Singapore, Aug. 18, 2026 (GLOBE NEWSWIRE) -- Questflow is building a “financial Codex” that turns professional investment strategies into transparent, configurable, and executable AI Finance Agents, giving investors broader access to expert financial intelligence while keeping them in control of their capital.
Questflow today announced a major brand evolution, repositioning the company as a platform for AI Finance Agents and advancing its vision of building a “financial Codex.” The platform is designed to help leading investors turn their market strategies, research frameworks, and decision-making processes into AI Finance Agents that retail investors can discover, understand, follow, and use across markets. The new positioning reflects Questflow’s broader mission: Financial Intelligence for All.
Markets are more open than ever. Robinhood expanded access to brokerage accounts, Hyperliquid brought sophisticated onchain markets to a broader audience, and AI trading tools have begun automating market monitoring, order placement, and portfolio rebalancing. Access to markets has been democratized.
Yet one of finance’s most important advantages remains concentrated: the judgment behind the trade.
Previous waves of financial technology gave investors better access and faster execution, but they did not give everyone the frameworks used by top investors to decide what is worth buying, why and when to enter, how much risk to take, or when to exit.
Questflow is built to close that gap. Each AI Agent captures a top investor’s strategy framework -including thesis, signals, triggers, risk rules, market coverage, and execution preferences, and turns it into a transparent system. Retail investors can discover these AI Agents, understand how they think, set personal permissions and risk boundaries, and let them invest across markets.
“Market access was the first step. The next is access to the intelligence behind the trade,” Bob Xu, Founder of Questflow. “Our mission is Financial Intelligence for All, giving everyone access not only to markets, but also to the strategy frameworks and decision-making discipline of top investors.”
Moving Beyond Trading Bots and Generic AI Advice
Current AI applications in finance generally follow one of two paths.
The first focuses on execution. These products automate processes such as account access, order placement, portfolio rebalancing, and trade management. Although they can reduce operational friction, they provide limited assistance with the underlying investment decision.
The second focuses on AI-generated financial advice. These tools use large language models to produce research summaries, signals, market commentary, and recommendations. However, generic AI models may struggle to reproduce the highly specialized, contextual, and risk-sensitive judgment required for professional investing.
Questflow identifies three structural limitations in today’s market.
First, strong investment judgment is often unstructured. It exists in portfolio managers’ experience, conversations, research notes, decision rules, and accumulated intuition. This expertise cannot always be reconstructed by combining isolated data points through a general-purpose language model.
Research referenced by Questflow from Bridgewater Associates and Thinking Machines Lab illustrates the potential value of specialist financial intelligence. In a financial information-filtering task, a customized model trained using expert data reportedly achieved an accuracy rate of 84.7%, compared with 78.2% for a general-purpose model, while operating at approximately one-thirteenth of the inference cost.
Questflow believes this suggests that domain expertise—and the ability to structure it—is at least as important as the underlying model.
Second, financial data, AI models, research tools, and trading venues are often disconnected. An AI system may generate a recommendation, but the user must still move to a separate exchange, brokerage, or wallet to execute it. This fragmentation creates an incomplete workflow between analysis and action.
Third, many existing participation models lack transparency. Traditional signal groups and copy-trading products may show users what to buy without clearly explaining why the strategy entered a position, when it should exit, or how much drawdown it is designed to tolerate.
Professional strategy providers face a related problem. Many possess tested investment processes and risk-management frameworks but lack the infrastructure to convert that expertise into transparent, scalable products. Their current options are often limited to publishing signals on social media or providing private trading guidance—models that can be difficult to scale, monetize, or verify over time.
A Two-Sided Marketplace for Financial Intelligence
Questflow is addressing these challenges by developing a two-sided marketplace.
On the supply side, professional investors and strategy providers can structure and distribute their investment frameworks through AI agents. On the demand side, users can discover, evaluate, subscribe to, and execute those strategies while retaining control over capital allocation and risk parameters.
Questflow’s central premise is not that a general-purpose AI system should independently predict financial markets. Instead, the company seeks to use AI to structure and operationalize human expertise.
The source of investment alpha remains human judgment. AI serves as the layer that makes this judgment reproducible, explainable, monitorable, and executable.
Through the platform, an investment methodology can be translated into a configurable system incorporating its research logic, entry criteria, portfolio rules, risk limits, and exit mechanisms. Users can then access the system as an intelligent financial agent rather than merely receiving a signal or static report.
Structuring Expert Judgment into Financial Agents
Questflow’s platform is designed around four connected layers: models, skills, data, and execution.
At the model layer, Questflow integrates leading AI models and allows users to select different models for different financial tasks, including research, analysis, monitoring, and execution. The company is also developing a benchmarking framework to compare financial models across real-world use cases, helping users assess which underlying model is most suitable for a given task.
At the skills layer, Questflow enables AI agents to follow specialized financial methodologies rather than relying exclusively on general model reasoning. Users can install packaged investment frameworks or upload their own trading systems, research processes, and decision rules.
These skills are intended to define how an agent evaluates information, identifies opportunities, applies risk controls, and responds to changing market conditions.
At the data layer, plugins can connect agents to sources such as financial news, market prices, blockchain activity, and social-media information. This gives agents access to continuously updated market signals and allows them to monitor relevant changes.
