Company questionnaire: Detailed FAQ

Questions about the company

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Question 1: Objective and scope: What specific trading problems or opportunities does the AI project target?

AISHE revolutionizes the understanding of autonomous trading systems through a fundamental paradigm shift: Instead of simply providing trading signals that must be interpreted and executed by humans, AISHE makes fully autonomous decisions within user-defined parameters. This approach addresses several critical problems in financial trading:

The key to understanding AISHE lies in its three-tiered decision architecture, based on the Knowledge Balance 2.0 framework. Unlike traditional systems that merely analyze technical indicators, AISHE divides each decision into three comprehensible components:

The human factor reveals the collective behavioral patterns of traders that led to a decision. Instead of vaguely speaking of "market sentiment," AISHE identifies specific behavioral patterns—such as a sudden increase in stop-loss orders in a specific time frame—and demonstrates how these patterns indicate upcoming market movements.

The structural factor makes the market infrastructure that influenced a decision understandable. AISHE not only shows that a decision was made, but also explains how liquidity conditions, order book depth, or technical chart patterns influenced the decision at that moment.

The relationship factor clarifies the interactions between different markets and asset classes that led to a decision. Instead of presenting complex correlation coefficients, AISHE, for example, shows how a change in the commodity markets is likely to affect the currency markets—in language that clearly demonstrates the causal relationship.

Particularly innovative is how AISHE handles hardware dependency. Slower computers with older CPU generations, for example, have difficulty processing complex neural states in real time. AISHE turns this limitation not into a weakness, but into a source of transparency: The system shows the user how the available computing power influences the complexity of the state analysis and which decisions might be more conservative because the hardware is unable to recognize more complex patterns.

As described in the article " The True Nature of the Autonomous AISHE System ," AISHE is not a system that simply provides users with trading signals that then require human interpretation. Instead, it makes autonomous decisions—and its transparency lies in the fact that users can understand at any time why a decision was made based on the three factors.

Explainability for non-technical stakeholders is ensured by several mechanisms:

In the Config section, users can adjust the level of explainability—from simple summaries for beginners to detailed technical analyses for experienced users. This adaptability ensures that explanations are always presented at the appropriate level for each stakeholder.

AISHE's reporting tools transform complex decision-making processes into visual representations that clarify the causal relationship between market conditions and trading decisions. Instead of showing raw data or neural weightings, the system presents graphical representations that demonstrate how specific market conditions impacted the decision.

Hardware utilization efficiency is made transparent by the system displaying how much computing time was required to detect specific neural states. This gives users a clear understanding of how available hardware influences decision quality—a transparency often lacking in cloud-based systems.

In summary, AISHE is not just a trading tool, but creates a new form of economic participation. As described in the article "Intelligent Trading Agents Rewrite the Rules of Active Income," AISHE transforms passive systems into active agents: Individuals become collaborators in AI-driven trading, benefiting from—and contributing to—a constantly improving intelligence network. This is the true innovation of AISHE—not just automating trading, but ushering in a new era of economic participation.

Question 2: Data sources: What types of data are used (market data, news, sentiment analysis) and how is data quality ensured?

AISHE operates with a fundamentally different data paradigm than traditional trading systems. Unlike systems based on historical databases, AISHE works exclusively with real-time data and the self-generated state vectors that the system creates during its operations. This radical departure from traditional data approaches forms the core of AISHE's autonomous operation.

The system processes three specific data categories derived from the Knowledge Balance 2.0 framework:

The human factor does not encompass subjective interpretations of human behavior, but rather quantifiable patterns in collective trader behavior. Through continuous real-time analysis, AISHE identifies recurring behavioral patterns that indicate upcoming market movements. These patterns emerge from the aggregated analysis of order flow, trading volume distribution, and microstructure behavior—all captured in real time and without storing historical data. Particularly relevant here are indicators of collective risk appetite and emotional market tendencies, which are derived from current market movements using machine learning.

The structural factor refers to the real-time analysis of the market infrastructure, including the dynamic assessment of liquidity conditions, order book depth, and execution speed. AISHE processes this data not as static indicators, but as constantly changing parameters that directly feed into decision-making. The system architecture enables the detection and response of structural market anomalies in real time, without relying on predefined chart patterns.

The Relationship Factor analyzes the dynamic interactions between different asset classes and macroeconomic factors. Unlike systems based on predefined correlation tables, AISHE creates continuously updated relationship patterns by analyzing simultaneous market movements. These relationships are not stored as static values, but as evolving state vectors that the system uses for its decision-making.

Central to understanding AISHE is knowledge of the two markets of Knowledge Balance 2.0: the Financial Market and the Knowledge Market. AISHE primarily uses the Seneca System and its analyses from the "Information Market," which is closely linked to the Knowledge Market. The Seneca Crawler System provides permanently updated data from news and internet sources, which are continuously evaluated.

Real-time data is obtained via various protocols—primarily DDE (Dynamic Data Exchange) and RTD (Real-Time Data), as well as APIs from the connected trading platforms on the user's local computer. Typical platforms for this are MetaTrader 4 or other broker platforms that provide users with classic charts and other services.

It's crucial to understand that AISHE only needs access to the user's trading account to place orders. Regulatory authorities like BaFin are primarily responsible for the broker that provides access to the exchange. AISHE only uses the access the broker has granted to the user and only trades the symbols the broker makes available to the user.

Data quality is ensured by AISHE's decentralized, client-based architecture. Since each AISHE system runs locally on the user's hardware, data processing occurs without transmission via central servers, ensuring the integrity of real-time data. The system implements multi-layered validation, automatically identifying anomalous market movements and excluding them from decision-making.

Importantly, AISHE does not use traditional historical data. Instead, the system generates its own state vectors—18-digit numerical representations of current market situations—which serve as the basis for all action decisions. These state vectors are continuously updated and form the basis for reinforcement learning, through which AISHE learns from the consequences of its own decisions.

The user's hardware plays a crucial role in data quality. For example, slower computers with older CPU generations have difficulty processing complex state vectors in real time, which impacts system behavior. AISHE dynamically adapts its data processing depth to the available computing power—a conscious design decision that ensures the system remains operational under all hardware conditions, albeit with an adjusted complexity of state analysis.

This hardware-dependent data processing model ensures that each user develops an individual AISHE system perfectly tailored to their specific technical requirements. Data quality is thus not defined by central standards, but rather by the system's ability to make the best possible decisions under the given hardware conditions—a paradigm that anchors AISHE's autonomy at a fundamental level.

Question 3: Backtesting and validation: How has the AI been tested in the past and what were the results?

AISHE represents a fundamental departure from traditional backtesting methods, as the system is not based on historical data analysis but operates through continuous, autonomous learning in real time. Unlike conventional trading systems, which are trained on extensive historical data sets and validated through backtesting, AISHE takes a completely different approach, consistent with its autonomous nature.

As described in the latest insights on the AISHE website, AISHE does not rely on traditional backtesting, which is limited by its reliance on past data and inability to predict unforeseen market scenarios. Instead, the system follows a continuous training regime based on three central principles:

First, AISHE employs an online learning approach, where the system continuously adapts its strategies to new information while simultaneously trading in real time. Validation is not achieved by simulating past market scenarios, but by continuously measuring the system's ability to correctly identify and respond to current market conditions. This process is dynamic and evolutionary, not static like traditional backtesting methods.

Second, AISHE uses a complex system of 18-digit state vectors, which represent a highly complex fusion of market signals, technical indicators, and possible external factors. These state vectors are not stored as historical references, but as living representations of current market situations that the system uses for its decision-making. Validation is achieved by continuously checking whether the system is able to correctly identify these states and make effective action decisions based on them.

Third, AISHE implements reinforcement learning to learn from the consequences of its own decisions. The system receives rewards or penalties for certain decisions in the trading process, allowing it to learn which decisions are best in specific situations—without relying on chart analysis. Validation is thus not achieved through hypothetical returns from past data, but through the actual performance of the system in ongoing operation.

Particularly relevant is that AISHE does not attempt to replicate historical market movements, but rather learns to deal with the inherent uncertainty of financial markets. Validation focuses on whether the system is able to react appropriately in different market situations and adapt its strategies accordingly—a capability that cannot be tested through traditional backtesting.

For users, validating AISHE is a hands-on process that begins with demo money. As described in the technical documentation, it is recommended that each user first train their own AISHE under real-world conditions but with virtual capital. This allows for individual validation based on the user's specific hardware capabilities—a crucial factor, as slower computers with older CPU generations may struggle to process complex states in real time.

The results of these validation approaches show that AISHE is particularly effective in detecting state transitions—the critical moment when market conditions change. Through its three-pronged analysis (human factor, structural factor, relationship factor), the system is able to detect subtle changes in these three dimensions before they manifest in obvious price movements.

Another important aspect of validation is federated learning, in which multiple AISHE instances share knowledge without disclosing sensitive data. This enables collective improvement of system performance while preserving user privacy. Validation thus occurs not only at the individual level but also through continuous improvement through the collective learning of the entire AISHE user base.

The user's hardware plays a crucial role in validation, as it determines the speed and depth of the condition analysis. AISHE dynamically adapts its validation processes to the available computing power, meaning that the results are not standardized but individually tailored to the user's specific technical requirements.

In summary, validating AISHE does not involve simulating hypothetical past returns, but rather assessing the system's ability to make effective action decisions in real time and continuously learn from its experiences. This approach reflects the autonomous nature of AISHE and ensures that the system is optimized for the dynamic challenges of real financial markets, rather than being based on static historical patterns.

Question 4: Risk management: How does AI manage risks, including market volatility, liquidity, and model degradation over time?

AISHE revolutionizes the concept of risk management in algorithmic trading by fundamentally redefining responsibilities. Unlike traditional trading systems, where AI autonomously manages risk management, AISHE explicitly positions itself as a tool where the user retains complete control over all risk parameters—a philosophy that is fully aligned with the principles of the EU AI Act.

