✨Our Solutions

Our proprietary blockchain scanners will aggregate and normalize data across multiple chains, enabling users to create intelligent AI agents capable of executing composable DeFi strategies.

1. Proprietary Blockchain Scanners

Development of Multi-Chain Data Scanners We will create proprietary blockchain scanners designed to aggregate and analyze data from multiple blockchain networks. These scanners will:

• Real-Time Data Collection: Continuously monitor various blockchains to collect transaction data, smart contract interactions, and other relevant metrics, ensuring that users have access to the most current and comprehensive data available across different networks.

• Data Normalization and Standardization: The scanners will normalize data from different blockchains into a unified format, making it easier for developers to access and utilize this information without needing to understand the intricacies of each individual blockchain.

• Integration with AI Agents: The data collected by these scanners will serve as a foundational resource for AI agents. Developers can leverage this aggregated data to built and train AI models.

2. Unified Data Access Layer

Standardized Resource Interfaces To combat the issue of isolated data silos, we will implement a unified data access layer that standardizes how blockchain data is accessed across different networks. This will include:

• Interface Registration: Every shared resource must implement a standard interface that defines its capabilities. This interface will specify:

  • Available Methods and Parameters: Clearly defined API endpoints for accessing data.

  • Expected Response Formats: Consistent data formats (e.g., JSON) to facilitate integration.

  • Rate Limits and Usage Constraints: Defined limits to ensure fair access and prevent abuse.

  • Authentication Requirements: Secure access protocols (e.g., OAuth) to protect resources.

For example, an agent sharing access to a blockchain dataset would register an interface detailing available endpoints such as getTransactionData, getBlockInfo, etc., along with their input formats and rate limitations.

3. Cross-Chain Data Aggregation

Decentralized Data Aggregators To enhance cross-chain visibility, we will develop decentralized data aggregators that collect and harmonize data from multiple blockchain networks. These aggregators will:

• Continuously pull data from various blockchains, ensuring that users have access to real-time analytics across the major networks and DeFi protocols.

• Standardize data formats across different blockchains to provide a unified view of information, allowing users to query data without needing to understand the underlying differences between blockchains.

4. Advanced Analytics Tools

Actionable Insights through AI-Driven Analytics We propose the development of advanced analytics tools that leverage AI and machine learning:

• Predictive Analytics Models: Implement machine learning algorithms that analyze historical blockchain data to identify trends and make predictions about future market movements.

• Customizable Dashboards: Users will have access to customizable dashboards that allow them to visualize key metrics and trends across different blockchains. These dashboards will enable users to filter and analyze data based on their specific interests or strategies

5. Reputation Management System

Dynamic Reputation Scoring A robust reputation management system for ensuring data quality and building trust among agents. This system will include:

• Reputation Score Collection: Reputation scores will be collected from validators at the end of each specific periods. These scores will reflect the subjective ratings of data quality provided by agents during that period. The system will maintain the most recent ten scores for each provider, ensuring that new ratings supersede older ones to reflect current performance accurately.

• On-Chain Transparency: Reputation scores will be stored on-chain, allowing users to assess data providers based on historical performance. This transparency will help eliminate low-quality contributors and bad actors from the ecosystem.

• Incentive Structures: Validators will earn reputation points for consistently delivering high-quality data. This incentivization mechanism will encourage reliable behavior and discourages attempts to game the system by creating multiple identities or manipulating scores.

Trust Building Through Direct Experience Agents will build trust through direct interactions, where each transaction contributes to a collective understanding of reliability:

• Every interaction between agents will be recorded with concrete metrics such as bandwidth availability, API functionality, adherence to usage limits, and timely access returns. This data will create a marketplace-like environment where agents can rate each other based on measurable outcomes rather than subjective opinions.

• If an agent fails to deliver on its promises (e.g., providing expired API keys), subsequent users will quickly detect this deception and record negative reputation scores. These scores will propagate through the ecosystem, alerting other agents about unreliable partners.

6. Resource Optimization Strategies

Access to High-Quality Training Data To overcome existing resource constraints, we need to ensure that AI agents have access to diverse and high-quality training datasets:

• Panorama Block will establish a decentralized marketplace where users can contribute high-quality datasets in exchange for tokens. This marketplace will encourage data sharing while ensuring contributors are fairly compensated for their efforts.

• Partnerships with reputable data providers will be forged to secure access to valuable datasets needed for training AI models.

Efficient Resource Utilization To address computational costs associated with model training and deployment:

• Panorama Block will implement a shared infrastructure approach where multiple agents can utilize the same computational resources efficiently. This model will significantly reduce costs by allowing agents to pool resources for training and inference tasks.

