The convergence of artificial intelligence and Web3 is creating a new frontier of innovation, but it also presents significant technical challenges. While AI models are becoming increasingly powerful, their integration into decentralized applications is hampered by a fundamental problem: a lack of trustless, verifiable, and economically sustainable infrastructure. How can a dApp securely call an AI model? How can the cost of that computation be accurately metered and paid for on-chain?

Enter Rayls (RLS), a Web3 protocol designed to be the foundational infrastructure for this emerging AI economy. Rayls is building the critical middleware that allows AI models to be treated like smart contracts: composable, verifiable, and permissioned. By creating a standardized layer for AI model invocation, access control, and payment, Rayls aims to unlock the full potential of AI within the decentralized world.

This deep dive will explore the core technology that powers Rayls, its carefully designed tokenomics, its potential applications across various sectors, and the growth strategies that are positioning it as a key player in the AI x Web3 narrative.

What is Rayls, and Why Does it Matter for Web3?

Rayls (RLS) is a Web3 protocol focused on AI model invocation, inference calls, and on-chain access control. It addresses two core problems that currently limit the integration of AI into the decentralized ecosystem:

1. How can AI models be securely and verifiably called by on-chain or off-chain applications?
2. How can AI services be metered, billed, and paid for in a trustless manner?

The vision of Rayls is to make AI capabilities as seamless and reliable as interacting with a smart contract. It aims to create an environment where AI is Composable (different models can be chained together), Verifiable (outputs can be proven correct), and Permissioned (access can be tightly controlled).

The project is gaining significant attention for several key reasons:

• AI x Web3 is a Dominant Trend: The fusion of AI’s intelligence with Web3’s decentralization is one of the most powerful narratives in the current market cycle.
• The Need for a Trustless Billing System: As AI model usage grows, the demand for a decentralized system to meter and charge for these services becomes critical. Rayls provides this payment rail.
• Infrastructure-Level Potential: Rayls is not just another “AI project” building a single application. It is creating the foundational protocol layer upon which thousands of future AI dApps could be built.

Core Technology: Building a Decentralized AI Network

The Rayls protocol is a sophisticated system designed to orchestrate the complex interactions between AI models, developers, and computational resources. Its architecture is composed of several key components that work in concert.

1. Model Call Layer

This is the unified gateway for developers. Rayls provides a standardized API that allows developers to call various AI models as if they were interacting with a single, cohesive service.

• Unified Interface: Simplifies the process of integrating different types of models, including Large Language Models (LLMs), image generation models, and data embedding models.
• Verifiable Outputs: The protocol includes mechanisms to ensure that the output received from a model is authentic and has not been tampered with.
• Cost Tracking: Every call made through the API is automatically logged and metered, providing transparent cost accounting for developers and users.

2. Access Control System

This powerful feature gives model developers granular control over who can use their creations and under what conditions.

• Usage Permissions: Developers can set rules based on wallet addresses, token holdings, or NFT ownership.
• Rate Limiting: Access can be limited to a certain number of calls per day or per user to prevent abuse.
• Tiered Access: Developers can create different pricing tiers (e.g., a free basic tier and a premium paid tier) for their models, managed on-chain.

3. Operators Network

This is the decentralized backbone of the Rayls ecosystem. A network of independent operators provides the computational power needed to run the AI models.

• Inference Execution: Operators are responsible for running the AI model inference tasks requested by users.
• Cost Metering: They track the computational resources consumed during each task to ensure accurate billing.
• Proof of Computation: Operators generate proofs that the computation was executed correctly, providing a layer of trust and verifiability.
• Staking and Rewards: To join the network and earn fees, operators must stake RLS tokens, which aligns their incentives with the health and security of the protocol.

4. Payment Metering System

This is the economic engine of the protocol. It allows for flexible and automated billing for all AI model calls.

• Pay-per-Call: Users can be charged a flat fee for each API call.
• Pay-per-Token: For LLMs, billing can be based on the number of input or output tokens processed.
• Subscription Models: Developers can set up time-based subscription fees for continuous access.

This system effectively creates a fully-fledged “on-chain AI economy” where computational resources can be bought and sold in a trustless market.

Tokenomics: How the RLS Token Captures Value

The value of the RLS token is directly tied to the utilization of the Rayls network. As more AI calls are processed and more operators join the ecosystem, the demand for RLS is designed to grow organically.

RLS Token Utilities

Use Case Category	Function
Model Call Fees	Users and dApps pay for AI model usage in RLS tokens.
Node Staking	Operators must stake a significant amount of RLS to participate in the network and earn rewards.
Governance (DAO)	RLS holders will vote on key protocol decisions, such as adjusting the fee model or the node reward structure.
Incentive Distribution	A portion of the protocol’s revenue is used to reward model providers, developers, and active community members.
Permissioned Access	Access to premium, high-performance AI models can be gated by holding a certain amount of RLS tokens.

Value Capture Logic

The tokenomics create a powerful flywheel effect that drives value to the RLS token:

• Increased Model Calls: As more developers build on Rayls, the volume of AI calls increases, leading to higher fee generation and greater demand for RLS as a payment currency.
• Network Expansion: To support a higher volume of calls, more operators are needed. This increases the demand for RLS for staking, locking up a larger portion of the circulating supply.
• Ecosystem Richness: A wider variety of available models makes the platform more attractive, creating a virtuous cycle that draws in more users and developers.