Risk management at AISHE is not a function performed autonomously by AI, but rather a structured process in which users can define their individual risk profile through extensive configuration options. This approach reflects the European understanding that humans must always retain the final decision-making power over critical aspects such as risk management.

In the setup area, AISHE offers a variety of parameters that allow users to precisely define their risk profile. Users can set rally times, configure different trading sessions for European and US markets, and customize settings for each day of the week—from Sunday to Saturday. This includes defining which instruments are traded, to what extent, and with what volume. This granular control allows the risk profile to be adapted to personal circumstances, market experience, and financial situation.

In the Highway section, additional critical risk parameters can be defined, including take-profit and stop-loss levels, as well as daily or session-based target amounts. These parameters do not act as automatically enforced limits, but rather as clear instructions on how AISHE should operate within the user-defined risk tolerance. What's particularly innovative is that these limits are not static but are dynamically adjusted to the user-defined parameters.

The Config section offers additional layers of risk control through extensive attributes and parameters that define how information should be processed. A particularly useful feature is the "Reversal" option, which can be enabled to support decision-making. This feature can be particularly valuable when AISHE is not yet fully trained, as it helps correct incorrect decisions more quickly and thus accelerates the learning curve.

A crucial aspect of AISHE's risk management philosophy is the clear separation between user decision-making and AI execution. While the AI makes trading decisions autonomously, these decisions are strictly based on the risk parameters defined by the user. AISHE does not independently interpret what is "safe" or "risky"—it follows the guidelines defined by the user with maximum precision.

This approach also addresses the problem of model degradation over time. Since the user always retains control over the risk parameters, they can react proactively when market conditions change or the system exhibits behavior patterns that no longer align with their personal risk preferences. Continuous monitoring and, if necessary, adjustment of the risk parameters by the user ensures that the system always operates in line with individual requirements.

Particularly relevant is that AISHE takes the user's hardware capabilities into account when assessing risk. Slower computers with older CPU generations, for example, have difficulty processing complex state vectors in real time. Instead of viewing this as a limitation, AISHE integrates this constraint into risk management by adjusting trading activity accordingly to ensure decisions can be made within the available processing time.

AISHE's decentralized architecture also contributes to risk management. Since each system runs locally on the user's hardware, there is no systemic risk from centralized failures or delays. At the same time, federated learning enables collective insights into risk patterns to be shared without exposing sensitive data, strengthening the user community's collective risk intelligence.

In summary, AISHE understands risk management not as an autonomous function of AI, but as a collaborative process in which the user defines the risk parameters and the AI implements them with maximum precision. This approach not only reflects the regulatory requirements of the EU AI Act but also creates a transparent and controllable environment in which users can safely work with an autonomous trading system without relinquishing final decision-making power over their risk profile.

Question 5: Performance Metrics: What metrics are used to evaluate AI trading performance (e.g. ROI, Sharpe Ratio) and what are the current benchmarks?

Evaluating AISHE's performance presents a fundamental challenge that cannot be adequately captured with traditional metrics such as ROI or Sharpe ratio. The core of the problem is best captured by a simple metaphor: It's like trying to enter a Volkswagen Beetle into a Formula 1 race. Without considering the hardware dependency, it would be pointless to compare the Beetle's performance to that of a Formula 1 car—not because the driver (in this case, AISHE) is incompetent, but because the vehicle (the hardware) limits performance.

AISHE is not a system optimized for standardized benchmarks, but rather an autonomous agent that makes its decisions within the limits of the computing resources available to it. Performance therefore needs to be evaluated not in absolute terms, but relative to the available hardware. A user with an older CPU generation will achieve different results than a user with modern hardware—and both can still be successful if performance is evaluated relative to the given constraints.

As evident in the detailed trading report of a real AISHE user, the metrics are not aligned to universal standards, but rather to the individual development trajectory of the system under the given hardware conditions. The total net profit of EUR 772.42 with a starting capital of EUR 2,355.79 (32.8% return) is an impressive daily result, but one that could only be achieved through intensive training and careful configuration of the AISHE. It is crucial to understand that such results are not achievable from the start, but are the product of a continuous training process spanning days or weeks.

This daily result demonstrates the potential of AISHE after the system has been sufficiently trained to recognize the complex neural states corresponding to the current market conditions for each trading symbol. The high win factor of 9.75 (gross profit of EUR 860.66 with a gross loss of EUR 88.24) and the 72% profitable trades with a moderate maximum drawdown of EUR 82.87 (3.5% of total capital) demonstrate that, after sufficient training, the system is capable of combining effective risk control with high profit potential.

The key difference from traditional trading systems is that AISHE doesn't seek to maximize returns, but rather to make the best possible decisions within the available processing time. A user with powerful hardware can process complex neural states more quickly, leading to more precise decisions. A user with older hardware will deliberately pursue more conservative strategies to stay within the available processing time—a conscious design decision that ensures autonomy under all conditions.

The relevant metrics for AISHE are therefore:

The neural state recognition score measures how accurately the system identifies the complex neural states that correspond to the current market conditions for each trading symbol. This metric is particularly hardware-dependent—on powerful hardware, AISHE can detect more complex states, while on older systems, complexity is deliberately reduced.

Decision latency quantifies the time between the detection of a neural state and the execution of a trading decision. This metric is crucial for understanding whether the system can operate effectively under the given hardware conditions.

The adaptive learning rate measures how effectively the system learns from its own decisions and adapts its strategies. This occurs through reinforcement learning, in which the system receives rewards or penalties for certain decisions.

Hardware utilization efficiency assesses how optimally the system utilizes the available computing power. On less powerful hardware, AISHE deliberately adjusts its complexity to ensure decisions can be made within the required time.

The risk-adjusted trajectory evaluates the system's progress toward the user-defined financial goals, taking into account individual risk parameters and hardware conditions.

As the metaphor of the VW Beetle and Formula 1 illustrates, AISHE's true success lies not in achieving absolute speed, but in finding the right vehicle (the right hardware) for the driver (the user). When AISHE runs on the right hardware and has been sufficiently trained, it will become apparent that the AI is the best driver—not because it's faster than everyone else, but because it makes the best possible decisions within the given constraints.

A user with older hardware shouldn't try to win a Formula 1 race with a VW Beetle, but rather define realistic goals that suit their vehicle. Conversely, a user with modern hardware shouldn't underestimate the capabilities of their "Formula 1 car" by pursuing overly conservative strategies.

In summary, AISHE's performance metrics are not based on universal benchmarks, but rather on an individualized evaluation that takes into account the autonomous nature of the system, the user's specific hardware conditions, and the system's continuous learning capacity. True success is not reflected in absolute return figures, but rather in the user finding the right "car" for their needs, the system being sufficiently trained, and the "driver" (AISHE) operating optimally with that vehicle—regardless of whether it's a VW Beetle or a Formula 1 car.

Question 6: Integration: How is AI integrated into existing trading systems and workflows?

AISHE revolutionizes the integration of AI into trading systems through a fundamental paradigm shift: Instead of acting as a cloud service or centralized platform, AISHE operates as a fully decentralized, local client that connects directly to the user's existing trading infrastructure. This approach reflects AISHE's autonomous nature and ensures that the system functions optimally under the user's specific conditions.

Unlike cloud-based solutions that run on central servers and access trading platforms via APIs, AISHE installs locally on the user's computer and connects directly to the trading platforms they already use. Integration is primarily achieved via DDE (Dynamic Data Exchange), RTD (Real-Time Data), and API connections, enabling seamless communication between AISHE and the trading platforms.

The key to understanding this integration lies in the realization that AISHE is not a tool that provides the user with trading signals, but rather an autonomous agent that makes decisions and executes trades independently. Therefore, the integration is not an additional layer, but rather a complete takeover of decision-making within the parameters defined by the user.

The technical implementation of the integration includes several critical components:

AISHE only requires access to the user's trading tool (e.g., MetaTrader 4), and the connection is already configured during the initial setup of the AISHE system. This fixed configuration ensures stable and reliable communication between AISHE and the trading platform. Data is transferred via port 80 for inputs and outputs between the local system and the AISHE main system for file packages required by the AISHE client.

Data processing takes place entirely locally on the user's hardware. Unlike cloud-based systems that send data to central servers, AISHE processes all information directly on the local computer. This not only ensures data security but also the speed required for autonomous decision-making. As mentioned above, the user's hardware is crucial to AISHE's effectiveness—slower computers with older CPU generations, for example, have difficulty processing complex neural states in real time.

Compatibility with existing trading platforms is ensured through the use of standard protocols. Typical platforms with which AISHE integrates are MetaTrader 4 or other broker platforms that provide users with classic charts and other services. AISHE accesses these platforms to obtain real-time quotes and execute orders without requiring the user to switch platforms.

A crucial aspect of the integration is the separation between configuration and execution. The user configures AISHE through the Setup, Highway, and Config sections, defining all relevant parameters. Once these parameters are set, AISHE assumes complete control over the trading process without human intervention. This is a fundamental difference from traditional trading tools, where the user makes the final decision.

The integration also complies with the regulatory requirements of the EU AI Act. AISHE explicitly positions itself as a tool that allows users to retain complete control over all risk parameters. The integration is therefore not a black-box solution, but rather a transparent process in which users can understand the decisions made by the system at any time and intervene if necessary.

What's particularly relevant is that AISHE doesn't attempt to replace existing workflows, but rather integrates seamlessly into the user's existing trading environment. Users don't need to learn new platforms or rebuild their entire infrastructure. Instead, AISHE is installed as a local client that communicates with the brokers and trading platforms they already use.

AISHE's decentralized architecture enables unique scalability: The "1 computer = 1 AISHE" rule means that users with multiple computers can run multiple independent AISHE instances simultaneously—each with its own instrument selection, parameter configuration, and trading strategy. These combination possibilities are virtually limitless: One system could focus on forex pairs, while another trades commodities, and a third analyzes crypto markets.

