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FREYA.OS Green Paper
FREYA.OS
Financial Intelligence Operating System Green Paper
Financial Intelligence Operating System — Green Paper
Global Edition | 2026
AI × DATA × STRATEGY × RISK × EXECUTION
This Green Paper is intended to present the product philosophy, technical architecture, governance principles, and long-term development direction of FREYA.OS. It has been prepared in the professional context of the global financial and technology markets for individuals, professional teams, financial institutions, enterprises, and developers across different regions.
This document is a statement of product and technology concepts. It does not constitute, and should not be construed as, an offer or solicitation in respect of any securities, futures, virtual assets, or other financial products, nor as investment advice, a research report, legal advice, tax advice, or any form of assurance of returns.
The actual availability and scope of the research, strategy, risk, execution, and portfolio capabilities described in relation to FREYA.OS are subject to the applicable product version, connected markets, data licensing, arrangements with partner institutions, user permissions, and applicable laws and regulatory requirements.
Where any product or service is offered to financial institutions, professional investors, retail clients, or corporate clients in any jurisdiction, the applicable licensing requirements, client classification and suitability, client-asset rules, anti-money laundering and sanctions compliance, data privacy, cybersecurity, outsourcing, cross-border data, and AI governance requirements should be assessed in accordance with the actual business model and local regulatory framework.
The Global Edition of this Green Paper provides a consistent product and governance framework. Before entering any specific market, separate local assessments should be completed in relation to regulatory mapping, product applicability, client classification, data and cross-border arrangements, taxation, consumer protection, and market communications.
Team biographies, prior employment, partnerships, technical capabilities, data sources, and any quantitative statements should be independently verified and internally approved before external publication.
The central proposition of FREYA.OS is to advance financial AI from a tool that answers questions into an operating system that coordinates the completion of financial tasks within clearly defined permissions and risk boundaries.
| Core Dimension | FREYA.OS Positioning |
|---|---|
| Work Interface | A transition from “people searching for functions” to “people defining objectives and the system organizing capabilities.” |
| Intelligent Collaboration | Seven categories of Agents—Research, Market, Strategy, Risk, Execution, Portfolio, and Learning—operate through a defined division of responsibilities and coordinated collaboration. |
| Financial Closed Loop | Data, insights, strategies, risk controls, authorization, execution, portfolio management, and learning are connected through a continuous feedback process. |
| Human Control | AI is responsible for collaboration and efficiency; people remain responsible for objectives, permissions, risk boundaries, and ultimate control. |
| Platform Direction | An evolution from standalone AI tools toward financial intelligence infrastructure accessible to individuals, professional teams, institutions, and developers. |
In global markets, the trustworthiness of financial AI depends not only on model capabilities, but also on cross-jurisdictional governance, accountability, fairness and transparency, data and privacy protection, human oversight, model risk management, and traceability. As regulatory requirements differ across markets, the FREYA.OS governance framework is based on a consistent set of global core principles, with localized implementation in accordance with local laws, licensing requirements, and market rules.
Governance and Accountability | Establish a clear AI governance structure, defined roles and responsibilities, model and vendor management, material-risk escalation, and approval mechanisms.
Human Oversight and Risk Classification | Apply different levels of human review according to task risk, client impact, and execution authority; retain human confirmation and revocation capabilities for high-impact financial operations.
Fairness, Transparency, and Disclosure | Client-facing AI applications should avoid misleading representations and provide appropriate disclosure of material limitations, data sources, model uncertainty, and the extent of AI involvement.
Personal Data and Data Security | Apply data minimization, purpose limitation, access controls, encryption, retention policies, and vendor risk management, while establishing clear boundaries for Agent access to data and use of tools.
Auditability and Traceability | Maintain traceable records of task origination, data inputs, model and Agent behavior, strategy formation, risk checks, authorization, execution, and subsequent adjustments.
Note: The above provisions are governance design principles presented at the Green Paper level and do not constitute any representation regarding the licensing status or regulatory compliance conclusions of FREYA or any partner.
In recent years, artificial intelligence has rapidly entered the financial sector. From information retrieval and data analysis to content generation and decision support, AI is becoming an increasingly important tool for investors and financial professionals. However, widespread use of tools does not in itself constitute genuine financial intelligence. In complex financial contexts, the fundamental challenge has never been limited to what AI can do. The more important question is whether AI can understand a real financial objective and continuously complete the full process surrounding that objective—from information acquisition and market analysis to strategy formulation, risk control, execution management, and review of outcomes.
Early financial AI primarily served as an auxiliary tool. Users asked questions and AI searched for information; users supplied data and AI performed analysis; users required content and AI generated it; when markets changed, AI offered assessments and recommendations. These capabilities improved the efficiency of information processing, but did not fundamentally change the way financial work was performed. Data, research, strategy, risk, and execution remained dispersed across different tools, systems, and professionals, leaving users responsible for extensive information integration and decision-process coordination.
Genuine financial intelligence must advance further. It must do more than answer a question; it must understand the objective the user is seeking to achieve. It must do more than analyze a dataset; it must connect market information from multiple sources. It must do more than express a view; it must convert that view into a strategy supported by a coherent rationale. It must do more than offer a trading recommendation; it must identify risks, establish boundaries before execution, and continuously adjust and review its approach in light of actual results.
Accordingly, the value of AI in finance is evolving from isolated capabilities to system-level capabilities.
The transition from “using AI” to “commanding AI” is, in essence, a transition from obtaining answers to completing tasks.
Financial markets are highly complex. Prices, liquidity, the macroeconomic environment, corporate fundamentals, market sentiment, and relationships among different assets are continuously changing. A seemingly simple investment decision often involves substantial data and multiple professional processes.
Traditional financial institutions manage this complexity through professional specialization. Researchers identify information and opportunities; market analysts assess market conditions; strategists formulate investment plans; risk professionals conduct risk reviews; traders manage execution; portfolio managers oversee asset allocation and performance; and investment research teams continuously improve the process through review.
The true potential of AI is not simply to replace any one of these roles, but to connect these distributed professional capabilities.
The central question for financial AI is therefore not, “What can AI answer?” but rather, “Can AI complete an integrated financial workflow around a genuine financial objective?”
When a user defines an investment objective, the system should be able to understand the objective and its constraints, proactively obtain relevant data, analyze the market environment, formulate a strategy, and advance the subsequent process within defined risk boundaries. After execution, the results should be returned to the system for performance analysis and strategy review.
This means that AI is no longer merely a tool awaiting instructions, but is becoming an intelligent participant in the financial workflow.
Data remains the foundation of financial intelligence, but data alone is not intelligence. Markets generate vast quantities of information every day, including prices, trading volumes, macroeconomic indicators, corporate financial data, news, market sentiment, and on-chain activity. When such information remains isolated, a greater volume of data may increase, rather than reduce, the complexity of decision-making.
Financial intelligence must connect, process, and interpret distributed data; identify market conditions from that data; identify potential opportunities and risks within those market conditions; and convert the resulting analysis into executable strategies. More importantly, financial markets are never static.
A strategy that was effective in the past may not remain effective in the future, and one successful execution does not mean that the same result can be replicated. A system designed for real financial use must therefore possess continuous feedback and learning capabilities. Every market change, strategy execution, risk event, and investment outcome can become an input for the next cycle of analysis and optimization.
Financial intelligence is therefore not a one-time judgment, but a continuously operating cycle. Data generates insights; insights support decisions; decisions drive actions; actions produce outcomes; and outcomes, in turn, support further learning.
FREYA believes that the ability to use AI is not the same as truly mastering it. When AI remains limited to information search, data interpretation, and content generation, it is still only a tool. Genuine financial intelligence should enter the core financial workflow and, within clearly defined objectives, permissions, and risk boundaries, connect information, analysis, strategy, execution, and outcomes to assist users in completing an integrated financial task.
This is the problem FREYA.OS is designed to address. FREYA.OS is not merely a conversational AI interface. It organizes financial capabilities—including research, market analysis, strategy, risk, execution, portfolio management, and learning—into a collaborative framework through AI Agents. Users no longer need to switch repeatedly between different tools and platforms; they can define a financial objective directly, allowing the system to organize the relevant intelligent capabilities and progress from analysis to action.
Within this process, AI is responsible for understanding, analysis, collaboration, and execution, while people retain control over objectives, permissions, and final decisions. AI does not replace human judgment; it enables every person to have access to a continuously operating financial AI team. More importantly, FREYA.OS is not intended to be a one-time decision tool, but a continuously operating closed loop of financial intelligence.
After a user defines an objective, the system interprets the requirement, multi-source data enters the financial intelligence layer and produces market insights, and AI Agents generate strategies based on the objective and market environment. The Risk Agent then performs its checks, and the process advances to execution only after the predefined boundaries have been satisfied and the required authorization has been obtained. Execution results are subsequently returned to portfolio management and performance analysis, where they become the basis for the next cycle of strategy evaluation and optimization.
Financial work thereby moves from traditional one-way information processing to a continuously operating intelligent system:
Data → Intelligence → Decision → Action → Learning
This is FREYA’s core understanding of financial intelligence: from answering questions to understanding objectives; from analyzing information to formulating strategies; from offering recommendations to coordinating execution; and from one-time judgments to continuous learning.
FREYA.OS brings AI into the financial workflow and connects distributed financial capabilities into an intelligent system that can operate continuously, remain controllable, and evolve over time.
Brand and Product Architecture: FREYA is the global brand and business system; FREYA.OS is the Financial Intelligence Operating System and technology foundation; and FREYA GLOBAL is the global financial services and market network.
FREYA.OS is a Financial Intelligence Operating System designed for global markets. With AI Agents at its core, FREYA.OS connects market data, financial analysis, strategy capabilities, risk management, and execution within a unified system, enabling different financial capabilities to collaborate around user objectives. Its purpose is not simply to provide more financial information or tools, but to establish an intelligent system capable of understanding requirements, invoking capabilities, and coordinating workflows. Starting from a financial objective, the system connects the required data, analysis, and strategy capabilities and advances the subsequent work in accordance with established permissions and risk boundaries.
FREYA.OS thereby serves as the core intelligence layer connecting users, AI, data, financial capabilities, and market execution, providing individuals, professional teams, and institutions with a unified financial intelligence work environment.
The interaction model of FREYA.OS is evolving from a traditional “function-based interface” toward an “objective-based interface.”
Users do not need to first understand complex financial tools, data sources, and operating procedures and then complete research, analysis, and strategy formulation one step at a time. Instead, they can directly express their financial objectives, investment direction, and relevant constraints, and FREYA will organize the appropriate intelligent capabilities in response. Once a task enters the system, different AI Agents participate according to their respective responsibilities. The Research Agent organizes relevant information and research materials; the Market Agent analyzes market conditions; the Strategy Agent develops an appropriate approach; the Risk Agent assesses relevant risks; and the Execution and Portfolio Agents manage the subsequent stages.
This approach shifts financial operations from “people searching for functions” to “the system understanding objectives,” while enabling more natural interaction with AI across complex financial work. FREYA makes financial intelligence a work interface that supports direct dialogue and collaboration. FREYA.OS does not assign all financial work to a single AI; it establishes an AI team composed of specialized Agents.
Research, Market, Strategy, Risk, Execution, Portfolio, and Learning Agents correspond to different professional functions within financial work. Each Agent has a relatively clear scope of responsibility, while sharing relevant data, context, and work products through FREYA.OS.