Ecosystem Applications: Where Rayls Can Be Deployed

As a foundational infrastructure layer, Rayls has broad applicability across numerous high-growth sectors within the Web3 ecosystem.

AI dApps

Rayls can serve as the backbone for a new generation of decentralized AI applications, such as a “decentralized ChatGPT,” on-chain AI assistants, or AI-powered trading bots that execute strategies based on real-time data.

DeFi and AI-Powered Risk Management

DeFi protocols can leverage Rayls to call sophisticated AI models for critical tasks like:

• Liquidation Prediction: Analyzing wallet behavior to predict liquidation risks.
• Dynamic Risk Modeling: Automatically adjusting protocol parameters based on market conditions.
• Algorithmic Trading: Calculating and executing complex trading strategies on decentralized exchanges.

The Creator Economy

Artists, writers, and musicians can use Rayls to access a wide array of generative AI models to create content. The metered payment system ensures that model providers are fairly compensated for their contributions to the creative process.

Enterprise API Services

Companies can use Rayls’ permissioning system to build their own private AI layers. They can host proprietary models on the network and grant access only to authorized employees or clients, all while leveraging Rayls’ decentralized billing and access control.

AI Agents

Rayls is perfectly positioned to become the infrastructure for autonomous AI agents. Each agent can be assigned an identity, a computational budget, and a set of billing rules, allowing them to operate independently on the blockchain while their resource consumption is tracked and paid for in RLS.

Social Trends and Growth Flywheel

Rayls’ community growth is driven by a strategy that focuses on developer engagement and transparent progress.

1. Showcasing Integrations: The official Rayls social media channels frequently post demonstrations of new model integrations, comparisons of different AI capabilities, and walkthroughs of the billing system. This technical content is highly effective at attracting the attention of developers, who are the primary users of the protocol.
2. Community-Driven Diffusion: As developers begin to build on Rayls, they are naturally inclined to share their creations. Posts showcasing small AI tools they’ve built, screenshots of their API call costs, or code snippets for integrating Rayls can spread organically through developer communities.
3. Exchange Listings and Market Visibility: Each new exchange listing significantly boosts the project’s visibility, increasing search volume and drawing a wider audience into the community discussions.

Tracking and Trading RLS

For those looking to track Rayls’ price trends, analyze trading depth, or utilize automated trading tools, platforms like XT.COM provide a comprehensive suite of options.

You can view the real-time RLS price directly. Also for those interested in the RLS, a reliable trading venue is a good starting point. XT Exchange, for example, offers a straightforward way to interact with the asset. Users can find the RLS/USDT spot market for buying and selling. For traders looking to implement more systematic approaches, the platform also provides automated tools. The RLS/USDT spot grid trading bot can help capitalize on market volatility by automating trades within a set range. Furthermore, traders can explore RLS/USDT automated strategies to fit their personal style. Access to these tools on a secure platform helps both new and experienced participants engage with the RLS project more effectively.

Risks and Considerations

While the vision for Rayls is compelling, it is important to be aware of the associated risks.

• Early-Stage Sector: The AI x Web3 infrastructure space is still in its infancy and highly competitive.
• Token Volatility: As an early-stage project, the RLS token is likely to experience high price volatility.
• Node Ecosystem Growth: The success of the protocol depends on its ability to attract a sufficient number of reliable operators to build a robust and decentralized network.
• Adoption Dependency: The ultimate value of the RLS token is contingent on the volume of AI model usage on the platform. If adoption by developers is slow, value accrual may be impacted.

Frequently Asked Questions (FAQs)

1. Is Rayls an AI model itself? No, Rayls is not an AI model. It is the Web3 protocol layer that facilitates the calling, billing, and access control for AI models.
2. What is the primary use of the RLS token? The main uses are paying for AI model calls, staking by network operators, governance, and incentivizing ecosystem participants.
3. Does Rayls support multiple AI models? Yes, the protocol is designed to be model-agnostic, with plans to integrate a wide range of popular LLM and computer vision models.
4. Who is the target user for Rayls? The primary users are AI application developers, institutions, builders of AI agents, and data analysts who need verifiable and metered access to AI computation.

Conclusion: The Backbone of the AI Economy

Rayls (RLS) is tackling one of the most fundamental challenges at the intersection of AI and Web3: creating a trustless economic layer for AI services. By making AI models verifiable, billable, and permissioned, the protocol provides the critical infrastructure needed for a new wave of decentralized innovation.

Its key strengths lie in its comprehensive model-calling system, robust access control mechanisms, decentralized operator network, and a clear value capture model for its native token. With its focus on high-demand areas like AI Agents, DeFi, and enterprise services, Rayls is well-positioned to become an essential component of the Web3 AI economy’s foundation.

About XT.COM

Founded in 2018, XT.COM is a leading global digital asset trading platform, now serving over 12 million registered users across more than 200 countries and regions, with an ecosystem traffic exceeding 40 million. XT.COM crypto exchange supports 1,300+ high-quality tokens and 1,300+ trading pairs, offering a wide range of trading options including spot trading, margin trading, and futures trading , along with a secure and reliable RWA (Real World Assets) marketplace. Guided by the vision “Xplore Crypto, Trade with Trust,” our platform strives to provide a secure, trusted, and intuitive trading experience.