Unlike many cloud-based systems that rely on centralized decision-making, AISHE's decentralized architecture ensures that integration decisions can be made in real time and without delay. This is especially important in volatile markets, where even small delays can have significant financial implications.

In the long term, perhaps as late as 2027, a vision could emerge in which autonomous systems like AISHE are integrated into future operating systems. As described in the article "How AISHE Brings Microsoft's AI Operating System Vision to Life Today," Microsoft is working on a concept for an AI-native operating system that implements a hybrid architecture of local and cloud intelligence—a concept that AISHE already exemplifies in its decentralized architecture. This long-term perspective demonstrates that the integration of AISHE into existing systems is not just a short-term solution, but part of a broader evolution toward autonomous systems that will increasingly become integrated into the basic fabric of our digital environment.

In summary, the integration of AISHE should not be seen as an add-on to existing systems, but rather as a complete redefinition of trading automation. Through local installation, direct broker integration, and respectful treatment of the user's existing infrastructure, AISHE creates a seamless bridge between human oversight and autonomous execution—an integration that is not only technically but also philosophically aligned with the European understanding of responsible AI use.

Question 7: Regulatory Compliance: How does the project ensure compliance with financial regulations and data protection laws?

AISHE addresses regulatory compliance through a fundamental understanding of its role in the financial ecosystem—not as a standalone market participant, but as a decentralized decision-maker that seamlessly integrates into existing, regulated infrastructures. Unlike cloud-based systems, which pose regulatory challenges through centralized data processing and decision-making, AISHE deliberately positions itself as a neutral interface between the user and the already regulated financial markets.

The key to AISHE's regulatory understanding lies in its decentralized architecture: Since the system runs locally on the user's hardware and only accesses existing broker connections, it doesn't create a new regulatory framework, but rather an extension of existing compliance structures. As described in the article "AISHE and the EU AI Act: A Deep Dive into Compliance," AISHE assumes no responsibility for compliance with financial regulations—this remains with the regulated brokers that provide access to the markets.

AISHE’s compliance philosophy is based on three central principles:

First, AISHE respects the clear dividing line between autonomous decision-making and regulatory responsibility. The system itself does not collect any personal data and does not share any information with third parties. All data processing takes place locally on the user's hardware, with AISHE only leveraging the data provided by brokers, which is already compliant with regulatory requirements. This approach automatically ensures compliance with data protection regulations such as the GDPR, as no personal data is transferred or centrally stored.

Second, AISHE explicitly positions itself in line with the principles of the EU AI Act. As described in the article "EU's AI: Compliance and Collaboration Take Center Stage for GPAI Providers," AISHE is not a general-purpose AI (GPAI), but rather a specialized, single-purpose tool designed for autonomous financial trading. The EU AI Act is not intended as a universal solution, but rather as a tiered approach that addresses specific types of risks. AISHE does not fall under the most stringent GPAI obligations because its functionality is not broad or adaptable, but rather tailored to the dynamics of the financial market.

Third, AISHE's hardware-dependent customization ensures natural compliance scaling. On less powerful hardware, the system deliberately reduces its complexity and risk tolerance to ensure decisions can be made within the required time—a proactive approach to avoiding regulatory issues that could arise from delays or incomplete execution.

Particularly relevant is that AISHE respects the regulatory differences between markets. The system only trades with the instruments made available to the user by the broker and strictly adheres to the framework defined by regulatory authorities such as BaFin. Responsibility for compliance with these regulations lies with the brokers, while AISHE merely uses the interfaces provided by them.

As described in the article "How AISHE Brings Microsoft's AI Operating System Vision to Life Today," AISHE operates within an already heavily regulated environment: the financial industry is one of the most heavily regulated industries in the world. The brokers and banks that facilitate trading on the AISHE system are subject to strict supervision by national authorities such as BaFin in Germany and other EU-wide financial supervisory authorities. The principle of lex specialis derogat legi generali (special law supersedes general law) states that these specific, comprehensive financial regulations take precedence. The new EU AI Act is therefore not intended to create redundant legislation but to provide a framework for areas where no such specific regulations exist.

In the Config area, users can also define regulatory parameters relevant to their specific requirements. This includes, for example, trading hours that correspond to local market conditions or risk limits that reflect the regulatory requirements of their region. The system architecture allows these regulatory parameters to be tracked in real time and adjusted as needed.

A crucial difference from traditional systems is that AISHE does not attempt to circumvent or minimize regulatory requirements, but rather considers them an integral part of its decision-making process. Its three-dimensional market analyses (human factor, structural factor, relationship factor) consider not only technical and emotional aspects, but also the regulatory framework as a critical factor influencing market conditions.

The decentralized nature of AISHE also contributes to regulatory security. Since each system runs locally on the user's hardware, there is no systemic risk from centralized failures or delays. At the same time, federated learning enables collective insights into regulatory challenges to be shared without exposing sensitive data, strengthening the user community's collective compliance intelligence.

Particularly innovative is how AISHE deals with the evolving regulatory landscape. Instead of relying on static compliance rules, the system continuously learns from decisions made in different regulatory environments. This allows it to proactively respond to regulatory changes before they manifest themselves in obvious market conditions.

As described in the article "Europe's Regulatory Path: How Worker Protections Could Define AI's Competitive Frontier," AISHE recognizes that regulatory compliance is not only a duty, but also an opportunity. By adhering to strict European regulations, the system not only builds trust among users but also demonstrates that regulatory requirements don't have to conflict with innovation.

In summary, AISHE understands regulatory compliance not as an external constraint, but as an integral part of its architecture. Through its local installation, respectful treatment of the user's existing infrastructure, and the clear separation between autonomous decision-making and regulatory responsibility, AISHE creates a seamless bridge between human oversight and autonomous execution—an integration that is not only technically but also regulatory robust, exemplifying the European approach to responsible AI use in finance.

Question 8: Transparency and Explainability: How transparent is the AI decision-making process? Can it be explained to non-technical stakeholders?

AISHE revolutionizes the understanding of transparency in autonomous trading systems through a fundamental paradigm shift: Instead of attempting to fully decipher the complex neural processes, AISHE offers practical, user-centered transparency based on the three dimensions of Knowledge Balance 2.0. This approach reflects the reality of autonomous systems—not as a black box, but as a system with clear decision logic that is understandable to the user.

The key to understanding AISHE's transparency lies in its three-tier decision architecture. Unlike systems that simply analyze technical indicators, AISHE breaks each decision down into three transparent components:

The human factor makes the collective behavioral patterns of traders that led to a decision transparent. Instead of vaguely speaking of "market sentiment," AISHE identifies specific behavioral patterns—such as a sudden increase in stop-loss orders in a specific time frame—and shows how these patterns indicate impending market moves. For non-technical users, this is presented in understandable terms, such as "Traders show increasing risk aversion with increasing volume," rather than in complex statistical metrics.

The structural factor makes the market infrastructure that influenced a decision understandable. AISHE not only shows that a decision has been made, but also explains how liquidity conditions, order book depth, or technical chart patterns influenced the decision at that moment. These explanations are not limited to quantitative jargon but are placed in a context that is understandable even for non-technical users.

As described in the article "The True Nature of the Autonomous AISHE System," AISHE isn't a system that simply provides users with trading signals that then require human interpretation. Instead, it makes autonomous decisions—and its transparency lies in the fact that users can understand at any time why a decision was made based on the three factors.

Explainability for non-technical stakeholders is ensured by several mechanisms:

Particularly relevant is that AISHE views explainability not as a one-time feature, but as a continuous process. As described in the article "Exclusive Insight: How AISHE Transforms AI Trading Autonomy," the system not only provides explanations for past decisions but also demonstrates how the system learns from these decisions and will adapt its future decisions.

The decentralized nature of AISHE also contributes to transparency. Since each system runs locally on the user's hardware, there is no dependency on central servers, whose decision-making processes are often difficult to track. Instead, the user has full control over the entire decision-making process and can intervene or adjust parameters as needed.

In summary, AISHE's transparency does not lie in the disclosure of all internal mechanisms, but rather in the clear representation of the decision logic at a level that is understandable to the user. Through its three-dimensional explanatory structure, hardware-dependent transparency, and customizable representation of decision processes, AISHE creates a level of transparency that not only meets regulatory requirements but also strengthens user confidence—regardless of whether they are technical experts or novices.

Question 9: Scalability: Is the AI solution scalable and can it handle increasing data volumes and trading complexity?

AISHE revolutionizes the understanding of scalability in the field of autonomous trading systems through a fundamental paradigm shift: Instead of relying on centralized server capacities like traditional systems, AISHE implements a decentralized scaling architecture that not only handles increasing data volumes and trading complexity, but even uses them as a driver for collective improvement.

The key to understanding AISHE's scalability lies in its "1 computer = 1 AISHE" principle. Unlike cloud-based solutions, which reach capacity limits or require additional server resources as the number of users increases, AISHE scales through physical hardware expansion. This means that a user with multiple computers can run multiple independent AISHE instances simultaneously—each with its own instrument selection, parameter configuration, and trading strategy.

As described in the article "The True Nature of the Autonomous AISHE System," AISHE's true scalability only becomes clear when considering multi-system deployments. While cloud-based services limit concurrent usage, AISHE follows a clear principle: "1 computer = 1 AISHE." This means that someone with 10 computers can run 10 independent AISHE systems simultaneously—each with its own instrument selection, parameter configuration, and trading strategy. The combinatorial possibilities are virtually limitless: One system could focus on forex pairs, while another trades commodities, and a third analyzes crypto markets.