Once a task enters the system, different Agents can collaborate sequentially along the workflow rather than requiring a single AI to address every issue at once. This professional specialization enables complex financial work to be decomposed, coordinated, and advanced on a continuous basis.
The value of FREYA.OS therefore lies not only in the capability of any individual AI, but in whether multiple Agents can operate as a genuinely collaborative financial AI team.
For AI to participate meaningfully in financial work, it must be supported by a continuous, reliable, and multidimensional data foundation. FREYA.OS connects market and financial data from multiple sources, including global market data, macroeconomic information, corporate fundamentals, derivatives, digital assets, news, and market sentiment, and converts such data into financial information that AI Agents can understand and use.
Within the system, data is no longer presented passively. It becomes a core input for research, market analysis, strategy generation, risk management, and portfolio management. As markets continue to change, FREYA.OS enables relevant information to flow continuously into the workflow, allowing AI Agents to operate in an evolving market environment rather than relying solely on static data.
In FREYA.OS, financial intelligence does not end with the generation of an analytical result or strategy recommendation. Once a strategy has been formulated, it must be assessed against the user’s objectives, capital conditions, and risk requirements. The Risk Agent can assess positions, volatility, stop-loss conditions, concentration, and other relevant factors to ensure that subsequent actions remain within established risk boundaries.
A strategy proceeds to execution only after the relevant conditions have been satisfied and the required authorization has been obtained. Execution results and market changes are then returned to the system for portfolio management, performance analysis, and subsequent strategy adjustment.
FREYA.OS therefore manages strategy, risk, and execution within a unified workflow. Financial intelligence must do more than produce an idea; it must withstand risk review and advance toward actual action in accordance with established rules.
FREYA.OS is not designed to replace people entirely.
As AI enters important areas such as strategy formulation and financial execution, human objective-setting, operating permissions, and risk control become even more important. FREYA.OS therefore combines the autonomous collaborative capabilities of AI with human control, establishing appropriate permission and confirmation mechanisms at different stages of the workflow. AI can process large volumes of information, analyze markets, coordinate Agents, and advance workflows; however, material decisions, risk settings, and actual execution are governed by predefined rules and authorization mechanisms.
This model gives AI sufficient capability to complete complex work while ensuring that users retain control over their objectives, permissions, and final decisions at all times.
AI is responsible for collaboration and efficiency; people are responsible for direction and control.
When research, data, strategy, risk, execution, and portfolio management are connected within a single system, financial work is no longer a series of one-time operations; it becomes a continuously operating intelligent process. Market data enters the system and produces analysis and insights; Agents generate strategies based on defined objectives; risk capabilities conduct checks; and execution proceeds after authorization has been obtained. The resulting outcomes are returned to the system and, following performance analysis and strategy review, become the foundation for the next cycle of work. FREYA.OS thereby establishes a continuously operating financial cycle:
Data → Intelligence → Decision → Action → Learning
Every market change, strategy execution, and resulting outcome can become an input for continuous system optimization.
FREYA.OS is not an AI tool used only when required. It is a Financial Intelligence Operating System capable of operating continuously in response to changing markets, strategies, and user needs.
From understanding objectives to organizing intelligence; from data insights to strategy execution; and from human control to continuous system operation—FREYA.OS is establishing a new mode of financial intelligence work for global markets.
FREYA.OS is being developed by professionals from finance, asset management, quantitative trading, artificial intelligence, software architecture, corporate operations, and other fields. Team members bring experience in global financial markets, AI technology, enterprise management, and product implementation, creating a multidisciplinary structure that integrates financial expertise, artificial intelligence, and commercial operations.
The team’s core capabilities are not confined to a single technology or financial discipline. They connect financial market expertise, AI Agents, quantitative trading, systems architecture, and enterprise execution to provide long-term support for FREYA.OS product development, technological advancement, and global business expansion.
Mr D
CEO
Professional Overview
Mr. D has more than 10 years of experience in global asset management and quantitative finance, with a professional background in cross-market investment research, quantitative strategies, and asset management. He previously held positions at Millennium Management and Two Sigma, where he accumulated experience in international financial markets and professional investment institutions.
His professional experience spans financial market research, quantitative investment, asset management, and investment strategy, and he has a detailed understanding of asset allocation, strategy formulation, and risk management across different market environments.
Professional Experience
Mr. D has focused on global financial markets and asset management throughout his career and has accumulated extensive experience in quantitative finance and investment strategy. His career has involved global market research, quantitative analysis, and investment management, providing practical understanding of financial market data, investment decision processes, and institutional-grade asset management systems.
His prior experience at Millennium Management and Two Sigma provides an important foundation for FREYA.OS in combining professional financial market capabilities with artificial intelligence technology.
Current Responsibilities at FREYA
As Founder and CEO of FREYA, Mr. D is responsible for the company’s overall strategic direction and long-term development, with a primary focus on financial AI, global market expansion, and institutional business development.
Primary Responsibilities:
Global Strategy and Corporate Development
Financial AI Product and Capability Direction
Institutional Business Development
Global Asset Management Business
Financial Markets and Investment Strategy Direction
Core Partnerships and Institutional Network
Long-Term FREYA.OS Product and Ecosystem Strategy
Core Competencies
Global Asset Management | Quantitative Finance | Investment Research | Cross-Market Analysis | Investment Strategy | Asset Allocation | Institutional Business | Financial AI Strategy
Mr. D’s principal responsibility is to connect professional financial market experience with the artificial intelligence capabilities of FREYA.OS and to define the platform’s long-term direction from a financial AI product toward global financial intelligence infrastructure.
Paul Marino
COO
Professional Overview
Paul Marino is an entrepreneur and executive with more than 10 years of experience in corporate growth, operations management, and technology projects. His experience includes cross-market business development, partner management, sales strategy, team building, and technology project implementation.
His career spans affiliate marketing, real estate, corporate management, and artificial intelligence. He has practical end-to-end experience in business development, client-network creation, team formation, and the execution of large-scale technology projects.
Professional Experience
2015–2020 | U.S. Corporate Affiliate Marketing
Managed affiliate marketing operations for U.S. companies, leading partner expansion and revenue growth, establishing and managing partner networks, and overseeing commercial partnerships, performance management, and growth strategies.
2020 | Dubai Real Estate Market
Entered the Dubai real estate market with responsibility for market expansion and client development, progressively establishing a stable client portfolio and market network while gaining cross-cultural and international business experience.
2023 | Establishment of a Real Estate Agency
Established an independent real estate agency and assumed full responsibility for company operations, sales strategy, and business development, including client management, team collaboration, market expansion, and day-to-day operations.
2025 | Large-Scale AI Project
Served as team lead for a large-scale AI project, managing more than 30 cross-functional professionals, coordinating functional teams and project resources, and advancing execution and delivery under demanding timelines.
During this period, he further developed experience in AI project management, cross-team coordination, and technology product implementation, combining enterprise operating capabilities with AI technology program management.
Current Responsibilities at FREYA
As Co-Founder and COO of FREYA.OS, Paul is responsible for day-to-day company operations, product execution, and cross-functional team coordination, advancing FREYA.OS from product design and technology development into practical business applications.
Primary Responsibilities:
Overall Corporate Operations Management
FREYA.OS Product Execution
Cross-Functional Team Coordination
AI Product Implementation
Business Development and Market Execution
Team Building and Talent Coordination
Integration of Partners and External Resources
Project Progress and Delivery Management
Global Business Development Support
Paul’s core role is to translate the technology and product capabilities of FREYA.OS into practical business and financial applications, connecting product, technology, teams, and markets.
Core Competencies:
Corporate Growth | Operations Management | Team Building | Business Development | Product Execution | Project Management | Partner Management | AI Project Management | Cross-Functional Collaboration | Enterprise Scaling
Bogdan Rotund
Technical Advisor
Professional Overview
Bogdan Rotund has more than 15 years of experience in distributed systems and low-latency architecture, with a long-standing focus on high-performance software systems, distributed architecture, and financial technology.
He previously held positions at Endava, Paddy Power Betfair, SDL, and Siemens, and has practical experience in large-scale enterprise software systems and high-performance technology architecture.
Professional Experience
His technical background includes distributed systems design, low-latency architecture, high-performance data processing, and trading systems, together with experience applying complex technology architectures to real business environments.
In quantitative trading and financial technology, his expertise covers the complete technology chain from data processing and strategy computation to trade execution and risk management, with particular emphasis on system performance, stability, and real-time processing.
Current Responsibilities at FREYA
He currently provides technical advisory support to FREYA.OS in AI and quantitative trading systems, assisting the platform in establishing a high-performance technology architecture for financial markets.
Primary Areas of Support:
Quantitative Trading System Architecture
Distributed Systems Design
Low-Latency Trading Architecture
Market Data Processing
AI-Driven Quantitative Strategy Systems
Trade Execution Capabilities
Risk Management Systems
High-Performance Financial Infrastructure
His professional experience supports FREYA.OS in connecting AI capabilities with real-world financial trading and market infrastructure, enabling the platform to develop toward a high-performance and scalable financial intelligence architecture.
Core Competencies
Distributed Systems | Low-Latency Architecture | Quantitative Trading | Market Data | Trade Execution | Risk Management | High-Performance Systems | Financial Technology Infrastructure
Dmytro Yemelianov
Technical Advisor
Professional Overview
Dmytro Yemelianov has more than 15 years of experience in software architecture and enterprise automation. He previously held positions at Autodesk and AMC Bridge and has experience developing large-scale software systems, enterprise applications, and technology architectures.
His professional focus has expanded from traditional enterprise software architecture to large language models, AI Agents, cloud systems, and enterprise automation, with an emphasis on integrating artificial intelligence capabilities into existing enterprise workflows.
Professional Experience
Dmytro has extensive experience in large-scale software systems and enterprise technology projects, with a comprehensive technical background spanning systems architecture design, software development, and enterprise automation.
With the rapid development of generative AI and Agent technologies, his professional focus has further expanded to LLMs, AI Agents, cloud architecture, and enterprise automation, including the integration of AI models and Agent capabilities into scalable enterprise systems.
Current Responsibilities at FREYA
He currently provides advisory support to FREYA.OS in AI systems architecture and platform technology, with a principal focus on establishing a scalable AI Agent architecture and financial intelligence platform.
Primary Areas of Support:
Large Language Model Architecture
AI Agent System Design
Agent Collaboration and Integration
Cloud Systems Architecture
Enterprise Automation
AI Workflow Design
Platform Scaling
Integration of AI Capabilities and Financial Applications
His expertise supports FREYA.OS in integrating different AI Agents, financial data, and business capabilities within a unified platform, establishing the technical foundation for future multi-agent collaboration and enterprise-grade financial AI applications.
Core Competencies
LLMs | AI Agents | Software Architecture | Cloud Systems | Enterprise Automation | AI Workflows | Systems Integration | Platform Scaling
The core of FREYA.OS is not to build an AI with more functions, but to reorganize specialized financial roles into an AI team capable of coordinated collaboration.
Traditional financial institutions are able to manage complex market environments because they rely on professional specialization across research, market analysis, strategy, risk, trading, and portfolio management. Different roles perform specific tasks and work together to complete the full investment process.