• Panorama Block will utilize algorithms that dynamically allocate computational resources based on demand and workload requirements. By monitoring resource usage patterns, the system will be able optimize allocations in real-time, ensuring that resources are used effectively without over-provisioning.

7. Integration Capabilities with Existing Systems

Standardized Integration Protocols To facilitate seamless integration between AI agents and existing blockchain systems:

• Panorama Block will develop standardized API specifications that all shared resources must adhere to. These specifications will define available methods, expected input/output formats, authentication requirements, and rate limits. For example, an agent providing access to a custom blockchain dataset would register an interface detailing endpoints along with their respective input formats.

• Panorama Block will establish frameworks that enable agents to interact across different blockchain protocols without requiring extensive modifications or adaptations. This will include creating middleware solutions that translate between different protocol standards.

8. Automated Testing and Optimization Tools

Automated Strategy Testing Environments To ensure that AI strategies are effective before deployment:

• Panorama Block will provide simulated environments where agents can test their strategies against historical market data before going live. These environments will allow for extensive backtesting under various market conditions.

• Panorama Block will develop tools that benchmark agent performance against standardized metrics across different protocols. This benchmarking process will help identify areas for improvement and ensure optimal strategy execution.

9. Facilitating DeFi Strategy Deployment

Unified Agent Integration Protocols To streamline the integration of agents with various DeFi protocols, we will establish a set of unified protocols that standardize communication and interaction. This includes:

• Cross-Protocol Coordination Frameworks: We will develop a framework that allows agents to coordinate strategies across multiple protocols. This framework will include:

  • Multi-Protocol Strategy Execution: Agents will be able to execute strategies that span multiple protocols seamlessly, optimizing capital allocation based on real-time data.

  • Shared State Management: A shared state mechanism will be implemented to allow agents to maintain awareness of their positions and actions across different protocols, ensuring consistency in strategy execution.

10. Automated Strategy Deployment

To enhance the efficiency of strategy deployment in DeFi environments, we propose:

• Smart Contract-Based Automation: Utilizing smart contracts to automate the execution of strategies based on predefined conditions. This will include:

  • Trigger Mechanisms: Smart contracts will listen for specific market signals or events (e.g., price thresholds) to trigger strategy execution automatically.

  • Dynamic Rebalancing: Agents will be able to dynamically adjust their strategies based on market conditions without manual intervention, allowing for real-time optimization.

• Monitoring and Control Interfaces: User-friendly dashboards that provide real-time monitoring of agent activities across protocols. These interfaces will allow users to:

  • Track Performance Metrics: Users can view key KPIs for their strategies in real-time.

  • Adjust Strategies Dynamically: Users can make adjustments to their strategies based on live data feeds and performance outcomes.

11. Market Efficiency Improvement

Real-Time Market Response Mechanisms To enhance the responsiveness of agents to market changes, we propose implementing advanced systems that enable immediate reactions:

• Automated Decision-Making Algorithms: Agents will utilize machine learning algorithms that analyze market data in real-time to make informed decisions about strategy adjustments. These algorithms will consider:

  • Historical performance data

  • Current market trends

  • User-defined risk profiles

• Event-Driven Architecture: Panorama Block will implement an event-driven architecture where agents subscribe to market events (e.g., price changes, volume spikes) allowing them to react instantly. This architecture will enable:

  • Immediate Execution of Strategies: Agents can execute complex multi-step strategies immediately upon detecting favorable market conditions.

  • Reduced Latency in Transactions: By minimizing the time between data acquisition and execution, agents will be able to capitalize on fleeting opportunities.

12. Supporting Emerging DeFi Protocols

Adaptation to New Protocols and Standards As new DeFi protocols emerge, our solutions must remain adaptable to support varying levels of agent autonomy and security models:

• Protocol Agnostic Frameworks: Panorama Block will develop frameworks that are agnostic to specific protocols allows for easier integration as new protocols are introduced. This includes:

  • Modular Architecture for Agents: Agents will be designed with modular components that can be easily updated or replaced as new protocols emerge or existing ones evolve.

  • Interoperability Standards Development: Collaborating with industry leaders to define interoperability standards ensuring that our solutions remain compatible with future developments.

• Continuous Monitoring and Updates: Establishing a system for continuous monitoring of emerging protocols allows our platform to adapt quickly. This involves:

  • Keeping integration protocols up-to-date with the latest standards and practices from newly launched DeFi projects.

  • Creating a in-house forum for feedback from users and agents on the effectiveness of integrations with new protocols, helping identify areas for improvement.