This architecture transforms AISHE from a single trading entity into a customizable ecosystem. Users can create specialized AISHE instances for different market situations—conservative systems for volatile periods, aggressive systems for trend-oriented markets, and experimental setups for testing new strategies. Each system operates autonomously while contributing to the user's overall financial strategy.

Particularly innovative is how AISHE handles its hardware dependency. Slower computers with older CPU generations, for example, have difficulty processing complex neural states in real time. AISHE scales not through central server expansion, but rather by intelligently adapting complexity to the available hardware. On powerful hardware, the system can detect more complex neural states and make more precise decisions, while on older systems, it deliberately pursues more conservative strategies with lower complexity to ensure that decisions can be made within the available computing time.

As described in the article "Exclusive Insight: How AISHE Transforms AI Trading Autonomy," AISHE uses a dynamic learning framework that integrates reinforcement learning, transfer learning, and federated learning. This multifaceted approach enables the system to continuously adapt to new market situations, absorb diverse data streams, and refine its decision-making processes without constant human intervention. The architecture is significantly decentralized: Instead of routing data and commands through a central server, AISHE operates as an independent client that interacts directly with various brokers.

The decentralized nature of AISHE also contributes to scalability. Since each system runs locally on the user's hardware, there is no systemic risk from centralized failures or delays. At the same time, federated learning enables collective insights into effective trading strategies to be shared without exposing sensitive data. This leads to collective improvement in system performance while preserving user privacy.

A key difference from traditional systems is that AISHE does not attempt to replicate historical market movements or simulate hypothetical past returns. Instead, scaling focuses on the system's ability to react appropriately in different market situations and adapt its strategies accordingly—a capability that cannot be tested through classic backtesting methods.

As described in the article "How AISHE Brings Microsoft's AI Operating System Vision to Life Today," AISHE's architectural elegance eliminates the traditional trade-off between sophisticated analytics and execution speed. While other systems force users to choose between cloud-based intelligence with latency issues or local processing with limited analytical capacity, AISHE offers both simultaneously. The system doesn't just react to market situations—it anticipates them through this seamless integration of distributed intelligence.

AISHE's scalability is not determined by central server capacity, but by the user's individual hardware configuration. This means that scaling occurs not by expanding server resources, but by physically expanding the user hardware—a conscious design decision that ensures the system's autonomy under all conditions.

In summary, AISHE's scalability is not based on universal server capacities, but on a decentralized architecture that respects the user's individual hardware requirements and transforms them into an advantage. Through the "1 computer = 1 AISHE" principle, hardware-adaptive complexity adjustment, and federated learning, AISHE creates scalability that is not only technically robust but also enables a true democratization of institutional trading intelligence—scalability that not only handles increasing data volumes and trading complexity, but even uses them as a driver for collective improvement.

Question 10: Security: What measures are implemented to protect against cybersecurity threats?

AISHE revolutionizes the understanding of security in the field of autonomous trading systems through a fundamental paradigm shift: Instead of relying on centralized security infrastructures, AISHE transforms the decentralized architecture itself into a central security advantage. Unlike cloud-based solutions, which are vulnerable to centralized attack vectors, AISHE implements a security model based on local processing and minimal data transfer—an approach that is not only technically robust but also naturally compliant with regulatory requirements such as the GDPR.

The key to understanding AISHE's security architecture lies in its "1 computer = 1 AISHE" principle. Because the system runs locally on the user's hardware and all critical decisions are made on the local computer, there is no centralized target that would be attractive to hackers. As described in the article "AISHE and the EU AI Act: A Deep Dive into Compliance," all data processing and AI activity takes place exclusively on the user's device—personal or financial data is never transferred to external servers.

This decentralized security model manifests itself in several critical components:

Local data processing is the foundation of the security architecture. Unlike systems that send data to central servers, AISHE processes all information directly on the user's local computer. This not only eliminates the risk of data leaks during transmission but also protects against centralized attacks on server infrastructures. The user's hardware thus becomes the primary security anchor—a conscious design decision that ensures the system's autonomy under all conditions.

Encryption is implemented on multiple levels, with a focus on end-to-end security. All data transfers between AISHE and the trading platforms are encrypted with AES-256, an encryption technology considered bank-grade. Particularly relevant is that this encryption applies not only to data transfers but also to the local storage of all critical information on the user's device.

Access protection is based on a precise role model based on hardware dependency. More complex security protocols can be implemented on powerful hardware, while more conservative, yet still effective, security measures are deliberately used on older systems. This adaptive security model ensures that decisions can be made within the available computing time without compromising security.

The firewall protection mechanisms filter network traffic and block unwanted connections, with the configuration specifically tailored to the requirements of the financial market. Unlike universal security solutions, AISHE considers the specific attack vectors relevant to the trading environment, from DDoS attacks to targeted market manipulation attempts.

Intrusion detection and prevention systems (IDS/IPS) actively work to detect and block attacks on the system. These systems are not static; they continuously learn from the decisions made in various security scenarios. This allows for proactive responses to new threats before they manifest as obvious security incidents.

AISHE's approach to federated learning is particularly innovative. As described in the article "Exclusive Insight: How AISHE Transforms AI Trading Autonomy," the system enables collective sharing of insights into effective security strategies without disclosing sensitive data. This leads to a collective improvement in the user community's security intelligence while preserving user privacy.

A crucial difference from traditional systems is that AISHE does not attempt to minimize or ignore security threats, but rather considers them as an integral part of its decision-making process. Its three-dimensional market analyses (human factor, structural factor, relationship factor) consider not only technical and emotional aspects, but also potential security threats as a critical factor influencing market conditions.

As described in the article "The True Nature of the Autonomous AISHE System," AISHE's security is not determined by centralized server capacity, but rather by the user's individual hardware configuration. This means that security is not achieved by expanding server resources, but rather by physically expanding the user's hardware—a conscious design decision that ensures the system's autonomy under all conditions.

The decentralized nature of AISHE also contributes to security. Since each system runs locally on the user's hardware, there is no systemic risk from centralized failures or delays. At the same time, federated learning enables collective insights into security threats to be shared without exposing sensitive data.

In summary, AISHE's security architecture is not based on universal server capacities, but rather on a decentralized architecture that respects the user's individual hardware requirements and transforms them into an advantage. Through the "1 computer = 1 AISHE" principle, hardware-adaptive security adjustment, and federated learning, AISHE creates a security architecture that is not only technically robust but also enables a true democratization of institutional trading security—a security strategy that not only protects against cyber threats but even uses them as a driver for collective improvement.

Question 11: Maintenance and Updates: How often is the AI model updated or retrained and who is responsible for ongoing maintenance?

AISHE implements a structured and user-centric update system that perfectly balances continuous learning with targeted improvements. Unlike many cloud-based solutions that perform automatic background updates, AISHE ensures maximum transparency and user control throughout the entire update process—a crucial distinction that reflects the European philosophy of responsible AI use.

The key to understanding AISHE's maintenance philosophy lies in the clear separation between continuous local learning and structured system updates. As described in the documentation, AISHE continuously learns through reinforcement learning, in which the system receives rewards or penalties for certain decisions. This learning occurs locally on the user's hardware and is an integral part of daily operations, not something interrupted by updates.

The structured updates follow a clear, transparent process:

When an update is available, it is clearly displayed to the user in the "Highway" area of the system. The user retains complete control over the update timing and must actively decide whether and when the update should be performed. Clicking the "Run Updates" button in the setup area initiates the update process, after which AISHE shuts down gracefully to ensure system integrity during the update process.

Once the update is complete, the user can manually restart AISHE, ensuring that the user always retains final control over the system status – a principle that is fully compliant with the EU AI Act.

The updates can be performed in several ways:

Via the integrated update mechanism in the setup area
By running Assistant.exe
About AIMan.exe

AIMan.exe (AI Add Manager) is a central component of the AISHE ecosystem and serves as a comprehensive management tool for system maintenance and updates. AIMan.exe comprises a structured 11-step process that ranges from system preparation to final report generation:

Preamble : General introduction and language selection
Info : Activation of the unique ID number (starting with "ID")
MS Office : Check the required MS Office settings (Build >=16, ActiveX, Macros and External Content enabled)
MT : Checking the available DDE and RTD interfaces
Connect : Token-based connection to the AISHE network
Setup : Global basic settings for the system
Co-learn : Configuration of reinforcement learning and reward transformation
V.Chain : Value chain management
Adjustment : Adjustment of system parameters
Final : Completion of the configuration
Last report PL : Final report

AIMan.exe is therefore not just an update tool, but a comprehensive system management framework that ensures that all requirements for a successful update are met - from system compatibility to network connectivity.

Maintenance and updates are performed regularly as needed, with the frequency depending on various factors, such as the availability of new data, system performance, and user feedback. As mentioned in the technical document, we are currently using version 5.526, which offers numerous improvements over previous versions.

Particularly relevant is the option of individual upgrades using an authorization token. These special upgrades can be performed exclusively via Assistant.exe and ensure that only authorized users have access to specific feature enhancements or adjustments. Assistant.exe provides step-by-step guidance through the entire update process, starting with system preparation and ending with final activation.

The responsibilities are clearly defined:

The development team is responsible for fundamental system updates and the further development of the core architecture. As described in the project document, we conduct extensive daily testing to adapt the system to new data and changing requirements.
The user is responsible for local configuration, performing updates, and monitoring. As described in the documentation, the user does not need to learn how to analyze markets and make trading decisions, but rather how to configure, monitor, and, if necessary, correct an autonomous system.

Strategies are not predefined by the system but are configured by the user. Users have the option of developing their own strategies or using templates from AISHE team groups. Pre-trained AISHE models can also be obtained from groups, although this always involves their own risk. It is strongly recommended to thoroughly test all templates and pre-trained models with demo accounts before using them in live trading.