FREYA.OS introduces this professional specialization into an AI system. Through multiple AI Agents with distinct functions, each Agent assumes responsibility for a specific area of financial work while sharing data, context, and task results. When a user defines a financial objective, FREYA.OS can organize the appropriate Agents according to the task, creating a continuous workflow across research, analysis, strategy, risk, execution, portfolio management, and learning.
FREYA’s AI is therefore not “one AI doing everything,” but a financial AI team whose members possess distinct strengths and collaborate with one another.
A single AI can answer questions, analyze data, and provide recommendations, but financial work is rarely a single question. It is usually a sequence of interconnected professional tasks.
For example, an investment decision may first require an understanding of the macroeconomic environment and market conditions, followed by analysis of assets and trading opportunities. After a strategy is developed, risks, position sizing, and execution conditions must be assessed. Once a trade is completed, portfolio management, performance analysis, and strategy review are still required. A deviation at any stage may affect the final outcome.
FREYA.OS therefore adopts a multi-agent architecture that assigns different financial functions to specialized AI Agents. Each Agent has a clearly defined scope of responsibility and can exchange information, share work products, and hand off tasks within a unified system.
This architecture is not simply intended to increase the number of AI systems. It is designed to give AI a structure of specialization, collaboration, and workflow more closely aligned with that of a professional team.
By progressing from one AI answering questions to multiple AI Agents completing tasks together, FREYA.OS advances AI from a “tool” to a “team.”
FREYA.OS organizes its financial intelligence team around seven core AI Agents. Each Agent corresponds to an important financial function, can independently complete professional tasks, and can collaborate with other Agents as required by the workflow.
01 | Research Agent
Establish market understanding and identify information and opportunities that merit attention.
The Research Agent operates at the front end of financial research, continuously organizing and analyzing material information that may affect markets, including macroeconomic developments, industry trends, corporate fundamentals, major events, and market factors.
When confronted with large volumes of fragmented market information, the Research Agent does more than retrieve information. It interprets data from different sources within the relevant market context, helping to identify material changes, potential drivers, and market signals requiring further investigation.
Within the actual workflow, the Research Agent can provide the research foundation for other Agents. For example, when a material market development occurs, the Research Agent can analyze the background and potential implications of the event, providing relevant support for the Market Agent’s market assessment and the Strategy Agent’s strategy analysis.
Core Capabilities: Macroeconomic Research | Industry Research | Corporate Fundamentals | Event Analysis | Factor Research | Information Organization
02 | Market Agent
Understand what is happening in the market and where the market may be heading.
The Market Agent focuses on real-time and historical market conditions, analyzing multidimensional information including prices, trading volumes, volatility, trends, and market sentiment to help the system identify different market environments.
Financial markets do not remain in a single state over time. Markets may be rising, declining, range-bound, or highly volatile, and each market regime has a direct impact on strategy effectiveness and risk levels.
The role of the Market Agent is to convert continuously changing market data into contextualized market understanding, helping other Agents assess the prevailing environment and relevant market conditions.
When market conditions change, the Market Agent can return new market signals to the broader workflow, where they become important inputs for strategy adjustment and risk assessment.
Core Capabilities: Market Regime Analysis | Trend Identification | Price Analysis | Volatility Analysis | Market Sentiment | Market Signals
03 | Strategy Agent
Convert market insights into specific strategies.
The Strategy Agent is responsible for converting research findings, market conditions, and user objectives into strategy proposals.
A strategy involves more than predicting price direction. It must consider the traded instrument, entry conditions, exit conditions, holding period, capital allocation, and other strategic parameters. The Strategy Agent can generate and compare different strategy proposals for different market environments and adjust and optimize the relevant parameters.
Where further validation is required, a strategy may also enter a backtesting and evaluation process to assess its performance under different market conditions.
The role of the Strategy Agent is to convert a “market view” into a “strategy that can be validated and executed.”
Core Capabilities: Strategy Generation | Parameter Optimization | Trading Signals | Strategy Comparison | Backtesting Analysis | Strategy Evaluation
04 | Risk Agent
Establish clear risk boundaries before every action.
The Risk Agent conducts risk analysis of strategies and portfolios, ensuring that financial actions are not assessed solely in terms of potential returns, but remain subject to appropriate risk constraints.
The system can review position size, asset concentration, volatility, risk exposure, stop-loss conditions, and other risk indicators to identify circumstances that may exceed predefined limits.
The Risk Agent does not wait until a trade has been completed before monitoring risk. It can intervene before strategy formation and execution, serving as an important control function within the financial workflow.
When the market environment, strategy conditions, or portfolio status changes, relevant risk signals can re-enter the system and trigger further assessment or strategy adjustment.
Core Capabilities: Position Monitoring | Risk Exposure | Value-at-Risk Analysis | Stop-Loss Management | Concentration Management | Compliance Checks
05 | Execution Agent
Convert an approved strategy into actual market action.
The Execution Agent manages the process through which a strategy enters the market, connecting to the relevant trading and execution infrastructure and processing trading instructions in accordance with established conditions.
Actual market execution is not simply a matter of “pressing the trade button.” Trading time, execution price, liquidity, slippage, order splitting, and execution route can all affect the final result.
The Execution Agent can therefore support intelligent routing, algorithmic execution, and trade management based on strategy and market conditions, while returning actual execution results to the system.
Strategies are thereby able to move beyond the analysis interface and progress toward actual operation within the applicable permissions and rules.
Core Capabilities: Intelligent Routing | Algorithmic Execution | Order Management | Slippage Control | Trade Management | Execution Feedback
06 | Portfolio Agent
Move from individual trades to integrated asset management.
The Portfolio Agent focuses not only on a single asset or trade, but on the allocation, structure, and performance of the entire portfolio.
When a user holds different assets, strategies, or market positions at the same time, analyzing each investment independently is insufficient to reflect overall risk. The Portfolio Agent can conduct a holistic portfolio analysis across asset allocation, position structure, correlations, risk exposure, and performance.
As markets and asset prices change, the original portfolio allocation may gradually diverge from its intended objectives. The Portfolio Agent can help identify areas requiring adjustment and support rebalancing and performance attribution.
This extends FREYA’s financial intelligence from “single-strategy management” to “integrated asset management.”
Core Capabilities: Asset Allocation | Portfolio Management | Rebalancing | Risk Allocation | Performance Analysis | Performance Attribution
07 | Learning Agent
Ensure that every outcome informs the next cycle of work.
The Learning Agent is responsible for the final stage of the financial workflow, returning market outcomes, strategy performance, and actual execution data to the system.
The effectiveness of a strategy cannot be assessed on the basis of a single result, and whether a model should continue to be used must be evaluated across different market environments and over longer-term datasets. The Learning Agent can backtest strategies, conduct performance reviews and model evaluations, and analyze strategy performance under different conditions.
More importantly, learning is not independent of the work of other Agents. New findings generated through review can be returned to the Research, Market, Strategy, and Risk Agents as inputs for the next cycle of work.
FREYA.OS therefore does more than complete a single financial task; it gives the entire financial intelligence team the ability to review and optimize its work continuously.
Core Capabilities: Backtesting | Performance Review | Model Evaluation | Strategy Optimization | Outcome Analysis | Continuous Iteration
The seven Agents are not seven unrelated AI tools. Together, they constitute the financial intelligence collaboration framework of FREYA.OS.
The Research Agent establishes the research foundation; the Market Agent interprets market conditions; the Strategy Agent formulates strategies; the Risk Agent establishes risk boundaries; the Execution Agent advances market execution; the Portfolio Agent manages the overall asset base; and the Learning Agent returns outcomes to the system to support the next cycle of analysis and optimization.
When these capabilities operate within the same system, financial work can move from the traditional model of “one person connecting multiple tools” to “an AI team collaborating to complete the task.”
FREYA.OS does more than make AI more intelligent; it enables AI to adopt professional specialization within financial work.
Seven AI Agents. One financial AI team of your own.
The core of FREYA.OS is not to have one AI handle all financial work simultaneously, but to enable specialized AI Agents to collaborate around the same financial objective.
In real financial work, research, market analysis, strategy formulation, risk management, and trade execution are interconnected, yet each requires distinct professional expertise. Even a highly capable individual Agent may find it difficult to maintain sufficient professional depth and risk-control capability across every stage. FREYA.OS therefore separates different financial functions into specialized Agents with clearly defined responsibilities and connects them through shared context, data, and workflows.
When a financial task enters the system, the Research Agent first establishes the information foundation relevant to the objective. The Market Agent then identifies the market environment, trends, and volatility conditions. The Strategy Agent formulates an appropriate strategy based on market conditions and the user’s objective. The Risk Agent independently reviews the strategy, identifies potential risks, and assesses compliance with predefined boundaries. Only after the relevant authorization has been obtained does the Execution Agent advance the transaction and related operations.
After execution, the Portfolio Agent assumes responsibility for subsequent position, allocation, and performance management, while the Learning Agent incorporates market changes, strategy performance, and execution results back into the analytical framework for backtesting, performance review, and subsequent optimization.
The seven Agents are therefore not independent AI tools, but a financial AI team that collaborates continuously around the same task:
Research → Market → Strategy → Risk → Execution → Portfolio → Learning
The essential feature of this collaborative model is that each Agent not only completes its own work, but also passes its work product to the next stage, enabling the information, judgments, and outcomes from one stage to become the foundation for the next.
FREYA.OS therefore reorganizes financial work that would otherwise need to be completed sequentially by different people, systems, and tools into an intelligent workflow advanced collaboratively by AI Agents.
More importantly, this collaboration does not mean that every stage is completed autonomously by AI. The capabilities, data-access scope, and operating permissions of different Agents can be managed according to the requirements of each task, while human confirmation and risk-control mechanisms can be retained for material decisions and actual execution.
FREYA.OS establishes a financial AI team characterized by professional specialization, connected context, and collaborative capability.
Agent collaboration within FREYA.OS ultimately returns to one central question: how can a genuine financial objective be understood, analyzed, executed, and continuously managed within the system?
In a traditional financial workflow, users generally need to search for information independently, use research tools, market-data platforms, analytical tools, strategy systems, and trading platforms separately, and then return to portfolio management. The different stages lack a unified context, requiring users to perform extensive information organization and workflow coordination themselves.
FREYA.OS reorganizes this process.
A task begins with the user’s objective. Users can directly express the financial objective they wish to achieve, including investment direction, asset class, time horizon, capital conditions, and acceptable risk range. FREYA AI interprets these requirements and converts objectives expressed in natural language into task conditions that can enter the financial workflow.
Once the task has been established, the Research Agent begins collecting and organizing market and financial information relevant to the objective. The Market Agent then analyzes current market conditions and identifies trends, volatility, liquidity, and other material factors that may affect the decision.
After sufficient market context has been established, the Strategy Agent formulates a strategy based on the user’s objective and the market environment. The strategy is not limited to a single trading signal; it may include asset selection, entry and exit conditions, position sizing, strategy logic, and the corresponding execution conditions.
Once the strategy has been formulated, the Risk Agent reviews the proposal. Based on predefined risk policies and user conditions, the system can assess position size, risk exposure, concentration, volatility, potential drawdown, and other factors to determine whether the strategy is consistent with the established risk boundaries.
