to reduce the margin of uncertainty in decision making, keeping the process auditable at any time.
All strategies undergo historical validation before any application. The process is documented in four stages, each with its own control criteria.
Collection and normalization of market data, news and macroeconomic indicators from multiple sources.
Application of statistical and machine learning models to identify patterns and estimate risk scenarios.
Simulation of alternative allocations, considering correlation between assets and previously defined exposure limits.
Presentation of structured recommendations, with a clear indication of the assumptions and limitations of each scenario.
Note on the role of AI: Sentinel AI models are intended to support analysis, not replace decision-making. Backtesting demonstrates the historical behavior of a strategy in different market conditions, but does not guarantee future results — information that we keep present in each report delivered.
The platform is used in different contexts, depending on the profile and objectives of each investor or organization.
In periods of marked instability, the system signals changes in the correlation between assets and suggests allocation adjustments aimed at preserving capital, keeping the investor in control of the final decision.
Detection of changes in sentiment and volume before market saturation, allowing to evaluate opportunities for operational efficiency and potential alpha generation with more timely information.
We've gathered the questions most asked by cautious investors before integrating AI analysis into their decision-making process.
Sentinel AI does not have custody of assets. The platform provides analysis and recommendations; The execution and custody of assets always remain the responsibility of the investor or the custodian institution chosen by him.
The data originates from public markets, financial news sources and publicly released macroeconomic indicators. Each report identifies the categories of data that contributed to the analysis presented.
Predictive models have known limitations when faced with events without historical precedent. For this reason, Sentinel AI combines predictive signals with fixed risk control rules, such as exposure limits, that do not rely solely on model prediction.
No. Backtesting demonstrates how a strategy performed under specific historical conditions. It is a robustness assessment tool, not a guarantee of future performance, and we always present it accompanied by the assumptions used.
Access to each client's information is segmented and restricted to the team involved in the respective analysis. Technical security details are shared directly during the onboarding process.
Request an introductory analysis of your current portfolio. Our team will contact you to clarify the scope of the data used and possible next steps.
Do you prefer direct contact? Write to [email protected]