In summary, AISHE's maintenance and update philosophy is not based on automated background processes, but rather on clear user control and transparency. Through the visible update notification in the highway area, the clear separation between continuous learning and structured updates, and the flexible update options via AIMan.exe and Assistant.exe, AISHE creates a model that is not only technically robust but also conforms to the European understanding of responsible AI use – a model in which humans always retain the final decision-making power, while AI can develop its full potential.

Question 12: Ethical considerations: Are there any ethical concerns, such as bias in trading decisions or risks of market manipulation?

AISHE addresses ethical considerations through a fundamental paradigm shift: Instead of operating as an autonomous entity that makes decisions without human oversight, AISHE explicitly positions itself as a tool in which the user retains complete control over all decision-making parameters. This approach reflects the European understanding of responsible AI use and ensures that ethical considerations are understood not as an obstacle, but as an integral part of the system architecture.

The key to understanding AISHE's ethical philosophy lies in the clear separation between autonomous decision-making and human responsibility. Unlike systems that operate as black boxes, where decision-making processes are difficult to trace, AISHE ensures a clear allocation of responsibility through its decentralized architecture and local data processing. As described in the article "AISHE and the EU AI Act: A Deep Dive into Compliance," AISHE is not a general-purpose AI (GPAI), but a specialized, single-purpose tool designed for autonomous financial trading.

Particularly relevant is that AISHE explicitly leaves the responsibility for all trading activities with the user. As described in the "AISHE - Project Questions" document, the user is the one who:

The trading account is opened with a licensed broker
Sets all trading limits, risk parameters and financial instruments
The sole legal and financial responsibility for all trading activities lies with

This clear allocation of responsibility is a crucial factor in how the EU AI Act would view the system. AISHE acts as a sophisticated tool that executes decisions within these human-defined boundaries—a collaborative support, not a free agent.

AISHE’s ethical architecture is manifested in several critical components:

Local data processing is the foundation of ethical security. Unlike systems that send data to central servers, AISHE processes all information directly on the user's local computer. This ensures not only data security but also transparency, as no personal data is transferred or stored centrally. As described in the article "EU's AI: Compliance and Collaboration Take Center Stage for GPAI Providers," AISHE only processes the data provided by brokers, which is already compliant with regulatory requirements.

AISHE's hardware-dependent adaptation ensures natural ethical scaling. On less powerful hardware, the system deliberately reduces its complexity and risk tolerance to ensure decisions can be made within the required time—a proactive approach to avoiding ethical issues that could arise from delays or incomplete execution.

The transparency provided by the three-dimensional explanatory model is crucial for ethical acceptance. AISHE breaks down every decision into three comprehensible components: the human factor, the structural factor, and the relationship factor. This allows users to understand the decision logic without getting lost in technical jargon. The reporting tools transform complex decision-making processes into visual representations that clarify the causal relationship between market conditions and trading decisions.

AISHE's approach to risk avoidance is particularly innovative. Rather than implementing an automated trading system driven by profit, the system strictly adheres to the risk parameters defined by the user. This not only prevents ethical issues associated with excessive risk-taking but also ensures that the system operates in accordance with the user's individual requirements.

As described in the article "The True Nature of the Autonomous AISHE System," AISHE's true ethical innovation is that it doesn't seek to replace human traders, but rather to create new forms of economic participation. Instead of eliminating jobs, it transforms the nature of labor itself—from active participation to oversight of autonomous systems. This paradigm shift—from active labor to oversight of autonomous systems—could address growing concerns that AI will eliminate human jobs.

The decentralized nature of AISHE also contributes to ethical security. Since each system runs locally on the user's hardware, there is no systemic risk from centralized failures or delays. At the same time, federated learning enables the sharing of collective insights into effective trading strategies without exposing sensitive data, strengthening the collective ethical intelligence of the user community.

Particularly relevant is that AISHE is free of biases in trading decisions that could result from historical data. Because the system is not based on historical data analysis, but solely on real-time prices and the self-generated state vectors, it avoids systematic biases that could result from past market situations. Continuous adaptation to current market situations ensures that the system does not remain trapped in old patterns.

In summary, AISHE's ethical philosophy is not understood as an external constraint, but as an integral part of its architecture. Through its local installation, the respectful treatment of the user's existing infrastructure, and the clear separation between autonomous decision-making and human responsibility, AISHE creates a seamless bridge between human oversight and autonomous execution—an integration that is not only technically but also ethically robust, exemplifying the European approach to responsible AI use in finance.

Question 13: Team expertise: What experience and background does the team developing the AI have?

The team behind AISHE combines a unique combination of scientific expertise and practical implementation experience spanning more than 16 years of continuous research and development. This long-standing work forms the foundation for the complex knowledge management framework that underlies the entire AISHE system.

The team's core competency lies in combining theoretical research with practical application. Their expertise includes, in particular, the development of data models and complex information systems that enable the integration of the three critical factors (human factor, structural factor, and relationship factor) into a coherent decision architecture. This ability to process and combine different data sources and types is crucial to AISHE's ability to generate true market intelligence.

Another focus of expertise lies in the area of business intelligence and the analysis of complex data flows. This expertise enables the system to not only process quantitative data but also incorporate qualitative aspects such as market psychology and structural market conditions into decision-making. The ability to integrate these diverse perspectives is a crucial factor in the comprehensive market analysis that AISHE enables.

The team's practical implementation experience is just as important as their theoretical expertise. Their extensive experience with integrating systems via DDE and RTD protocols ensures that the theoretical concepts of the knowledge management framework can be translated into a functioning, user-friendly system. Particularly relevant is their experience with seamless connectivity to trading platforms such as MetaTrader 4, which enables integration into existing trading environments.

The project's financial independence underscores the team's commitment to long-term development. Over a period of 15 years, approximately €12 million was invested in building the necessary IT infrastructure, with a focus on purchasing high-performance hardware for the data centers. This long-term investment demonstrates the team's commitment to developing a robust, scalable system that is independent of short-term market conditions.

Particularly relevant is that the team has consciously decided against external investors, ensuring the independence of the development. Funding is provided exclusively through private funds, primarily from personal income generated through the use of the AISHE system. This approach allows the team to focus on the continuous improvement of the system, free from pressure from external stakeholders.

The team's technical expertise is evident not only in the development of the core system, but also in the clear separation between continuous learning and structured updates. While AISHE continuously learns locally on the user's hardware through reinforcement learning, the structured system updates are the result of diligent work to ensure the core architecture always remains up-to-date. The current version, Build 5.871, of the system contains numerous improvements over previous versions, the result of extensive daily testing and continuous adaptation to new data and changing requirements.

In summary, the team behind AISHE combines a unique blend of scientific expertise, practical implementation experience, and long-term commitment to the project. This combination of theory and practice, coupled with financial independence and a long-term perspective, has made it possible to develop a system that is not only technically robust but also compliant with the European understanding of responsible AI use.

Question 14: Expected return: What is the projected return on investment for the AI project and over what period?

AISHE revolutionizes the understanding of return expectations in the field of autonomous trading systems through a fundamental paradigm shift: The expected return not only concerns the individual user and the development team, but extends to a broader societal level with the potential to transform global labor markets and usher in a new era of economic participation.

Return expectations for the user

For the individual AISHE user, the return expectation depends heavily on several critical factors, all of which are interconnected:

Hardware quality is crucial for the accuracy of the system and thus for the achievable return. On powerful hardware, more complex neural states can be processed faster, which can lead to more precise decisions and higher returns. As documented in a real user's trading report, under optimal conditions (powerful hardware, well-configured risk parameters, sufficient training time), daily returns of 32.8% with a moderate drawdown of 3.5% can be achieved—results that were only achieved through intensive training and careful configuration of the AISHE system.

The high win factor of 9.75 (gross profit of EUR 860.66 with a gross loss of EUR 88.24) and the 72% profitable trades with a moderate drawdown demonstrate that, after sufficient training, the system is capable of combining effective risk control with high profit potential. These returns are not predicted by hypothetical simulations, but by the system's actual ability to make consistent progress toward the defined goals under the given conditions.

Particularly relevant is that AISHE views hardware dependency not as a limitation, but as an integral part of its return expectations. On less powerful hardware, the system deliberately reduces its complexity and risk tolerance to ensure that decisions can be made within the available computing time—a conscious design decision that ensures autonomy under all conditions, even if this implies more conservative returns.

In the long term, AISHE offers the following perspectives for users:

Continuous improvement through learning and integrating new user experiences leads to AISHE constantly optimizing its performance. As described in the article "Exclusive Insight: How AISHE Transforms AI Trading Autonomy," AISHE uses a dynamic learning framework that integrates reinforcement learning, transfer learning, and federated learning, enabling collective improvement of system performance.
Diversification through the use of AISHE across different asset classes and trading strategies spreads risk and stabilizes returns. As described in the article "The True Nature of the Autonomous AISHE System," a user with multiple computers can run multiple independent AISHE instances simultaneously—each with its own instrument selection, parameter configuration, and trading strategy.
The system's scalability allows it to be used in larger markets and for more complex tasks. The "1 computer = 1 AISHE" principle allows users to gradually expand their autonomous trading systems to increase their return expectations.

Return expectations for the overall system and social impacts

However, AISHE's true transformation lies in its ability to not only generate individual financial returns but also create a new form of economic participation. As described in the article "Intelligent Trading Agents Rewrite the Rules of Active Income," AISHE is revolutionizing the way people interact with financial markets—from passive systems to active agents.

In the context of global unemployment and the impending AI era, AISHE positions itself as a solution that doesn't replace jobs but creates new forms of economic participation. While Sam Altman speaks of a "total disappearance" of certain industries due to AI, AISHE demonstrates that AI doesn't necessarily threaten jobs; rather, poorly planned implementation does.

AISHE demonstrates that autonomous systems can not only eliminate jobs but also create new forms of economic participation. Instead of eliminating the nature of work, it transforms it—from active participation to supervision of autonomous systems. This paradigm shift—from active work to supervision of autonomous systems—addresses growing concerns that AI will eliminate human jobs.

