Marquess, a leader in AI-driven asset management, has received prestigious awards such as the 2025 Global Best AI Asset Management Award and the 2024 FinTech Pioneer Award, and plans to expand its global presence and develop tailored solutions for emerging markets.

The company remains client-centric with a long-term outlook, offering customized solutions and transparent communication, which has contributed to its industry recognition.

Marquess Core AI analyzes over 1,200 global data sources in real time, including macroeconomic indicators and social sentiment, achieving prediction accuracy over 92% and executing millisecond-level portfolio adjustments across various asset classes.

During the volatile 2023 market, Core AI's risk intervention mechanisms protected portfolios by automatically triggering stop-loss and stop-profit measures based on liquidity, volatility, and macro risk assessments.

Core AI supports multi-asset classes, including cryptocurrencies, equities, ETFs, and family office scheduling, consistently delivering strong results, especially during periods of high volatility.

Founded in 2019 and headquartered in Singapore, Marquess emphasizes technological innovation and data-driven strategies to lead in structured investing.

On July 8, 2025, Marquess School Global Ltd launched its flagship AI-powered investment system, Marquess Core AI, marking a significant step forward in intelligent wealth management.

The system executes rapid portfolio adjustments across cryptocurrencies, U.S. equities, and ETFs, with select strategies delivering up to 700% annualized returns in cryptocurrencies and around 18% in U.S. stocks, supported by built-in risk management measures.

Marquess operates globally with offices in Singapore, Dubai, New York, and London, and expanded its Southeast Asian hub in 2024 to enhance asset diversification and access to emerging markets.

The company emphasizes transparency through open-source frameworks and real-time dashboards, providing clients full visibility into investment strategies and data inputs.