Where actual trading or another materially consequential operation is involved, the system can enter a human-confirmation stage. Users can review the relevant strategy, risk information, and proposed operation and proceed to execution only after their requirements have been satisfied and the relevant authorization has been provided.
The Execution Agent then advances the relevant operations in accordance with the approved strategy and authorization conditions, while monitoring the execution process, including transaction status, execution deviations, and market changes. Completion of execution does not mark the end of the process.
The Portfolio Agent updates positions and asset-allocation status based on actual results and analyzes changes in the portfolio and its performance. The Learning Agent then reviews and evaluates the strategy and outcomes, returning actual market performance to the system as a basis for the next cycle of research, strategy adjustment, and risk assessment.
A complete financial task can therefore follow this sequence:
User Objective → FREYA AI → Research → Market Analysis → Strategy Generation → Risk Review → Human Confirmation → Execution → Portfolio Management → Learning and Review
The purpose of this process is not merely to introduce more AI into financial work, but to ensure that each stage receives the results of the preceding stage and continues to pass its own information to the next.
A financial task therefore ceases to be a collection of disconnected tools and becomes a complete intelligent workflow incorporating context, permissions, risk controls, and feedback on outcomes.
For the user, the starting point may be only an objective. For FREYA.OS, that objective can be converted into a financial task completed collaboratively by multiple specialized AI Agents.
Beginning with an objective and proceeding through intelligent collaboration, the process ultimately forms a financial workflow that is understandable, controllable, and capable of continuous optimization.
The technology system of FREYA.OS is not composed solely of artificial intelligence models, data platforms, or trading systems. It is centered on the financial workflow and connects interaction, AI Agents, financial intelligence, global data, and underlying infrastructure into an integrated technology system.
Its purpose is to ensure that AI possesses not only understanding and analytical capabilities, but can also enter actual financial workflows with the support of reliable data, financial models, risk controls, and permission management. Beginning with a user-defined objective, the process advances through Agent collaboration, data analysis, strategy generation, and risk review before connecting progressively to execution, portfolio management, and outcome feedback.
FREYA.OS therefore adopts a five-layer financial intelligence architecture that connects upper-layer user interaction with lower-layer financial data and technology foundations. The resulting financial intelligence system combines intent intake, intelligent processing, data support, risk control, and continuous operation.
The Interaction Layer is the point at which users connect with FREYA.OS. It converts users’ natural-language input, financial requirements, and operating intentions into tasks that the system can understand and execute. FREYA.OS does not require users to first understand complex financial tools, data interfaces, or system workflows. Instead, through more natural modes of interaction, users can directly submit requests relating to market research, strategy analysis, asset allocation, or portfolio management through interfaces including the FREYA App, Web, and API. For individual users, the FREYA App and Web provide direct access to financial intelligence. For professional traders and enterprises, APIs and enterprise interfaces enable FREYA’s financial intelligence capabilities to be integrated into existing work environments and business systems.
The core purpose of the Interaction Layer is not merely to enable users to operate the system, but to convert human financial intent into work tasks that AI can understand. Through user identity and permissions, task and context management, and intelligent interaction mechanisms, relevant requirements can advance continuously within a clear and controlled environment. The system then presents research analysis, strategy proposals, and execution results back to users, creating a complete interaction process from intent input and AI understanding to task progression and outcome feedback. The Interaction Layer thereby becomes the principal interface connecting users with the financial intelligence capabilities of FREYA.OS.
The AI Agent Layer is the core intelligent collaboration layer of FREYA.OS.
Rather than using a single AI model to handle every financial issue, FREYA.OS divides financial work into specialized functions completed collectively by multiple AI Agents with clearly defined responsibilities.
The current core architecture includes seven financial AI Agents:
Research Agent Responsible for macroeconomic, industry, event, factor, and market research.
Market Agent Analyzes market regimes, trends, volatility, liquidity, and market sentiment.
Strategy Agent Formulates strategy proposals based on market conditions and user objectives, and conducts strategy analysis and optimization.
Risk Agent Responsible for risk identification, position analysis, risk exposure, and review against relevant risk rules.
Execution Agent Responsible for strategy execution, intelligent routing, trade management, and monitoring of the execution process.
Portfolio Agent Responsible for asset allocation, position management, rebalancing, and performance analysis.
Learning Agent Responsible for backtesting, performance review, model evaluation, and continuous strategy optimization.
The seven Agents are not seven independently operated tools. They collaborate within the same financial task by sharing task context, relevant data, and work results through FREYA.OS. This architecture allows different Agents to hand off work progressively in accordance with the logic of financial operations, advancing the system from “a single AI answering questions” to “a financial AI team collaborating to complete tasks.”
The Financial Intelligence Layer is the core capability layer connecting AI Agents with underlying financial data. Raw data cannot itself produce financial decisions. FREYA.OS must process, analyze, and model market data and then convert it into financial intelligence that AI Agents can understand and invoke. This layer provides the core analytical and decision-support capabilities required for financial work, including market analysis, strategy engines, backtesting systems, scenario analysis, and risk models. Market analysis converts information from different sources into an understanding of market conditions, including trends, volatility, liquidity, market sentiment, and cross-asset relationships. The strategy engine combines market insights with user objectives to formulate different strategy proposals and supports conditional analysis, parameter adjustment, and strategy comparison. Backtesting and scenario analysis use historical and simulated data to evaluate strategy performance and potential risks under different market environments.
On this foundation, the Financial Intelligence Layer also provides asset allocation, portfolio analysis, performance analysis, and strategy evaluation. It incorporates different assets and strategies into an integrated portfolio and supports allocation, rebalancing, risk assessment, and continuous optimization. Risk models provide quantitative analysis of strategies and portfolios across dimensions including position size, risk exposure, concentration, volatility, and potential drawdown. The core function of this layer is to convert underlying data into professional financial capabilities that Agents can invoke continuously, enabling AI not merely to “understand language,” but to acquire the capabilities required to participate meaningfully in financial workflows including research, analysis, strategy, risk, and portfolio management.
Data is the foundation of financial intelligence. Global financial markets continuously generate many different types of data, and complex relationships exist across assets, markets, and time horizons. FREYA.OS must connect financial data distributed across multiple sources to establish a global financial data foundation that supports the continuous work of AI Agents. The Data Layer covers multidimensional financial information including market data, macroeconomics, corporate fundamentals, derivatives, equities, foreign exchange, precious metals and commodities, digital assets, real-world assets, news, events, market sentiment, and on-chain data. Through data processing and integration, it provides continuous and structured information inputs for different financial activities.
These data are not used solely to display market quotations. They enter workflows for research, market analysis, strategy generation, risk management, and portfolio management. For example, market-price and transaction data can support the Market Agent in identifying trends and volatility; macroeconomic and corporate-fundamental data can provide an analytical foundation for the Research Agent; derivatives data can help assess market expectations and potential risks; and news, market sentiment, and on-chain data can supplement information not fully reflected in traditional quantitative datasets. As data continue to update, AI Agents receive new information in dynamic market environments, enabling financial intelligence to move beyond static datasets and continuously perceive and interpret market changes. The core value of the Data Layer is to establish for FREYA.OS a continuously updated, multi-market, multi-asset, and multidimensional financial information foundation that provides reliable data support for the upper financial intelligence and AI Agent layers.
The Infrastructure Layer provides the underlying technology support for FREYA.OS and is responsible for ensuring system stability, security, scalability, and governability. As AI enters financial workflows, the system may process not only general information, but also market data, user asset information, trading instructions, and other material financial operations. Financial AI architecture must therefore address more than model capabilities; it must also establish comprehensive security, identity, permission, and governance mechanisms. The Infrastructure Layer includes core capabilities such as cloud computing, system security, data encryption, identity authentication, permission management, access controls, system monitoring, and operational records, providing a stable and reliable operating environment for upper-layer AI Agents, financial intelligence, and enterprise services. AI governance mechanisms also manage model use, Agent behavior, decision processes, and related operations, enhancing the controllability, traceability, and continuing manageability of financial AI in practical applications.
The five-layer architecture of FREYA.OS does not consist of five independent technology modules. It is a complete financial intelligence system whose layers are vertically connected and operate collaboratively. The Interaction Layer receives user objectives and financial requirements; the AI Agent Layer interprets tasks and organizes specialized intelligence; the Financial Intelligence Layer provides core capabilities including market analysis, strategy, risk, and asset allocation; the Global Data Layer supplies continuous market and financial information; and the Infrastructure Layer provides a secure, stable, and controlled technology environment for the entire system. Through coordinated operation across these five layers, FREYA.OS connects user intent, AI Agents, financial capabilities, global data, and underlying technology foundations into an integrated system, enabling financial intelligence to progress from information understanding to analysis, decision-making, execution, and continuous management.
This produces the following sequence:
User Intent → AI Agents → Financial Intelligence → Global Data → Infrastructure
When execution results return to the system, they re-enter the financial intelligence and Agent collaboration processes, creating a continuously operating feedback mechanism.
The five-layer architecture of FREYA.OS is therefore more than a technical hierarchy. It connects users, AI, financial capabilities, data, and infrastructure into an integrated financial intelligence operating environment.
Its ultimate purpose is to ensure that FREYA.OS possesses not only AI understanding capabilities, but also the data foundation, professional capabilities, execution capabilities, and control capabilities required to enter financial workflows, providing individuals, professional teams, and financial institutions with a scalable technology foundation for financial intelligence.
Financial intelligence within FREYA.OS is not based on simply supplying large volumes of market data to AI and asking a model to generate analytical text. It establishes an integrated workflow from data intake and processing to market understanding, insight formation, and strategy generation. Based on user objectives and financial tasks, the system organizes the required data and uses AI Agents to analyze and integrate the information, progressively converting raw information into structured capabilities that can support financial decision-making. Once a strategy has been formulated, the system advances to risk assessment and execution, enabling financial intelligence to progress from “understanding the market” to “forming an action.”
When a financial task begins, FREYA.OS first identifies and organizes the types of data required according to the user’s objective and the task requirements. Different financial tasks require different data. Equity research, for example, may require the integration of market data, corporate fundamentals, the macroeconomic environment, news events, and market sentiment, while digital-asset analysis may additionally incorporate on-chain data, trading volume, and capital flows. Data access is therefore not merely a search for information; it is the selection and organization of relevant information around a specific financial task.
Financial data from different sources have different formats, time horizons, and data characteristics. FREYA.OS organizes, standardizes, and integrates the relevant data, converting market information distributed across multiple sources into analytical inputs that can be used within a unified financial workflow. Processed data can then be transformed into market indicators, event information, asset characteristics, and other financial analytical data, providing a foundation for subsequent market understanding by AI Agents.
After the relevant data have been obtained, different AI Agents interpret the market from different perspectives according to their responsibilities. The Market Agent can analyze market conditions, price trends, volatility, and market sentiment, while the Research Agent combines macroeconomic, industry, corporate, and event information to establish a more complete research context. The objective at this stage is not simply to predict a single price, but to understand the current market environment from multiple dimensions, identify what is occurring in the market, and assess the factors influencing asset prices and market expectations.
After information from different sources has been integrated and analyzed, the system can identify material market signals, structural changes, and potential risks and form financial insights for use in subsequent decisions. These insights may include market trends, relative asset strength, volatility conditions, market sentiment, macroeconomic factors, material events, and cross-asset relationships. Financial insights are not themselves trading decisions; they convert fragmented market information into a more structured basis for judgment and provide the foundation for strategy formulation.