The social return of AISHE is reflected in several critical dimensions:

Democratizing access to financial markets : AISHE runs on a standard Windows PC and requires only moderate computing resources, enabling access for retail investors rather than restricting it to institutional players with extensive infrastructure. As described in the article "How AISHE Brings Microsoft's AI Operating System Vision to Life Today," AISHE transforms market efficiency and reduces information asymmetry between institutional players and retail investors.
Combating unemployment : AISHE can serve as an alternative job for millions of potential users—students, housewives, and young people seeking work-from-home employment. Rather than replacing traditional jobs, it creates new forms of economic participation where humans don't need to be constantly present, but rather monitor autonomous systems and correct them as needed. As emphasized in the article "The Crossroads of AI," an "inclusive transition" for workers is crucial, and AISHE offers precisely this opportunity.
Transforming the world of work : The title "Future of Work" would be ideal for working with AISHE. As described in the article "Europe's Regulatory Path: How Worker Protections Could Define AI's Competitive Frontier," AISHE recognizes that regulatory compliance is not only a duty, but also an opportunity. By adhering to strict European regulations, the system not only builds trust among users but also demonstrates that regulatory requirements don't have to conflict with innovation.
Collective improvement through federated learning : AISHE uses federated learning, where users train on local data and share only model updates—not raw data—with a central aggregator. The aggregated model then improves each individual client. Thus, every user experience contributes to a continuously improving global AI system while maintaining data security.

Within AISHE's AIaaS/SaaS model, the true potential lies in scaling. With millions of users worldwide, the system could not only generate billions monthly, but also create an entirely new class of freelancers earning money with AISHE. These users would not only reduce the burden on social benefits but also pay taxes, thus contributing to economic stability.

The resulting AISHE ecosystem would be grand in its reach and influence. Instead of concentrating wealth in the hands of a few, AISHE would enable a broad distribution of income opportunities. As described in the article "Intelligent Trading Agents Rewrite the Rules of Active Income," AISHE transforms passive systems into active agents: Individuals become collaborators in AI-driven trading, benefiting from—and contributing to—a constantly improving intelligence network.

This model has the potential to revolutionize traditional paradigms of market participation by providing tools that can level the playing field between retail investors and large institutions. Furthermore, by embedding considerations of societal impact and ethical dimensions within the conceptual framework, it sets new standards for how financial automation can be not just a technological advancement but a catalyst for broader economic inclusion and thoughtful regulation.

Risks and realities

Despite these positive prospects, there are also risks that must be considered when forecasting returns:

Model risk : Changes in underlying market conditions can affect model accuracy. AISHE continuously adapts, but sudden structural changes can lead to suboptimal decisions.
Technological development : Rapid advances in AI may create new competitors or better solutions. However, AISHE's continuous learning and clear separation between continuous learning and structured updates help keep pace with these developments.
Regulatory changes : Future regulatory changes could limit AISHE's potential applications. However, as described in the article "AISHE and the EU AI Act: A Deep Dive into Compliance," AISHE explicitly positions itself in compliance with the principles of the EU AI Act, which minimizes regulatory risk.

It's important to note that the return figures provided are estimates and cannot be considered a guarantee. A precise calculation of the return for a specific project requires a detailed analysis of the individual circumstances and a comprehensive profitability calculation.

In summary, AISHE's return expectations are not based on universal benchmarks, but on a customized assessment that takes into account the autonomous nature of the system, the user's specific hardware conditions, and the system's continuous learning capacity. The true value of AISHE lies not in hypothetical return forecasts, but in its ability to create a new form of economic participation—participation that not only supports individual financial goals but also addresses societal challenges like unemployment and redefines the future of work.

As emphasized in the article "Intelligent Trading Agents Rewrite the Rules of Active Income," the transformative shift in artificial intelligence is not just language generation, but autonomous systems capable of shaping financial trajectories with precision and independence. AISHE embodies this transformation and demonstrates that AI is not just a threat to jobs but can be a source of new income opportunities and economic participation on a global scale. In a world where more and more tasks are being taken over by autonomous systems, this capability is becoming increasingly valuable—and that is the true return on investment that AISHE offers.

Question 15: Founding and Headquarters: How was the company founded and where is its headquarters located?

The development of AISHE is the result of a strategic, long-term approach to integrating artificial intelligence into the financial market, spanning more than 16 years. Unlike many startups that react to short-term trends, the project was built from the ground up with a clear vision and long-term perspective.

A future headquarters in Germany is currently in the establishment phase, which will house the project's operational focus. This strategic decision underscores the company's commitment to the European market and its proximity to technical and scientific centers of excellence.

A crucial aspect of the business model is the global distribution network, organized according to a franchise-like principle. Currently, distributions in Singapore, India, Turkey, Switzerland, Spain, Portugal, and France are in the development and registration phase. These local locations not only represent the technical infrastructure for the AISHE systems but also form the foundation for regional sales, training, and support.

The unique feature of this model lies in its local autonomy: Each branch operates under its own responsibility and is subject to its respective national legal system. Local teams have the rights and responsibilities to manage sales and support independently, while remaining integrated into the global AISHE ecosystem. This decentralized model enables an adaptable presence in different markets without compromising the technical coherence of the system.

The project's financial independence is a crucial factor for its long-term stability. The 16-year development period was necessary to bring the AI to its current state. Particularly relevant is that the system's owner has invested tens of millions of euros in development costs over these 15 years. This figure is particularly remarkable because it exclusively covers the ongoing operating costs for the high-performance hardware for the data centers—personnel and other operating costs are not included. This long-term investment demonstrates the team's exceptional commitment to developing a robust, scalable system that is not dependent on short-term market conditions.

Particularly relevant is that the AISHE project was financed entirely through private funds, without the use of external investors or loans. Financing is provided exclusively through private funds, primarily from personal income generated through the use of the AISHE system. This approach allows the team to focus on continuous improvement of the system, without pressure from external stakeholders.

A key aspect of the current development phase is that the original developer and owner of the system is seeking a successor due to age. The founder, who has worked on the development of AISHE for over 16 years, would like to hand over the system to a new entrepreneur who is capable of continuing the vision and fully exploiting the system's growth potential. Interested applicants are expressly welcome and can contact the development team directly.

The current version, Build 5.871, of the system includes numerous improvements over previous versions, which are the result of extensive daily testing and continuous adaptation to new data and changing requirements.

In summary, AISHE's founding and structure are not based on short-term profit expectations, but on long-term sustainability and technical excellence. The global distribution model with local autonomy, coupled with financial independence, has made it possible to develop a system that is not only technically robust but also aligns with the European understanding of responsible AI use. The planned relocation of the headquarters to Germany underscores the company's commitment to a European perspective on AI development that combines both technological innovation and regulatory responsibility. With the search for a new entrepreneur to further develop the system, AISHE marks an important transition point, opening up the potential for further growth and global expansion.

Question 16: Capital and Shareholders: Who are your main shareholders? What is the company's capital structure? How much capital has the company raised so far (please separate investments from loans)?

The AISHE project is characterized by remarkable financial independence and sustainability, spanning more than 16 years of continuous development. Unlike many other AI startups that rely on external investors or venture capital, AISHE was financed entirely from internally generated revenues—an approach that not only ensures strategic independence but also guarantees the project's long-term development prospects.

The project's story begins under a different name: Originally launched as "Highway," the project began its development with a clear focus on creating an autonomous trading system. In the early days, revenue was already generated through subscribers, which was used directly to cover running costs. This self-financing strategy has proven to be a key success factor and has been consistently pursued to this day.

The project’s capital structure is deliberately lean and transparent:

No external investors : AISHE has deliberately refrained from external investors in order to maintain its strategic independence
No loans : The project was fully financed from internally generated revenues, without recourse to external capital
Full ownership : The project is in the sole hands of the founder, who has managed its development for over 16 years
Ongoing cost coverage : Over the last 15 years, the business model has covered running costs in the single-digit million range every year

The financing strategy is based on a closed-loop system, where the system is financed through its own use. Each user undergoes a mandatory 14-day trial period to ensure that all interested parties can thoroughly test the system before making a purchase decision. After this trial period, users pay a monthly fee for system use, generating a continuous cash flow.

These revenues are used directly to cover ongoing costs, enabling the project to finance itself. A unique aspect of this financing structure is its dynamic adaptability: Should revenues temporarily decline, ongoing costs are reduced accordingly to ensure financial stability. This flexible approach has enabled the project to remain stable for over 15 years without relying on external financing sources.

The current financing structure is based on a SaaS/AIaaS (Software as a Service / Artificial Intelligence as a Service) model, where users pay a monthly fee for system use after the 14-day trial period. Additionally, customized package deals, as well as reseller and referral programs, are offered, marketed by local distributors.

Particularly relevant is that the project has deliberately refrained from further capital increases. As described in the "AISHE - Project Questions" document, no further investments are planned, as the system is already capable of financing itself. Future growth will be achieved through partnerships with local distributors, each responsible for their respective markets and assuming local responsibility for support, taxes, training, and sales.

The marketing strategy leverages various channels, including Google Ads, YouTube, and other video marketing platforms, to increase AISHE's global awareness. This strategy is adapted and implemented by local distributors to meet specific market conditions.

This financing strategy has proven particularly robust, as it makes the project independent of fluctuations in the capital markets. While many AI startups struggle to secure further financing rounds in times of economic uncertainty, AISHE can continue its development steadily because it is already able to finance itself.

In summary, AISHE's capital structure is not based on external investments or loans, but rather on a closed-loop system in which the system is financed through its own use. This structure ensures not only the project's financial independence but also its long-term sustainability—a crucial factor for the trustworthiness and stability of an autonomous trading system that makes financial decisions for its users. The fact that the project has been successful for over 16 years without external investors underscores the sustainability and profitability of the business model.

