Radiant Kapitos applies systematic data modeling and real-time risk assessment to digital-asset allocations, within a regulatory and security framework designed for the Mauritius market.
Each function operates on a distinct data cycle, from ingestion through to adjustment, and is validated against historical and live market conditions before any recommendation reaches a client account.
Market, on-chain, and macro-economic data streams are processed through statistical models trained to identify recurring volatility patterns, rather than to forecast short-term price direction.
Portfolio exposure is recalculated continuously against defined risk thresholds, with deviations flagged for review before any automated action is taken.
When exposure drifts beyond a client-approved tolerance band, rebalancing proposals are generated and routed through the oversight layer described below.
The process below is designed to remain traceable at every stage, so that each recommendation can be reconstructed and reviewed after the fact.
Exchange feeds, custody records, and macro indicators are collected on a rolling basis and normalized into a common schema.
Normalized data is scored against predictive and volatility models, producing a risk index for each asset class under management.
Risk indices are compared against the tolerance parameters set during onboarding, isolating positions that require attention.
Proposed adjustments are compiled into a structured report and routed to the human-in-the-loop review stage before execution.
No automated recommendation is executed without a defined review checkpoint. This structure is intended to keep the model accountable to human judgment, not to replace it.
Digital-asset custody is treated as a distinct security domain, separated from the analytical layer that generates recommendations.
The majority of client holdings are kept in offline storage, disconnected from internet-facing systems.
Transfers from custody require multiple independent approvals before execution, reducing single-point failure risk.
Every custody and transaction event is recorded in a tamper-evident log retained for compliance review.
Client holdings are tracked on segregated internal ledgers, kept separate from operational accounts.
Radiant Kapitos was designed around a simple constraint: an automated system managing capital should be explainable at every decision point. Rather than presenting a single opaque output, the platform exposes the reasoning chain behind each recommendation, including the data inputs and threshold logic that produced it.
This approach is intended for investors who weigh evidence before acting, and who expect a system managing digital assets to be held to the same scrutiny as a conventional financial instrument.
The underlying engine is the same across account types; the parameters and reporting cadence are adjusted to the mandate.
For treasury and fund structures holding digital assets alongside conventional instruments, allocation is monitored against liquidity and concentration limits set by the mandate.
For individual investors prioritizing capital stability over speculative upside, the model weights downside protection more heavily than growth optimization in its scoring.
Personal and portfolio data is encrypted at rest and in transit, and access is restricted to authorized systems and personnel on a need-to-know basis. Data retention follows the applicable regulatory guidance for financial service providers.
Withdrawal requests are verified through identity confirmation and pass through a time-delayed security check before cold-storage funds are released. This delay exists to reduce the risk of unauthorized transfers.
Models are trained on historical market and on-chain data, then back-tested against out-of-sample periods before deployment. Performance is reviewed on a recurring schedule, and material model changes go through an internal validation step before release.
No. Radiant Kapitos is designed to support risk assessment and allocation decisions; it does not guarantee returns or eliminate market risk. All outputs should be read as decision support, not financial certainty.
Yes. Clients can configure the platform to require manual approval for every proposed adjustment, or to operate within narrower tolerance bands than the default setting.
Access a structured overview of the platform's risk framework, custody architecture, and onboarding requirements. No commitment is required to review the documentation.
Standard onboarding, including identity verification, is typically completed within two to three business days.