Once the market environment and relevant financial factors have been sufficiently understood, the Strategy Agent can generate an appropriate strategy proposal based on the user’s objective, investment conditions, and prevailing market conditions. The strategy may include trading direction, entry and exit conditions, position recommendations, stop-loss and risk conditions, asset allocation, and strategy horizon, converting market insights into a specific and assessable financial proposal.
FREYA.OS thereby completes the intelligent transformation from “Raw Data → Market Understanding → Financial Insights → Strategy Proposal.” For a financial system, however, a seemingly reasonable strategy is not sufficient to enter the market directly. It must still undergo risk checks, permission confirmation, and validation of execution conditions. The next stage of financial intelligence therefore advances from strategy formation to risk assessment and intelligent execution.
The value of a strategy in financial markets can ultimately be validated only through actual execution.
However, multiple stages must be confirmed between strategy formation and actual trading. FREYA.OS therefore connects strategy generation, strategy validation, risk review, permission confirmation, and execution management, ensuring that a strategy cannot bypass control mechanisms and proceed directly into the market.
The Strategy Agent formulates a strategy proposal based on market analysis, user objectives, and relevant conditions. A strategy does more than indicate a trading direction. To the greatest extent possible, it structures the operating conditions, target assets, trading direction, expected horizon, position range, risk conditions, and execution requirements, establishing a foundation for further validation and execution.
After a strategy has been formulated, FREYA.OS can evaluate it through historical data, backtesting, scenario analysis, and related methods. The purpose of validation is not to guarantee future returns, but to analyze the strategy’s potential performance and risks under different market environments, including historical performance, maximum drawdown, volatility, strategy stability, and potential failure conditions, thereby providing a reference for subsequent risk assessment.
After strategy validation, the Risk Agent conducts a further review, assessing position size, market risk, asset concentration, liquidity, volatility, maximum drawdown, stop-loss conditions, and overall portfolio risk. Its central purpose is to ensure that the strategy does not pursue potential opportunities in isolation, but advances within predefined risk boundaries and the conditions established by the user.
After the strategy has passed the relevant checks, the system must further confirm the actual execution environment, including current market prices, market liquidity, available funds, trading time, order type, execution costs, and potential slippage. Because market conditions change continuously, FREYA.OS must maintain a connection between the strategy conditions and the real-time execution environment to prevent material divergence between the strategy assessment and actual market conditions.
Where an actual financial operation is involved, the system determines the next action in accordance with predefined permissions and authorization rules. For operations requiring human confirmation, FREYA.OS can organize the strategy details, risk assessment, and expected execution conditions for user review. Operations that have already been authorized and satisfy established rules may proceed according to the applicable permissions, ensuring a clear separation between AI operating capabilities and actual execution authority.
After the relevant authorization has been obtained, the Execution Agent converts the approved strategy into actual operations and manages orders, execution routing, transaction conditions, slippage, and execution-status monitoring in accordance with established execution conditions. Execution is not limited to issuing a trading instruction; the process must be managed continuously in response to market conditions.
After a strategy enters the execution stage, market prices, liquidity, and transaction conditions may continue to change. The Execution Agent must therefore manage orders and execution outcomes in accordance with the actual market environment and continuously record relevant execution data. This maintains a traceable connection between the strategy and the actual transaction and provides complete data for subsequent performance analysis and strategy review.
After execution has been completed, actual transaction prices, execution costs, position changes, and strategy outcomes return to FREYA.OS as important inputs for portfolio management, performance analysis, and strategy review. FREYA.OS therefore establishes not a simple “Strategy → Trade” sequence, but a complete process comprising Strategy → Validation → Risk → Authorization → Execution → Transaction → Feedback, enabling financial intelligence to progress from strategy analysis to controlled action.
As AI acquires the capability to move from strategy to execution, a more important question arises: who determines what AI is permitted to do and the scope within which it may act? This question forms the foundation of the FREYA.OS mechanism for “human-centered intelligent control.”
The development of financial intelligence does not mean transferring every decision entirely to AI.
On the contrary, as AI progresses from information analysis to participation in strategy and execution, the human role must shift from “personally completing every operation” to setting objectives, managing permissions, establishing risk boundaries, and retaining ultimate control.
The core principle of FREYA.OS is:
AI is responsible for collaboration and efficiency; people are responsible for direction and control.
FREYA.OS separates the capabilities of different AI Agents from their operating permissions and defines the applicable access and operating scope according to each Agent’s function, the user’s identity, the task type, and the risk level. Capabilities ranging from data access, research analysis, and strategy generation to portfolio management and trade execution can be configured according to actual requirements, rather than granting AI unrestricted permissions at the outset. This reduces unnecessary operating risk at the architectural level.
For tasks involving material financial decisions or actual operations, FREYA.OS can establish a human-confirmation mechanism. Before the execution stage, the system presents the relevant strategy, traded assets, direction of operation, expected position, risk conditions, and execution method in a consolidated form, enabling users to confirm the operation after fully understanding the relevant conditions. Human confirmation thereby serves as an important control point between AI recommendations and actual financial operations.
AI capabilities must operate within clear and quantifiable risk boundaries. FREYA.OS can establish limits in accordance with the risk policies of the user or institution, including position size, risk exposure, maximum drawdown, concentration in a single asset, stop-loss conditions, and operating frequency. Where a strategy or operation exceeds the predefined range, the system can trigger controls such as renewed confirmation, suspension, or rejection of execution, preventing AI from operating beyond its authorized scope without appropriate constraints.
FREYA.OS clearly distinguishes between “providing financial recommendations” and “executing actual operations.” AI Agents may complete research, market analysis, and strategy generation in accordance with the task, but progression to execution depends on predefined permissions and authorization conditions. Through layered authorization, different Agents can perform their respective functions without concentrating all financial operating permissions in a single Agent, while still giving AI sufficient capability to work within clearly defined rules.
When an AI task enters continuous operation or execution, users must retain real-time control. FREYA.OS therefore supports the suspension, termination, or revocation of tasks within the applicable permission scope and allows risk conditions or Agent permissions to be adjusted in response to the market environment and actual requirements. This ability to intervene and terminate ensures that continuous AI operation does not reduce the user’s control over the overall financial workflow.
Every material operation performed by financial AI requires clear traceability. FREYA.OS can create operational records for key stages including task origination, data inputs, AI analysis, strategy formation, risk checks, permission confirmation, and execution outcomes, while retaining subsequent adjustments and outcome feedback. The entire financial workflow can thereby form a complete operational chain, providing a basis for subsequent analysis, risk management, and system governance.
Through these mechanisms, FREYA.OS establishes not a simple relationship between “the user and an AI tool,” but a human-machine collaboration model built on clear objectives, defined permissions, controlled risk, and traceable operations. AI can assume greater responsibility for research, analysis, and execution, while key direction, risk boundaries, and ultimate control remain with people and institutions.
For a financial-grade AI system, however, permissions and control mechanisms are only the foundation. Once AI participates deeply in financial workflows, the system must continuously address whether its models are reliable, its data are secure, its operations are traceable, and the intelligent system as a whole can be effectively monitored. FREYA.OS must therefore establish a governance and security framework covering the full lifecycle of models, data, systems, and operations.
Financial AI processes more than general information. It may involve market data, trading strategies, portfolios, account information, and actual financial operations. In addition to analytical and decision capabilities, a financial intelligence system must therefore establish a comprehensive framework for governance, security, and risk control. FREYA.OS integrates governance and security into the operation of financial intelligence and establishes a manageable, monitorable, and traceable operating environment across models, data, identities, permissions, operations, and risk monitoring, providing the foundation for the stable long-term operation of financial intelligence.
FREYA.OS places the use of AI within a manageable and traceable governance framework. It establishes appropriate management mechanisms covering Agent behavior, model use, data sources, permission scope, risk parameters, and execution rules. The purpose is not to restrict AI capabilities, but to ensure that AI can participate effectively in financial work while its behavior and outputs remain within a clear framework of rules and controls.
AI models do not maintain the same level of accuracy and reliability under all conditions. Data bias, insufficient information, model errors, AI hallucinations, model drift, and changes in market conditions may all affect model outputs. FREYA.OS must therefore use model evaluation, outcome monitoring, strategy backtesting, continuous validation, and related measures to understand the applicable scope and potential risks of AI outputs and avoid treating model results as financial judgments that are valid without qualification.
Data is a critical foundation of financial intelligence, and financial data may be highly sensitive. FREYA.OS establishes security mechanisms across data transmission, storage, and use. Encryption, access controls, data segregation, and protection of sensitive information are used to reduce the risks of unauthorized access and data leakage and to ensure that different users, Agents, and system components can use relevant data only within their authorized scope.
Financial AI must operate within a stable and continuously available technology environment. The FREYA.OS infrastructure must therefore address cloud and network security, system monitoring, anomaly detection, backup, and recovery. Potential anomalies are identified continuously during system operation to improve overall service stability and resilience and reduce the effect of a single system failure on the financial workflow.
Identity authentication is the first foundation of security controls within a financial system. FREYA.OS must confirm the identities of different users, enterprise roles, and system components and use those identities as the basis for subsequent permission allocation, operational controls, and audit tracing. A clear identity framework enables the system to determine more accurately which data different parties may access and which operations they may perform.
Identity confirmation does not confer permission to perform every operation. FREYA.OS further allocates permissions according to user role, task type, Agent function, and authorization scope, maintaining appropriate separation among data access, analysis, strategy generation, and execution capabilities. Through layered and granular access controls, AI capabilities can be managed separately from actual operating permissions, reducing the risks arising from excessive concentration of authority.
Financial intelligence requires clear operational traceability. FREYA.OS establishes operational records for material financial workflows, forming a complete work chain from data input and AI analysis to strategy generation, risk checks, human confirmation, and execution outcomes. These records provide an important basis for subsequent risk management, issue investigation, and system governance.
Governance is not a one-time system configuration, but a continuous process. Market risk, model risk, execution risk, and system risk may all change in response to market conditions and system status. FREYA.OS therefore requires continuous monitoring and anomaly detection to identify departures from predefined conditions promptly and to issue alerts or apply restrictions, suspension, or additional confirmation in accordance with established mechanisms.
Through governance, security, and risk-control mechanisms, FREYA.OS integrates security capabilities into every material stage of financial intelligence rather than applying additional checks only after the financial work has been completed. As data, Agents, strategy, risk, execution, and governance become connected, financial intelligence moves beyond one-time analytical tasks and enters a continuously operating, continuously monitored, and continuously optimized intelligent closed loop.
Traditional financial tools often support one-time analysis, decisions, or transactions, but financial markets do not stop changing after a single operation.
FREYA.OS organizes financial work into a continuously operating intelligent closed loop:
Data → Intelligence → Decision → Action → Learning
Global financial markets continuously generate new information, including prices, trading volumes, macroeconomic data, corporate information, market sentiment, and on-chain activity. These continuously updated data re-enter FREYA.OS and provide AI Agents with the latest market information, ensuring that the system’s understanding of the market environment is not confined to a single point in time.
AI Agents reassess market conditions based on the latest data. The Research Agent can update research materials, the Market Agent can reassess trends, volatility, and sentiment, and the Strategy Agent can review existing strategies in light of new market conditions, enabling financial intelligence to update continuously as markets change.