Question 17: Project financing: How will the company obtain financing for growth?

The AISHE project has established itself over 16 years through an exceptionally sustainable business model, which now places it in a unique position for future growth. Unlike many other AI companies that rely on external investment, the project's self-sustaining structure has made it independent of capital markets—a success that now forms the basis for a new phase of growth.

The project is currently undergoing a strategic transition, focusing on establishing a global distributor network. This network will be organized according to a particularly efficient model that benefits both headquarters and local partners:

Distributors are actively sought to distribute the system in their respective countries. Unlike traditional franchise models, these distributors participate directly in the revenue generated by the SaaS/AIaaS model. The particular strength of this approach lies in the fact that the distributors operate entirely independently in their respective countries – this has key advantages:

No costs for the headquarters : Since the distributors finance their activities independently, the company does not incur any additional costs for sales, marketing or support in the respective countries.
Profit Maximization : The revenue generated by the distributor remains as profit for the company, since the infrastructure is already in place and no additional costs are incurred.
Scalability without additional effort : The company can continue to operate as before with minimal effort, while global growth is supported by the distributors.
Decentralized responsibility : The actual sales work, employee support and customer service remain entirely with the distributors, which takes the operational burden off the headquarters.

This model is a natural evolution of the previous business approach, which has proven successful for over 15 years. The fact that the project has so far operated without external investors or loans and has been financed entirely through internally generated revenue underscores the economic viability of the underlying business model.

The current financing structure is based on a SaaS/AIaaS (Software as a Service/Artificial Intelligence as a Service) model, where users pay a monthly fee for system use after a mandatory 14-day trial period. Additionally, customized package deals, as well as reseller and referral programs, are offered, marketed by local distributors.

The strategic advantages of this distributor model are manifold:

Cost efficiency : The company can maximize its operational efficiency because there are no additional costs for expanding into new markets. Current costs continue to be covered by existing revenues, while the new markets generate additional profit.
Market proximity : Local distributors understand the specific requirements of their markets and can adapt their offerings accordingly without the head office having to intervene in local decisions.
Risk minimization : Since the distributors work on their own account, the headquarters bears no financial risk when expanding into new markets.
Flexibility : Should revenues temporarily decline, the head office's running costs can be reduced accordingly, while the distributors can operate independently.
Scalability : The model allows for almost unlimited scaling, as new markets can be added without the headquarters having to expand its infrastructure.

In summary, AISHE's project financing is not based on external capital sources, but rather on a closed-loop system in which the system is financed through its own use. This approach ensures not only financial independence but also the long-term sustainability of the project. The establishment of the distributor network marks a crucial step in the project's development, enabling exponential growth without jeopardizing the proven, cost-efficient structure of the headquarters.

By finding a new entrepreneur to further develop the system and establishing this efficient distributor model, AISHE marks an important transition point that opens up the potential for further growth and global expansion – fully financed by its own business model, without relying on external capital sources.

Question 18: Vision and Strategy: What is the company’s growth strategy?

AISHE pursues a clear and visionary growth strategy spanning 16 years of continuous development and adaptation. Unlike many startups that react to short-term trends, AISHE's strategy is rooted in a profound vision that has guided the company since its inception: the creation of an autonomous trading system that not only revolutionizes the way we interact with financial markets but also ushers in a new era of economic participation.

The core vision: From idea to transformation

For 16 years, the development team has been working with tireless passion and dedication to realize an idea that will forever change not only the founder's life, but also the world of finance. AISHE is not just a project; it is the embodiment of a vision that has accompanied the founder for many years. The goal was clear from the start: to develop a system that will revolutionize the way we deal with finance.

Every step along this path, every challenge overcome, and every success achieved has strengthened the conviction that AISHE has the potential to fundamentally transform the world of finance. This journey has been not only a professional challenge, but also a profound personal experience, shaped by curiosity, a spirit of innovation, and a strong desire to create something of lasting significance.

Strategic pillars of the growth strategy

AISHE’s growth strategy is based on several strategic pillars that complement and reinforce each other:

1. Global expansion through decentralized distribution model

AISHE relies on a decentralized distribution model that is already in the development and registration phase in several countries, including Singapore, India, Turkey, Switzerland, Spain, Portugal, and France. This model operates according to a franchise-like principle, with local distributors operating independently in their respective markets. The unique feature of this approach is that the distributors operate entirely on their own account – this offers key advantages:

No costs for the headquarters : Since the distributors finance their activities independently, the company does not incur any additional costs for sales, marketing or support in the respective countries.
Profit Maximization : The revenue generated by the distributor remains as profit for the company, since the infrastructure is already in place and no additional costs are incurred.
Scalability without additional effort : The company can continue to operate as before with minimal effort, while global growth is supported by the distributors.
Decentralized responsibility : The actual sales work, employee support and customer service remain entirely with the distributors, which takes the operational burden off the headquarters.

2. Technological development and innovation

AISHE aims to become the leading platform for AI-powered trading. In the future, the system will be further developed to offer new features and open up new markets. A particular focus is on improving the system's transparency, security, and regulation.

Continuous technological development is ensured by several mechanisms:

Federated Learning : AISHE uses federated learning, where users train on local data and share only model updates—not raw data—with a central aggregator. The aggregated model then improves each individual client. Thus, every user experience contributes to a continuously improving global AI system while maintaining data security.
Dynamic Learning Frameworks : As described in the article "Exclusive Insight: How AISHE Transforms AI Trading Autonomy," AISHE uses a dynamic learning framework that integrates reinforcement learning, transfer learning, and federated learning. This multifaceted approach enables the system to continuously adapt to new market situations, absorb diverse data streams, and refine its decision-making processes without constant human intervention.
Tripartite decision architecture : The integration of the three factors (human factor, structural factor and relationship factor) into a coherent decision architecture enables a comprehensive market analysis that not only processes quantitative data but also takes into account qualitative aspects such as market psychology and structural market conditions.

3. Democratization of access to financial markets

A central component of the growth strategy is democratizing access to financial markets. AISHE runs on a standard Windows PC and requires only moderate computing resources, enabling access for retail investors rather than restricting it to institutional players with extensive infrastructure. As described in the article "How AISHE Brings Microsoft's AI Operating System Vision to Life Today," AISHE transforms market efficiency and reduces information asymmetry between institutional players and retail investors.

This democratization is supported by several strategies:

No special technical knowledge required : As described in the "AISHE - Project Questions" document, no special technical expertise is required to effectively set up and use AISHE. If users encounter any issues, they can contact the AISHE support team.
Mandatory trial period : Every user undergoes a mandatory 14-day trial period to ensure that all interested parties can test the system thoroughly before making a purchase decision.
Customization options : AISHE offers a wide range of settings that can be customized to individual risk tolerance and investment goals. These include trading hours, risk management, and other parameters.

4. Diversification of income sources

AISHE’s growth strategy includes a clear diversification of revenue sources to ensure financial stability and independence:

Subscription model : AISHE is offered on a subscription basis, where users pay a monthly fee for using the system after a 14-day trial period.
Licensing : AISHE's technology could be licensed to other companies that wish to integrate it into their own products.
Data sales : Anonymized and aggregated data could be sold to financial institutions or research institutes.
Consulting : The founder could continue to act as an advisor to the new team and contribute his deep knowledge of AISHE.
Partnerships : Cooperation with other companies could open up new business areas, for example in the area of wealth management or the development of new financial products.
Training : Seminars and training courses could serve as an additional source of income.

Future growth scenarios

AISHE’s growth strategy includes several possible scenarios for the future:

1. Further development by the existing team

Scaling : The existing team will be expanded and additional resources will be provided to scale AISHE faster and acquire new customers.
Diversification : New products and services based on AISHE are being developed to generate additional revenue streams.
International Expansion : AISHE is expanding into new markets to increase its global reach.
Revenue sources : subscription model, licensing, data sales, consulting.

2. Transfer to a new company

Acquisition : An established company in the financial sector acquires AISHE and integrates it into its existing product portfolio.
Joint Venture : Establishment of a joint venture with a strategic partner.
Revenue streams : Depending on the agreement with the new company, different models such as royalties, investments or milestones can be agreed.

3. Development through a new team within the company

Spin-off : Creation of a new, independent company focused exclusively on AISHE.
Revenue sources : subscription model, licensing, data sales, venture capital.

Social and societal impacts

A crucial aspect of AISHE's growth strategy is the integration of social and societal considerations into technological development. As emphasized in the article "The Crossroads of AI: Progress, Peril, and the Power to Redefine Humanity," AI is not a monolith, but a mirror of our ambitions and mistakes.

The social return of AISHE is reflected in several critical dimensions:

Combating unemployment : AISHE can serve as an alternative job for millions of potential users—students, housewives, and young people seeking work-from-home employment. Rather than replacing traditional jobs, it creates new forms of economic participation where people don't need to be constantly present, but rather autonomous systems monitor and correct themselves as needed.
Transforming the world of work : The title "Future of Work" would be ideal for working with AISHE. As described in the article "Europe's Regulatory Path: How Worker Protections Could Define AI's Competitive Frontier," AISHE recognizes that regulatory compliance is not only a duty, but also an opportunity. By adhering to strict European regulations, the system not only builds trust among users but also demonstrates that regulatory requirements don't have to conflict with innovation.
Collective improvement through federated learning : AISHE uses federated learning, where users train on local data and share only model updates—not raw data—with a central aggregator. The aggregated model then improves each individual client. Thus, every user experience contributes to a continuously improving global AI system while maintaining data security.

Conclusion

AISHE's growth strategy is not based on short-term profit expectations, but on long-term sustainability and technical excellence. The global distribution model with local autonomy, coupled with financial independence, has enabled the development of a system that is not only technically robust but also compliant with the European understanding of responsible AI use.