At the execution layer, accounts can be connected to exchanges, brokerages, and wallets. This enables a strategy to move from research and analysis to monitored execution within the user’s predefined permissions.
Together, these layers convert an investor’s end-to-end process into modular components that can be configured and recombined: model-based reasoning, professional methodology, real-time data access, and transaction execution.
Creating Incentives for Strategy Providers
Once an investment strategy has been structured, the next challenge is distribution.
Questflow plans to allow professional strategy providers to publish verified strategies as Funds, the platform’s internal term for strategy portfolios. These products are not regulated investment funds.
Strategy providers may earn fees based on factors such as assets allocated to their strategies and performance. Questflow believes this structure can create stronger incentives for providers to improve their methodologies, maintain transparent performance records, and build durable relationships with users.
Rather than monetizing one-time signals or private trading groups, providers can potentially build scalable financial products around their expertise.
For users, the marketplace is intended to function like a transparent strategy marketplace. Investors will be able to filter strategies by market, asset class, investment approach, and risk profile.
Before subscribing, users can review the strategy’s logic, risk boundaries, historical behavior, and execution framework. They can then determine how much capital to allocate and set their own risk limits.
Questflow believes this structure can align incentives on both sides: strategy providers receive a repeatable channel for monetizing professional knowledge, while users receive greater transparency and control over the strategies they follow.
Keeping Users in Control of Their Assets
The ability of an AI agent to execute financial transactions creates an essential question: How can users trust an AI system with their money?
Questflow’s role is to provide the intelligence and execution infrastructure that powers AI Finance Agents, not to take custody of users’ assets.
The platform is being designed so that users retain control over their funds, permissions, and risk boundaries. AI agents may act only within explicitly defined rules, such as capital-allocation limits, maximum position sizes, stop-loss conditions, approved markets, and transaction-approval requirements.
Users can determine how much capital an agent may deploy, what actions it is authorized to take, and whether transactions require additional approval.
In this model, the user keeps control of the steering wheel while the AI operates only within clearly defined lanes.
Building a “Codex for Finance”
Questflow’s ambition is inspired by the concept of a software-agent harness: an operating framework that allows AI to complete complex tasks reliably by combining models, tools, instructions, permissions, and execution environments.
In software development, systems such as coding agents can generate code, debug applications, call external tools, and complete multi-step engineering tasks through such a framework.
Questflow is applying a similar principle to finance.
Its financial-agent infrastructure is intended to help AI systems understand investor instructions while operating within the specialized requirements of financial research, decision-making, risk management, compliance controls, and trade execution.
The company believes this could establish a new model for AI in financial services—one in which AI does not merely generate commentary or automate isolated actions, but coordinates an entire investment workflow under human-defined rules.
Initial Focus on Digital-Asset Markets
Questflow is initially focusing on cryptocurrency markets, perpetual contracts, and prediction markets.
These markets operate continuously, produce large volumes of information, and experience rapid price movements. The cost of manually monitoring them around the clock can be significant, making them well suited to agent-based research, risk monitoring, and execution.
Questflow’s strategy is to validate the commercialization of structured investment judgment in these markets before expanding into additional areas of traditional finance.
Questflow brings experience in building AI-agent infrastructure and has previously announced collaborations and ecosystem relationships with organizations including Circle, Coinbase, Google, NEAR, and CoinGecko.
Questflow has also disclosed backing from investors and ecosystem participants including CyberFund, Delphi Labs, Systemic Ventures, Animoca Brands, HashKey Capital, and ElizaOS.
The company believes its prior work in AI-agent coordination provides a technical foundation for building financial agents capable of combining specialized reasoning, real-time information, permissions, and execution.
Addressing an Emerging Market Opportunity
Trading infrastructure, liquidity, and financial APIs have become increasingly mature. However, many AI-finance products remain focused on individual tools: automated trading bots, black-box signals, research assistants, or copy-trading platforms.
Questflow sees an opportunity to connect professional judgment with transparent execution in a single system.
Should the company successfully establish this model, Questflow could become an infrastructure layer through which global investors discover and access professional financial intelligence—allowing investment frameworks to be indexed, configured, combined, and executed in a manner similar to software libraries.
The company acknowledges that this approach is more complex than building a conventional trading bot or signal platform. It requires credible strategy providers, robust performance monitoring, transparent risk controls, secure integrations, and sustained user trust.
However, Questflow believes those same requirements could create a more defensible platform over time.
“Investment intelligence should become as accessible and composable as software,” Questflow said. “Our goal is to help expert financial knowledge move beyond private notes, closed institutions, and fragmented signals—and become something investors can understand, configure, and use without giving up control of their assets.”
About Questflow
Questflow is building a “Financial Codex” that enables top investors to transform their market strategies into AI Finance Agents that retail investors can follow, understand, and use to invest across markets. Its mission is Financial Intelligence for All.
By connecting AI models, financial skills, real-time data, strategy distribution, risk controls, and transaction infrastructure, Questflow aims to make expert investment frameworks more accessible while allowing users to retain control over their assets and execution permissions.
CONTACT: Una CHEN contact@questflow.ai