Based on the latest market understanding, FREYA.OS can reassess existing strategies and portfolios. Some strategies may continue to satisfy their original conditions, others may require adjustment, and material changes in the market environment may require a strategy to be suspended or terminated. Decision-making therefore ceases to be a one-time outcome and becomes a process that can be continuously revised as new information emerges.
Where established risk conditions and authorization requirements have been satisfied, an approved strategy can proceed to the execution workflow. The system simultaneously records actual transactions, execution costs, position changes, and portfolio performance on a continuous basis, enabling every action to become important data for subsequent analysis.
After execution has been completed, the outcomes return to FREYA.OS as inputs for the next cycle of financial work. Based on actual results, the Learning Agent can conduct performance analysis, strategy review, backtesting, model evaluation, parameter review, and comparison across market environments. This helps the system understand actual strategy performance under different conditions and provides a reference for subsequent strategy adjustments.
Formation of a Continuously Operating Intelligent Closed Loop
The financial workflow established by FREYA.OS is therefore not a simple “Start → Analyze → End” process, but rather:
Objective → Data → Intelligence → Decision → Action → Outcome → Learning → Next Decision Cycle
Every market change, strategy execution, and actual outcome can become an input for the next cycle of financial work. Through this continuously operating mechanism, FREYA.OS progressively transforms financial intelligence from a one-time analytical tool into an intelligent system capable of continuous perception, continuous decision-making, and continuous optimization.
When AI Agents, financial data, strategy capabilities, risk management, and execution are connected through a unified platform, FREYA.OS becomes more than an AI tool for individual users. It becomes an open financial intelligence ecosystem capable of connecting markets, institutions, users, developers, data, and financial services.
This ecosystem is centered on FREYA.OS as its intelligence core, built on global multi-asset markets, and supported by financial services and technology infrastructure. Through open APIs, SDKs, Agents, and collaborative networks, different participants can use, access, and expand financial intelligence capabilities together.
The FREYA.OS ecosystem will therefore be developed across three levels:
Connect Global Markets → Connect Financial Services → Connect Ecosystem Participants
The ultimate objective is an open, collaborative, and continuously expanding global financial intelligence network.
Financial markets are inherently diverse and interconnected. Different asset classes are affected by macroeconomic conditions, liquidity, policy, market sentiment, global capital flows, and other factors; no market operates in complete isolation.
FREYA.OS is therefore not limited to a single asset class or trading scenario. It establishes unified financial intelligence capabilities across global multi-asset markets.
The principal asset classes currently covered include:
Coverage includes major equity assets such as listed companies, industries, and market indices, providing the data foundation for fundamental research, market analysis, and portfolio management.
Connection to major currency markets and exchange-rate data supports macroeconomic analysis, currency-trend research, cross-market analysis, and risk management.
Coverage includes major precious-metals markets such as gold and silver, supporting asset allocation, hedging analysis, and market-trend research.
Coverage includes energy, industrial metals, and other major commodity markets, supporting analysis of the effects of global economic cycles, changes in supply and demand, inflation, and related factors.
Connection to digital-asset markets and relevant on-chain data provides intelligent support for digital-asset research, market analysis, strategy, and risk management.
Explore connections between traditional assets and digital financial infrastructure through the digitization of real-world assets and on-chain financial applications.
Building on coverage across different asset classes, FREYA.OS extends financial intelligence from “single-asset analysis” to “cross-asset intelligence.” Data, signals, and market conditions from different markets are incorporated into a unified analytical framework to support cross-asset market analysis, asset-correlation research, market-rotation identification, asset allocation, risk diversification, hedging analysis, and portfolio optimization. This means that FREYA.OS no longer processes market data as isolated datasets, but develops a deeper understanding of relationships among different financial markets and assets, advancing financial intelligence from a single-market perspective toward multi-asset coordination. As this cross-asset intelligence becomes connected to trading, asset management, and other professional financial services, FREYA.OS can progressively expand from a market-analysis platform into a more comprehensive financial intelligence service system.
If FREYA.OS is the technology core of financial intelligence, FREYA GLOBAL extends those capabilities into real financial business and service environments.
With FREYA.OS as its technology foundation, FREYA GLOBAL connects AI, data, trading, asset management, custody, and regional service resources to progressively establish a global financial services system addressing different markets and client requirements.
Its overall architecture can be summarized as:
Financial Intelligence + Financial Services + Financial Infrastructure
By combining these three elements, FREYA provides users with more than AI analytical capabilities; it connects the data, tools, professional services, and infrastructure required for financial work.
Through regional service centers and a professional services network, FREYA GLOBAL provides users and partners with product and account support, market information services, user services, education and training, regional market expansion, and partner services. Through this localized service system, FREYA seeks to reduce barriers to accessing and using financial intelligence services in different regions while establishing a more direct service connection between the platform and regional markets.
FREYA GLOBAL brings the financial intelligence capabilities of FREYA.OS into actual financial workflows, providing professional users and relevant institutions with market research, strategy support, trading management, portfolio management, risk monitoring, and performance analysis. By combining AI with professional financial services, market insights can advance beyond analysis and connect to financial workflows involving strategy, management, and execution.
As the financial intelligence core of the overall system, FREYA.OS is responsible for connecting:
Data → AI Agents → Strategy → Risk → Execution → Portfolio
Different business and service capabilities can invoke the relevant intelligence through FREYA.OS.
In support of asset and account management, FREYA GLOBAL progressively connects relevant custody and asset-service capabilities, including asset records, position management, account information, asset reporting, back-office operational support, and related professional services. These foundational services provide the business support required for the continuous operation of financial intelligence and help establish a more comprehensive asset and account management system.
As the global business expands, FREYA will establish regional service networks in accordance with the requirements of different markets, connecting local users, financial institutions, and partners.
Regional Service Centers will be responsible for:
Local Operations | Market Expansion | Partner Services | User Support
They will thereby serve as connection points between the global platform and regional markets.
Building on the foregoing capabilities, FREYA GLOBAL seeks to progressively establish a global financial services network composed of financial intelligence, financial services, financial institutions, regional nodes, and financial infrastructure. FREYA.OS provides the core financial intelligence capabilities, while FREYA GLOBAL connects those capabilities to real financial-service environments and global markets. As platform capabilities continue to open, this financial intelligence infrastructure can further serve professional trading teams, asset management firms, and financial institutions, establishing a development path from technology capabilities to financial services and ultimately to global financial infrastructure.
Individual users can obtain financial intelligence through FREYA. Professional trading teams, asset managers, and financial institutions, however, generally require not another standalone AI tool, but financial intelligence infrastructure that can be integrated into their existing business systems.
FREYA.OS therefore makes financial AI capabilities available to institutional clients in a modular and open form.
Primary users include:
Professional Trading Teams
Asset Management Institutions
Financial Institutions
Corporate Clients
Financial Technology Platforms
Different institutions can select and integrate the financial intelligence capabilities appropriate to their own business requirements.
Through APIs, financial institutions and enterprises can integrate the financial intelligence capabilities of FREYA.OS into existing financial systems and business workflows. Without rebuilding a complete AI financial infrastructure, they can invoke market data, AI analysis, strategy, risk, portfolio, Agent, and related capabilities as required. API integration also enables FREYA.OS to serve as an intelligence layer behind existing financial systems and connect with an institution’s established products and workflows.
SDKs provide development teams with more flexible development and integration options, enabling the financial AI capabilities of FREYA.OS to be embedded in different software, platforms, and use cases. Developers can combine relevant AI Agents, financial intelligence modules, and workflows according to their product architecture and business requirements, extending financial AI from standalone applications into a broader product and service environment.
For financial institutions and corporate clients, FREYA.OS can provide white-label and customized solutions that integrate financial intelligence into the client’s own brand, products, and business system. Institutions can configure user interfaces, AI Agents, financial data, strategy modules, risk modules, permission frameworks, and administrative systems according to actual requirements, thereby establishing a financial AI platform aligned with their own business processes and service model.
FREYA.OS provides institutions with composable financial AI capabilities, incorporating AI Agents, financial data, strategy modules, risk modules, permission frameworks, and administrative systems within a unified architecture. Institutions can flexibly invoke and combine different capabilities according to their business requirements and establish financial intelligence applications aligned with their own workflows.
In institutional environments, financial AI must provide intelligent analytical capabilities while also satisfying enterprise requirements for security, permissions, stability, manageability, and auditability. FREYA.OS therefore integrates AI Agents, financial data, access controls, risk management, and business workflows within an enterprise-grade architecture, enabling AI capabilities to enter actual financial operations under a clear management and governance framework.
The long-term direction of FREYA.OS is to develop financial AI from an individual product capability into an enterprise-grade platform capability, enabling every company to have its own financial AI platform. Institutions can invoke different Agents and financial intelligence modules through FREYA.OS and establish dedicated AI financial workflows according to their business requirements, allowing research, analysis, strategy, risk, portfolio management, and related capabilities to operate collaboratively within the same architecture.
This also means that FREYA.OS is not only a financial AI platform serving end users; it can also become infrastructure connecting financial institutions with AI capabilities. Truly open infrastructure should not serve only the platform itself. It must also connect data, technology, financial institutions, and professional services, enabling different participants to contribute jointly to the development of the financial intelligence ecosystem.
The FREYA ecosystem is not a one-directional “platform-to-user” relationship. It is an open collaborative network involving users, professional traders, financial institutions, developers, data providers, and service partners. Different participants perform different functions within the ecosystem and use FREYA.OS to connect financial data, AI Agents, strategies, trading, and professional services, enabling financial intelligence to extend progressively from an individual product capability into a wider range of financial work environments.
Users can access financial intelligence capabilities through FREYA.OS, including market research, financial analysis, strategy support, and portfolio management, and can invoke different AI Agents to complete relevant financial tasks in accordance with their objectives, risk preferences, and authorization scope.
Professional traders can use the AI Agent team to improve the efficiency of research, strategy, risk, and trading work, reduce repetitive information processing, and devote more attention to market judgment and asset management.
Financial institutions can access FREYA.OS through APIs, SDKs, white-label solutions, and enterprise-grade AI, combining AI Agents, data, strategy, and risk capabilities according to their business requirements to establish dedicated financial intelligence workflows.
Developers can build Agents, Skills, strategies, and financial AI applications around FREYA.OS, introduce additional professional capabilities to the platform, and continuously expand financial intelligence use cases.
Data providers can supply market, macroeconomic, fundamental, derivatives, on-chain, and other professional financial data, continuously expanding the data sources and market coverage of FREYA.OS.
Service partners can connect professional services in trading, custody, payments, asset management, and financial technology, integrating external financial infrastructure into FREYA’s intelligent workflows.
Ultimately, FREYA seeks to establish an open ecosystem in which “users apply intelligence, institutions integrate intelligence, developers build intelligence, data providers supply intelligence, and service partners extend intelligence,” enabling different participants to connect, share capabilities, and jointly advance ecosystem development within the same financial intelligence network.
FREYA’s ecosystem philosophy is not to replace existing financial institutions, data platforms, technology companies, or professional service systems, but to connect their different capabilities through financial intelligence infrastructure.
Its core relationships can be summarized as:
People × AI × Data × Markets × Financial Institutions × Technology × Services
Within this framework:
People Define objectives, set direction, and retain ultimate control.