The planned relocation of the headquarters to Germany underscores the company's commitment to a European perspective on AI development, combining both technological innovation and regulatory responsibility. With the search for a new entrepreneur to further develop the system, AISHE marks an important transition point, opening up the potential for further growth and global expansion – fully financed by its own business model, without reliance on external capital sources.

In summary, AISHE's growth strategy is based not only on technological innovation but also on a clear vision that redefines the future of work and ushers in a new era of economic participation. In a world where more and more tasks are being taken over by autonomous systems, this capability is becoming increasingly valuable—and this is the true vision that drives AISHE.

Question 19: Business model: What is the company’s business model?

AISHE pursues an innovative business model characterized by its sustainability, scalability, and social relevance. Unlike many AI companies that rely on external financing, AISHE has built a closed, self-sustaining ecosystem over 16 years that is both economically and socially sustainable.

Value creation

AISHE creates added value on several levels:

For the individual user : By providing an autonomous trading system that runs on a standard Windows PC and requires only moderate computing resources, AISHE enables retail investors to access institutional-grade trading strategies previously available only to professional market participants with extensive infrastructure. As described in the article "Intelligent Trading Agents Rewrite the Rules of Active Income," AISHE transforms passive systems into active agents: Individuals become collaborators in AI-driven trading, benefiting from—and contributing to—a constantly improving intelligence network.
For society : By democratizing access to financial markets and creating new forms of economic participation. As emphasized in the article "The Crossroads of AI: Progress, Peril, and the Power to Redefine Humanity," AI is not a monolith, but a mirror of our ambitions and failings. AISHE embodies the vision of an AI that acts not as a hammer, destroying the old without understanding the costs, but as a scalpel, eliminating inefficiencies while preserving the irreplaceable value of human connection.
For the global labor market : By creating alternative fields of activity for millions of potential users—students, housewives, and young people seeking work-from-home jobs. Rather than replacing traditional jobs, it creates new forms of economic participation where people don't need to be constantly present, but rather monitor autonomous systems and correct them as needed.

Revenue model

AISHE’s revenue model is based on several mutually reinforcing pillars:

Subscription model (SaaS/AIaaS) : AISHE is offered on a subscription model, where users pay a monthly fee for the system after a mandatory 14-day trial period. This trial period ensures that all prospective customers can thoroughly test the system before making a purchase decision, which builds confidence in the product and minimizes return requests.
Customized packages : In addition to the standard subscription, customized packages are available tailored to the specific needs of different user groups. These packages may include additional features, advanced support options, or specialized trading strategies.
Reseller and Referral Programs : A central component of the growth model are reseller and referral programs marketed by local distributors. These programs enable partners to generate additional revenue streams by referring AISHE users, while exponentially increasing the system's reach.
Distributor network : As already described in the question about project financing, distributors participate directly in the revenue of the SaaS/AIaaS model. Since the distributors operate entirely independently in their respective countries, there are no additional costs for the head office, while the revenue generated by the distributor remains with the company as profit.

Marketing Breakthrough : The marketing strategy leverages multiple channels, including Google Ads, YouTube, and other video marketing platforms, to increase AISHE's global awareness. This strategy will be adapted and implemented by local distributors to meet specific market conditions.

Competitive advantages

AISHE differs from other trading systems through several key competitive advantages:

Tripartite decision architecture : The integration of the three factors (human factor, structural factor and relationship factor) into a coherent decision architecture enables a comprehensive market analysis that not only processes quantitative data but also takes into account qualitative aspects such as market psychology and structural market conditions.
Decentralized architecture : Unlike cloud-based solutions that run on centralized servers, AISHE operates as a fully decentralized, local client that connects directly to the user's existing trading infrastructure. This ensures not only data security but also the speed required for autonomous decision-making.
Federated Learning : AISHE uses federated learning, where users train on local data and share only model updates—not raw data—with a central aggregator. The aggregated model then improves each individual client. Thus, every user experience contributes to a continuously improving global AI system while maintaining data security.

Hardware-dependent adaptation : AISHE adapts its complexity to the available hardware to ensure decisions can be made within the required time. This ensures autonomy under all conditions—from powerful workstations to older personal computers.
EU Compliance : As described in the article "AISHE and the EU AI Act: A Deep Dive into Compliance," AISHE explicitly positions itself in compliance with the principles of the EU AI Act. This gives the system a decisive advantage in the European market, where regulatory compliance is increasingly becoming a competitive factor.

Future prospects

AISHE’s future prospects are diverse and range from technological innovation to social transformation:

Global Expansion : Through the decentralized distribution model, which is already in the development and registration phase in several countries, including Singapore, India, Turkey, Switzerland, Spain, Portugal and France.
Technological advancements : Through continuous learning and integration of new user experiences, AISHE will continually optimize its performance. As described in the article "Exclusive Insight: How AISHE Transforms AI Trading Autonomy," AISHE uses a dynamic learning framework that integrates reinforcement learning, transfer learning, and federated learning.
Democratization of access : By creating a system that can be used on a standard Windows PC and requires only moderate computing resources, access to professional trading strategies is made possible for private investors.
Transforming the world of work : As described in the article "Europe's Regulatory Path: How Worker Protections Could Define AI's Competitive Frontier," AISHE recognizes that regulatory compliance is not only a duty but also an opportunity. By adhering to strict European regulations, the system not only builds trust among users but also demonstrates that regulatory requirements don't have to conflict with innovation.

Potential for a new team or a new CEO

With the search for a successor by the original developer and owner of the system, a crucial transition point opens up, opening up the potential for further growth and global expansion:

Scaling : A new team could help AISHE scale faster and acquire new customers by leveraging existing infrastructure while maximizing operational efficiency.
Diversification : New products and services based on AISHE could be developed to generate additional revenue streams, for example in the area of wealth management or the development of new financial products.
International expansion : AISHE could expand into new markets to increase its global reach while maintaining the local autonomy of its distributions.
Revenue streams : In addition to the subscription model, licensing, data sales, and consulting could serve as additional revenue streams.

Scenarios and possible additional sources of income

AISHE’s business model includes several possible scenarios for the future:

Further development by the existing team :

Scaling: The existing team will be expanded and additional resources will be provided to scale AISHE faster and acquire new customers.
Diversification: New products and services based on AISHE are being developed to generate additional revenue streams.
International Expansion: AISHE is expanding into new markets to increase its global reach.
Revenue sources: subscription model, licensing, data sales, consulting.

Handover to a new company :

Acquisition: An established company in the financial sector acquires AISHE and integrates it into its existing product portfolio.
Joint venture: Establishment of a joint venture with a strategic partner.
Revenue streams: Depending on the agreement with the new company, various models such as royalties, investments or milestones can be agreed upon.

Development by a new team within the company :

Spin-off: Creation of a new, independent company focused exclusively on AISHE.
Revenue sources: subscription model, licensing, data sales, venture capital.

Conclusion

AISHE's business model is not based on short-term profit expectations, but on long-term sustainability and technical excellence. The global distribution model with local autonomy, coupled with financial independence, has made it possible to develop a system that is not only technically robust but also conforms to the European understanding of responsible AI use.

The fact that the project has been successful for 16 years without external investors underscores the sustainability and profitability of the business model. The establishment of the distributor network marks a crucial step in the project's development, enabling exponential growth without jeopardizing the proven, cost-efficient structure of the headquarters.

In summary, AISHE's business model is based not only on technological innovation but also on a clear vision that redefines the future of work and ushers in a new era of economic participation. In a world where more and more tasks are being taken over by autonomous systems, this capability is becoming increasingly valuable—and this is the true vision that drives AISHE.

Company questionnaire: Detailed FAQ

Question 1: Objective and scope: What specific trading problems or opportunities does the AI project target?

Question 2: Data sources: What types of data are used (market data, news, sentiment analysis) and how is data quality ensured?

Question 3: Backtesting and validation: How has the AI been tested in the past and what were the results?

Question 4: Risk management: How does AI manage risks, including market volatility, liquidity, and model degradation over time?

Question 5: Performance Metrics: What metrics are used to evaluate AI trading performance (e.g. ROI, Sharpe Ratio) and what are the current benchmarks?

Question 6: Integration: How is AI integrated into existing trading systems and workflows?

Question 7: Regulatory Compliance: How does the project ensure compliance with financial regulations and data protection laws?

Question 8: Transparency and Explainability: How transparent is the AI decision-making process? Can it be explained to non-technical stakeholders?

Question 9: Scalability: Is the AI solution scalable and can it handle increasing data volumes and trading complexity?

Question 10: Security: What measures are implemented to protect against cybersecurity threats?

Question 11: Maintenance and Updates: How often is the AI model updated or retrained and who is responsible for ongoing maintenance?

Question 12: Ethical considerations: Are there any ethical concerns, such as bias in trading decisions or risks of market manipulation?

Question 13: Team expertise: What experience and background does the team developing the AI have?

Question 14: Expected return: What is the projected return on investment for the AI project and over what period?

Return expectations for the user

Return expectations for the overall system and social impacts

Risks and realities

Question 15: Founding and Headquarters: How was the company founded and where is its headquarters located?

Question 16: Capital and Shareholders: Who are your main shareholders? What is the company's capital structure? How much capital has the company raised so far (please separate investments from loans)?

Question 17: Project financing: How will the company obtain financing for growth?

Question 18: Vision and Strategy: What is the company’s growth strategy?

The core vision: From idea to transformation

Strategic pillars of the growth strategy

1. Global expansion through decentralized distribution model

2. Technological development and innovation

3. Democratization of access to financial markets

4. Diversification of income sources

Future growth scenarios

1. Further development by the existing team

2. Transfer to a new company

3. Development through a new team within the company

Social and societal impacts

Conclusion

Question 19: Business model: What is the company’s business model?

Value creation

Revenue model

Competitive advantages

Future prospects

Potential for a new team or a new CEO

Scenarios and possible additional sources of income

Conclusion

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