Prime Vexa 2X applies predictive intelligence to identify allocation opportunities and, at the same time, operates an intelligent stop-loss system that reduces drawdowns before they become significant losses for small and medium-sized companies.
Three layers work together to transform market data into risk-controlled allocation decisions, without relying on constant manual monitoring.
Models continuously process historical series and market signals to estimate short-term scenarios, supporting capital optimization decisions based on probabilities, not intuition.
Exposure limits and exit rules are recalculated with each analysis cycle, allowing drawdown mitigation before variation accumulates beyond what is tolerable for the company's cash flow.
The same analysis structure adjusts to different volumes of available cash, maintaining consistency of criteria between smaller operations and more relevant positions.
Relevant market variations can occur within minutes. The Prime Vexa 2X guardrail system was designed to react within this window, without waiting for a manual review.
Prices, volumes and macroeconomic indicators are collected and normalized on an ongoing basis, forming the basis on which the models operate.
Trained models identify combinations of signals historically associated with increased volatility or trend reversals, generating an updated risk score.
When the risk score crosses the configured limit, the system triggers the intelligent stop-loss and adjusts the exposure, recording the action for later review by the manager.
Highlighted bars represent windows where the risk score exceeded the configured threshold, triggering the guardrail before visual confirmation of a downward trend.
The results of predictive analysis are only valuable when they translate into safer decisions and time recovered for the manager.
The system's focus is to limit losses before seeking gains, prioritizing the continuity of working capital available for operation.
The routine of manual position monitoring is replaced by automatic alerts and adjustments, freeing up the financial team's time for other priorities.
Each recommendation is accompanied by the rationale that originated it, allowing auditing and learning over time, instead of decisions based solely on market perception.
Exposure limits and rules of action are defined by the company and remain visible and adjustable, keeping the manager in control of the strategy.
The predictive layer combines Random Forests models, suitable for capturing non-linear relationships between market variables, with LSTM networks, specialized in identifying dependencies throughout time series. The combination allows the system to recognize both structural and sequential patterns in the analyzed data.
The risk parameters — exposure limits, drawdown tolerance and reassessment frequency — are defined together with the company before activation and can be reviewed at any time, without the need for technical intervention.
The infrastructure follows banking-grade security practices, with data encryption in transit and at rest, and the privacy of each customer's information is treated separately, without sharing between accounts.
We have gathered the most common questions from managers who are considering placing available cash under the management of an AI-driven system.
Speak with a strategy consultant to understand how AI-assisted allocation and smart stop-loss apply to your company's cash profile.