AI Performs analysis, supports collaboration, generates strategies, and advances workflows.
Data Provide market, macroeconomic, fundamental, and other financial information.
Markets Provide the real financial environment and continuously changing market signals.
Financial Institutions Provide professional financial services and business infrastructure.
Technology Provides AI, cloud computing, systems architecture, and development capabilities.
Services Connect actual financial functions including trading, custody, payments, and asset management.
When these participants are connected through unified financial intelligence infrastructure, the value of FREYA.OS is no longer limited to one product or one user. It progressively forms a financial intelligence network capable of continuous expansion.
The FREYA ecosystem can develop through three stages:
Capability Connection Connect AI, data, markets, and financial services.
↓
Capability Access Open APIs, SDKs, Agents, and modular capabilities to institutions and developers.
↓
Collaborative Ecosystem Enable users, institutions, developers, data providers, and service partners to participate jointly in building the financial intelligence network.
FREYA’s long-term direction is not to establish a closed AI platform, but to form an open, scalable, and collaborative global financial intelligence ecosystem.
Financial intelligence can thereby become a new generation of financial infrastructure connecting people with markets, institutions with technology, and data with services.
As this ecosystem extends from technology and business into different regions, FREYA’s next stage will progress from “building a global financial intelligence ecosystem” to “establishing global markets and regional service networks.” This transition leads naturally to the next chapter on global markets and regional deployment.
The development of FREYA.OS extends beyond the establishment of a financial AI product.
As AI Agents, financial data, strategy engines, risk management, and execution capabilities are progressively integrated, FREYA.OS will evolve from a user-facing AI tool into a financial intelligence platform serving individuals, professional traders, financial institutions, and developers, and will subsequently advance toward global financial intelligence infrastructure.
This development is not simply an expansion of product functions. It is the progressive evolution of financial AI capabilities from standalone tools to system-level capabilities, from an individual product to a platform, and from a platform to infrastructure.
Its core path is:
AI Tool → AI Agent → AI Team → AI System → AI Infrastructure
The ultimate objective is financial intelligence infrastructure connecting global markets, financial institutions, data, AI, and financial services.
Early artificial intelligence primarily existed in the form of tools through which users searched for information, generated content, or obtained analytical results.
The complexity of financial work, however, means that a single AI tool cannot independently manage the complete process from market research to strategy execution.
FREYA therefore adopts a progressive product-evolution path, moving AI from “providing answers” to “participating in work,” and subsequently toward a financial intelligence system capable of continuous operation.
At the initial stage, AI primarily serves as an auxiliary tool for financial work. Users can express requirements in natural language, and AI can assist with information organization, market inquiries, basic analysis, content generation, and data interpretation, making financial information easier to understand and use. The tool model, however, still requires users to ask questions proactively, locate information, and organize workflows. The next stage therefore advances from “AI answering questions” to “AI proactively undertaking work.”
The emergence of AI Agents transforms AI from a conversational tool into an intelligent work unit with a defined function.
Within FREYA.OS, different Agents undertake specialized financial functions, including:
Research Agent Responsible for research materials, the macroeconomic environment, industries, and market-event analysis.
Market Agent Responsible for identifying market conditions, trends, volatility, and market sentiment.
Strategy Agent Responsible for strategy generation, strategy analysis, and parameter optimization.
Risk Agent Responsible for reviewing risk exposure, positions, stop-loss conditions, and related risk parameters.
Execution Agent Responsible for trade execution, transaction management, and control of execution conditions.
Portfolio Agent Responsible for asset allocation, rebalancing, and performance analysis.
Learning Agent Responsible for backtesting, performance review, model evaluation, and continuous optimization.
AI therefore begins to do more than answer questions. It starts to:
Understand the Task → Perform the Work → Produce an Outcome → Return the Next Step
When multiple Agents are connected within the same workflow, AI develops from individual intelligent units into a financial AI team capable of coordinated collaboration.
Different Agents divide responsibilities according to their respective functions:
Research → Market → Strategy → Risk → Execution → Portfolio → Learning
Each Agent is responsible for its own professional scope while sharing the necessary data, context, and work results.
This architecture converts the professional specialization found in traditional financial institutions into a digital workflow in which AI can collaborate.
The central change is:
A transition from “one AI answering every question” to “an AI team completing the work together.”
When AI Agents, financial data, strategy engines, risk management, and execution capabilities are integrated within the same platform, AI becomes more than a collaborative team composed of multiple Agents; it forms an integrated financial intelligence system. Beginning with the user’s objective, FREYA.OS connects financial data, AI Agents, market analysis, strategy generation, risk management, permission confirmation, execution management, portfolio management, and learning and review into a complete workflow extending from understanding requirements and analyzing markets to formulating strategies, executing them, and continuously optimizing outcomes. At this stage, the value of AI no longer depends solely on the capability of any individual model, but on whether the system as a whole can coordinate different financial capabilities effectively and connect data, intelligence, decisions, and actions. This also marks the progression of FREYA.OS from an individual “AI product” to a “Financial Intelligence Operating System,” establishing the foundation for broader financial applications and infrastructure development.
As the financial intelligence capabilities of FREYA.OS become increasingly open to individuals, professional teams, financial institutions, and developers, the role of the system will evolve from a product platform toward financial intelligence infrastructure. At this stage, FREYA will no longer provide only an App or a standalone AI tool. It will consolidate AI Agents, financial data, market analysis, strategy, risk management, portfolios, execution, APIs, SDKs, permission governance, enterprise-grade AI, and related functions into composable, callable, and scalable foundational capabilities. Financial institutions can integrate these capabilities according to their business requirements; developers can build new financial AI applications on them; professional teams can incorporate them into daily workflows; and end users can directly access FREYA’s financial intelligence services. FREYA.OS will thereby progress from “a financial AI product” to “financial intelligence infrastructure that can be used and invoked by different financial participants.”
The essential distinction between a product and infrastructure is whether its capabilities can be continuously expanded and reused.
Traditional financial products are generally built around specific functions or client segments, such as trading platforms, portfolio tools, or market-information platforms.
FREYA.OS seeks instead to consolidate different financial capabilities into composable system capabilities.
For example:
Data Capabilities + Research Agent
can form a financial research service;
Market Data + Strategy Agent + Backtesting Capabilities
can form strategy research and quantitative analysis services;
Strategy + Risk Agent + Execution Capabilities
can form an intelligent trading workflow;
Multi-Asset Data + Portfolio Agent
can form intelligent asset-allocation and portfolio-management capabilities.
The same underlying capabilities can therefore support different products, institutions, and application scenarios.
This “modularization of capabilities” is an important foundation for FREYA’s progression from platform to infrastructure.
FREYA’s long-term development will likewise progress from “using products” to “integrating capabilities.”
Individual users can access financial AI directly through FREYA.
Professional traders can use a complete financial AI team.
Financial institutions can integrate relevant capabilities through APIs, SDKs, and white-label solutions.
Developers can build new financial AI applications using the Agents, Skills, data, and financial capabilities of FREYA.OS.
FREYA’s service model can therefore progress through:
Direct Use → Module Integration → Platform Integration → Ecosystem Co-Creation
Different participants can use FREYA according to their own requirements without adopting an identical product format.
To become financial infrastructure, a system requires not only AI capabilities, but also the system capabilities necessary for long-term operation and continuous expansion.
FREYA.OS will establish its financial intelligence foundation across the following areas:
Continuously connect multidimensional data from global markets, macroeconomics, fundamentals, derivatives, digital assets, real-world assets, news, on-chain activity, and related sources.
Establish a continuously scalable Agent architecture through which different professional financial capabilities can be incorporated into the system in Agent form.
Consolidate market analysis, strategy, backtesting, risk, asset allocation, execution, and related capabilities into callable financial modules.
Establish identity authentication, permission management, risk boundaries, operational records, and AI governance mechanisms to ensure that the system operates under appropriate control in financial environments.
Enable financial institutions, enterprises, and developers to integrate FREYA’s financial intelligence capabilities through APIs, SDKs, white-label solutions, and other standardized methods.
Together, these capabilities constitute the technology and business foundation required for FREYA.OS to evolve from a “product” into “infrastructure.”
Once financial intelligence possesses sufficient system capabilities and openness, the service scope of FREYA.OS can progressively expand from an individual product and market to global financial markets.
Building on multi-asset financial intelligence, FREYA will connect different markets including equities, foreign exchange, precious metals, commodities, digital assets, and real-world assets, while using global data to develop a continuous understanding of different market environments. As financial institutions, trading services, asset management, custody, and other financial services are progressively integrated, financial intelligence will no longer remain isolated within analytical tools, but can enter complete financial workflows.
Developers, technology partners, and data providers can also participate, continuously expanding FREYA’s data, Agents, applications, and service capabilities.
FREYA thereby seeks to progressively establish a global financial intelligence network connecting AI, data, markets, financial institutions, financial services, and technology, making financial intelligence an important collaboration layer among different markets and financial participants.
The long-term development of FREYA.OS can be summarized in five stages:
Make financial information easier to understand.
↓
Enable AI to begin undertaking specific financial work.
↓
Enable different financial Agents to collaborate on complex tasks.
↓
Connect data, intelligence, strategy, risk, and execution into a complete workflow.
↓
Make financial intelligence a foundational capability that individuals, institutions, and developers can use and integrate on a continuing basis.
The central purpose of this evolution is not to enable AI to replace more people, but to allow intelligent systems to assist with a wider range of financial work under clear permission, risk, and governance frameworks.
The long-term objective of FREYA.OS is to establish financial intelligence infrastructure serving individuals, professional teams, financial institutions, and developers.
For individuals, FREYA seeks to enable everyone to have a financial AI team of their own. For professional traders, AI will become an important collaborative partner in research, strategy, trading, and asset management. For financial institutions, FREYA.OS can become an enterprise-grade financial AI platform connecting data, AI, strategy, risk, and financial services.
For developers and the broader financial technology ecosystem, FREYA seeks to consolidate financial intelligence into foundational capabilities that can be invoked, combined, and used to create new solutions, enabling more financial applications to be built on the same intelligent foundation.
FREYA’s ultimate objective is therefore not to build another isolated financial AI application, but to establish a new generation of financial intelligence infrastructure connecting people, AI, data, markets, institutions, and financial services.
As financial intelligence progresses from an individual product to an open platform and then from a platform to global infrastructure, FREYA.OS will support more than one-time financial analyses or trading tasks. It will become a global financial intelligence network capable of continuously understanding markets, coordinating intelligence, managing risk, and connecting financial services. This will provide the foundation for FREYA’s next stage of global development and establish a long-term basis for a more open, intelligent, and sustainable financial services ecosystem.
2023
Institutional Asset Management Foundation
Deepen institutional-grade asset management and investment services
Serve professional institutional clients and accumulate cross-market experience
Establish investment research, risk-control, and trade-execution capabilities
Develop a mature asset management system through multiyear validation
2024
Business Expansion
Increase the scale of capital management and broaden service coverage
Expand diversified financial businesses and market presence
Enhance risk-control, operating, and management mechanisms
Accumulate market data, strategy, and execution experience
2025
Architecture Formation
Formally establish the development direction of FREYA.OS
Complete the overall architecture design of the Financial Intelligence Operating System
Establish the AI Agent team and collaboration framework
Plan the foundational architecture for data, capabilities, execution, and governance
2026
Infrastructure Development
Launch the core FREYA.OS technology stack
Deploy the 1+7 Multi-Agent core architecture
Connect multi-asset data and execution capabilities
Launch managed-account and AI strategy services
2027
Business Development
Open institutional-grade APIs and SDKs
Advance the establishment of regional service centers
Launch white-label and enterprise-grade solutions
Expand integration across custody, payments, and infrastructure
2028
Scaling
Establish a cross-market financial intelligence network
Launch composable strategy and Agent modules
Advance enhanced compliance and risk automation
Expand institutional asset services
2029+
Global Platform
Build a global financial intelligence layer
Develop a developer and partner ecosystem
Establish multi-regional intelligent infrastructure
Form a unified global intelligent management network for multiple asset classes
The development direction of FREYA.OS is not merely to add more functions or build more AI tools, but to reconsider how AI will participate in financial work and how financial services will be reorganized.
As financial markets become increasingly globalized, data-driven, and complex, traditional working methods that rely on human experience, fragmented tools, and multiple platforms will face growing challenges in efficiency, information integration, and decision coordination.
FREYA seeks to transform financial intelligence from a “passively used tool” into a “continuously collaborative work system,” enabling AI to understand objectives, connect information, coordinate professional capabilities, and advance workflows while preserving human control over direction, permissions, and material decisions.
Ultimately, FREYA seeks to make financial intelligence—a capability historically concentrated in a limited number of professional institutions—a foundational capability accessible to a broader range of individuals, enterprises, and financial institutions.
Traditional financial work often consists of numerous fragmented tools and processes.
Users must first locate market data and then use different platforms to perform analysis; they must research different assets and formulate strategies; and once a strategy has been developed, they must still conduct risk checks, execute trades, and manage the portfolio.
Throughout this process, people must continually switch among different tools, data sources, and workflows.
FREYA seeks to change this function-centered mode of working.
In the future, users will not need to understand how every financial tool should be operated before beginning. Instead, they can start from their own objectives and express directly to financial intelligence:
What do I want to achieve?
For example:
“I want to understand the principal risks currently affecting global markets.”
“I want to research future investment opportunities in a particular industry.”
“I want to establish a portfolio consistent with a specified level of risk.”
“I want to assess whether my existing positions are excessively concentrated.”
After receiving an objective, FREYA can organize the relevant data, AI Agents, and financial capabilities according to the task and convert financial work that would otherwise require multiple steps into a more natural intelligent workflow.
This means that the interaction model of financial AI will shift from:
People Searching for Functions
progressively toward:
People Define Objectives; the System Organizes Capabilities.
AI will no longer simply wait for users to perform each operation, but will become a collaboration layer within financial work.
The human role will likewise progress from “operating tools” to “setting objectives, exercising judgment, and controlling outcomes.”
Historically, comprehensive financial research and asset management capabilities have been concentrated primarily within large financial institutions and professional teams.
A mature investment process typically requires the participation of different professionals, including researchers, market analysts, strategy researchers, risk managers, traders, and portfolio managers.
This professional specialization can improve the quality of decision-making, but it also creates significant expertise and resource requirements.
The development of AI Agents creates new possibilities for reorganizing these professional capabilities.
FREYA.OS converts functions including research, market analysis, strategy, risk, execution, portfolio management, and learning into collaborative AI Agents, enabling work formerly distributed across different professional roles to be completed collectively by an AI team.
FREYA therefore seeks to build not:
A more intelligent chatbot.
But rather:
A financial AI team for every person.
This team can assist with different levels of financial work according to the objectives and requirements of different users.
For general users, it can reduce barriers to understanding and using financial information.
For professional investors, it can improve the efficiency of research, strategy, and risk management.
For professional trading teams, it can assist in processing large volumes of data and repetitive work.
More importantly, an AI team does not mean transferring every decision to AI for autonomous completion.
FREYA’s core principle remains:
AI is responsible for collaboration and efficiency; people are responsible for direction and control.
AI can serve as an amplifier of capabilities, but ultimate objectives, risk requirements, and material decisions remain under human control.
If individuals require a financial AI team, enterprises and financial institutions require foundational financial AI capabilities that can be integrated into their own business workflows.
Future financial institutions will not necessarily need to build an entire AI financial technology system from the ground up.
FREYA.OS can serve as an intelligent enterprise platform connecting:
Data × AI × Strategy × Risk × Execution × Financial Services
within a unified intelligent platform.
Enterprises can integrate different Agents and financial intelligence modules according to their own business requirements.
For example:
Research teams can integrate Research and Market Agents;
Trading teams can integrate Strategy, Risk, and Execution capabilities;
Asset management institutions can integrate portfolio and asset-allocation capabilities;
Financial technology companies can incorporate financial intelligence into their own products through APIs, SDKs, and white-label solutions.
Under this model, financial AI is no longer an independent application. It can become an intelligence layer within an enterprise’s existing systems.
FREYA seeks to enable enterprises to establish, in accordance with their own business models:
Financial AI workflows of their own.
Enterprises can determine which Agents to use, which data to connect, which risk boundaries to establish, and which operations require human confirmation.
A financial AI platform is therefore more than a technology tool. It is an important foundation for the future management of financial data, intelligent decision-making, and financial services within the enterprise.
FREYA’s ultimate development direction is not to build an AI platform with an ever-increasing number of functions.
The more important objective is to establish foundational capabilities that can operate over the long term, expand continuously, and serve different financial participants.
These capabilities must include:
Understanding Objectives
Understand the financial tasks that users and institutions seek to complete.
Connecting Markets
Maintain continuous connections to global multi-asset markets and relevant financial data.
Coordinating Intelligence
Enable different AI Agents to complete complex work together according to their professional functions.
Managing Risk
Establish appropriate risk conditions and control mechanisms throughout strategy and execution processes.
Advancing Execution
Advance financial strategies into actual workflows subject to applicable permission and authorization requirements.
Continuous Learning
Return market changes, execution outcomes, and historical performance to the analysis and optimization process.
This produces the following sequence:
Objective → Data → Intelligence → Decision → Risk → Action → Learning
This is no longer a financial AI tool used for a single task, but a financial intelligence system capable of continuous operation.
As this system is further opened to individuals, professional teams, financial institutions, developers, and partners, its role will progressively evolve from a product into infrastructure.
FREYA’s long-term vision is to extend financial intelligence from an individual user, platform, and market into an intelligent network spanning roles, institutions, and markets.
Individuals can use financial AI teams of their own;
professional traders can use AI to improve research and trading efficiency;
enterprises can establish financial AI platforms of their own;
financial institutions can integrate FREYA’s financial intelligence capabilities;
developers can build new financial applications using Agents, Skills, APIs, and SDKs;
and data and service providers can participate in the broader financial intelligence ecosystem.
Different participants connect through FREYA.OS to progressively establish:
People × AI × Data × Markets × Institutions × Services
as a financial intelligence network.
Within this network, AI is no longer merely an additional feature of financial services, but progressively becomes the intelligence layer connecting different financial capabilities.
FREYA’s vision can be expressed through three principal directions:
Make professional capabilities—including research, market analysis, strategy, risk, and portfolio management—more accessible through AI.
Bring financial intelligence closer to every person.
Enable enterprises and financial institutions to integrate AI, data, strategy, risk, and financial services into their own business workflows.
Make financial intelligence a core enterprise capability.
Connect global multi-asset markets, financial institutions, data providers, technology partners, and financial services to establish a financial intelligence network spanning markets, institutions, and regions.
Make financial intelligence the infrastructure of the next generation of finance.
FREYA.OS is exploring more than the question, “What can AI do for finance?”
The more important question is:
When AI becomes a genuine collaborative capability within financial work, what will financial work itself become?
In the future, the interface for financial work may no longer be a complex list of tools and functions, but an intelligent work interface capable of understanding objectives.
Professional financial capabilities will no longer exist only within the organizational structures of large institutions. They can be reorganized, invoked, and coordinated through AI Agents.
For individuals, this means a financial AI team of their own.
For enterprises, it means a financial AI platform of their own.
For global financial markets, it means an intelligent network jointly built by AI, data, institutions, technology, and financial services.
The long-term direction of FREYA.OS follows this progression:
AI Tool → AI Agent → AI Team → AI System → AI Infrastructure
progressively advancing toward the next generation of financial intelligence.
A financial AI team for every person.
A financial AI platform for every company.
Global markets connected by financial intelligence.
This is FREYA’s long-term vision for the future of financial intelligence.
| Review Area | Recommendation |
|---|---|
| Positioning and Licensing Boundaries | Clearly distinguish, by jurisdiction, among technology platforms, information services, research tools, investment advice, asset management, trade execution, custody, and activities relating to digital assets. Avoid making commitments concerning regulated activities until the applicable local licensing, exemption, or partnership arrangements have been confirmed. |
| Clients and Distribution | Distinguish among retail clients, professional or qualified investors, institutional clients, and corporate clients in accordance with local rules, and configure the corresponding functions, disclosures, risk warnings, suitability requirements, and human-review requirements. |
| AI Governance | Establish a globally consistent AI governance baseline, including management accountability, model and Agent inventories, risk classification, testing and validation, vendor due diligence, change management, incident reporting, and continuous monitoring. Supplement this baseline with local controls in accordance with the regulatory requirements of each market. |
| Agentic AI Controls | Apply least-privilege access, tiered authorization, sandbox testing, human oversight, and comprehensive logging to high-impact operations involving Agent tool use, data access, external connections, payments, or transactions. |
| Data and Privacy | Design the data lifecycle in accordance with applicable data-protection and privacy laws. When processing personal data, assess the purpose of collection, scope of use, retention period, cross-border transfers, third-party processing, data-subject rights, data-localization requirements, and security requirements. |
| Client Communications | Clearly explain the role, limitations, and uncertainties of AI. Avoid presenting model outputs as guarantees, certain predictions, or risk-free outcomes. |
| Market and Model Risk | Establish testing and monitoring for backtesting bias, data latency, model drift, hallucinations, extreme market scenarios, liquidity, slippage, and execution deviations. |
| Records and Audit | Retain material inputs, outputs, risk checks, human approvals, execution instructions, versions, and anomalous events to support internal audits, reviews by partner institutions, and regulatory inquiries across different jurisdictions. |
This Appendix provides a general review framework for global market launches and product governance. It does not replace formal advice from licensed legal counsel, compliance advisers, tax advisers, or relevant regulatory authorities in any jurisdiction. Before implementation in any market, an independent assessment must be completed in accordance with local laws, licensing requirements, and the applicable business model.
The Chinese edition consistently uses Traditional Chinese and professional terminology commonly used in the international financial and technology sectors, including terms equivalent to “users, data, portfolio, operations, privacy, access controls, and artificial intelligence.” Terminology relating to regional regulation, law, and markets should be localized for the target jurisdiction.
English technical terms—including AI Agent, API, SDK, LLM, and VaR—are retained in English to avoid distortion of their professional meaning. On first use, the relevant function is explained in Chinese.
Avoid absolute statements that may be misleading, including “guaranteed returns,” “certain profits,” “fully autonomous,” and “zero risk.”
Separate “AI autonomy” from “execution authority”: capabilities may be automated, but regulated or high-impact operations remain subject to risk rules, authorization, and human oversight.