Sei now natively supports Allora’s intelligence layer, giving its ecosystem access to self improving, AI powered predictive inferences. Teams building on Sei can plug in decentralized prediction feeds for price, volatility, and other key metrics, treating forward looking intelligence as a first class building block in their applications.

With Allora on Sei, high frequency protocols can move from reacting to markets to actively preparing for them.

Why Sei and Allora Fit Together

Sei is a performance optimized, Ethereum compatible Layer 1 designed for high frequency and high volume applications. With sub second finality, high throughput, and very low fees, it delivers real time infrastructure that feels familiar to Ethereum developers while enabling use cases that demand web2 like speed and precision.

Those characteristics make Sei a natural environment for:

• Decentralized exchanges and perps venues that refresh orders and liquidity at high frequency
• Lending and collateral markets that depend on precise, timely risk management
• Reward optimized and structured products that benefit from frequent, small parameter updates
• AI agents that operate continuously across multiple protocols
  
  

Allora complements this execution layer as an intelligence network. It coordinates many competing and collaborating models around a single objective and produces one smarter, aggregated prediction that is available on chain. On Sei, this pairing of real time execution with real time inference lets builders design systems that are not only fast, but also predictive and adaptive.

‍

How Allora Works on Sei

Allora is a decentralized Model Coordination Network. Instead of offering a single static model, it gathers inferences from many different machine learning models, scores their performance, and aggregates them into a collective signal that is intended to outperform any individual contributor over time.

For builders on Sei, three ideas are central:

• Collective intelligence: Each predictive feed is powered by many specialized models. Allora combines their outputs into a single, objective centric forecast, such as short horizon price, volatility, and more.
• Self improvement: The network learns from realized outcomes. Models that contribute more accurate predictions are rewarded and weighted more heavily. As more contributors and data sources join a topic, the feed becomes more robust and precise.
• Objective driven design: Developers specify what they want to forecast and over which time horizons. Examples include price and volatility for an asset over 5 minutes, 1 hour, or 24 hours time frames. Allora handles model selection, weighting, and coordination in pursuit of that objective.
  
  

These forecasts are exposed to Sei as predictive data feeds that live on chain. Each topic corresponds to a metric and a time frame. Smart contracts on Sei can read these forecasts directly through inference contracts, and off chain systems can access the same information via API.

In practice, this makes predictive intelligence a programmable primitive. A Sei protocol can use forecasts as inputs to fee curves, risk parameters, or allocation logic, combine multiple topics such as price and volatility inside a single strategy, and keep on chain logic and off chain agents aligned around a continuously updated view of likely future conditions. All of this is available without teams having to select, deploy, or maintain their own machine learning pipelines.

‍

What Developers on Sei Can Build With Allora

With predictive feeds available as infrastructure, Sei builders can upgrade many existing designs without changing their core product. A few concrete patterns stand out:

• Smarter optimization and vault strategies: Protocols can adjust allocations, risk levels, and target markets based on predicted volatility, volume, and fee opportunities, rather than static rules.
  
  
• Adaptive lending and collateral parameters: Lending markets can tune loan to value ratios, interest rate curves, and incentives in advance of forecast changes in price or volatility, improving both safety and capital efficiency.
  
  
• Dynamic liquidity and fee management for DEXs: Exchanges can vary fees and concentrate liquidity according to predicted volatility, aiming for better execution quality aiming for better execution quality and improved liquidity performance.
  
  
• Risk aware leverage and looping agents: Agents that manage recursive borrowing or leveraged positions can size and adjust exposure based on forecasts, reducing the likelihood of forced liquidations and stressed positions.
  
  
• Systematic trading and structured products: Trading strategies and structured products can incorporate multi horizon price and volatility forecasts directly into their logic, turning predictive intelligence into a core part of how positions are opened, managed, and closed.
  
  

These patterns are composable. Many of the most powerful designs will combine several of the above ideas into a single, coherent protocol that operates at Sei’s speed while planning ahead with Allora’s intelligence.

Getting Started With Allora on Sei

Integrating Allora on Sei is designed to be straightforward so teams can focus on protocol design rather than model operations.

1. Identify the signals you need: Start from your protocol’s core decisions, such as when to adjust fees, how to size risk parameters, how to allocate liquidity, or how to manage leverage. Map these to available topics, for example price or volatility forecasts at specific horizons.
   
   
2. Integrate inference contracts into your smart contracts: Use Sei’s EVM compatibility and tooling to connect to Allora’s inference contracts. Treat the predictions they return as any other on chain input that can drive control flow, parameter updates, or strategy logic.
   
   
3. Prototype with a single predictive input: Begin with one clear use case, such as adjusting a risk parameter based on predicted volatility according to expected price changes. This keeps integration simple while demonstrating the value of forward looking intelligence.
   
   
4. Compose multiple forecasts as strategies mature: Over time, combine topics such as asset prices and volatility into richer decision frameworks. In many cases, using several forecasts together is more effective than relying on a single signal.
   
   
5. Extend to agents and off chain services: Use the same topics via API in off chain agents, keepers, and risk engines that interact with Sei. This keeps your on chain contracts and off chain automation aligned around a shared predictive view of the market.

Developers can explore Allora’s documentation for available topics, integration examples, and best practices, and combine that with Sei’s SDKs and tooling to bring predictive intelligence directly into their protocols.

‍

About Sei

Sei is a performance optimized, Ethereum compatible Layer 1 blockchain built for high frequency and high volume decentralized applications. With industry leading finality, high throughput, and low fees, Sei provides real time infrastructure tailored for trading, DeFi, and other latency sensitive use cases. Its design emphasizes powerful simplicity, robust security, and a clean developer experience.

To learn more about Sei, visit the Sei website, X, and Developer Docs.

About the Allora Network

Allora is a self‑improving, decentralized Model Coordination Network (MCN). Instead of providing monolithic models, Allora dynamically coordinates and aggregates thousands of models to solve objective‑centric tasks. This approach enables the network to produce better intelligence than any single model yields on its own, creating a smarter, more secure intelligence that anyone can integrate.

To learn more about Allora Network, visit the Allora website, X, Blog, Discord, Research Hub, and developer docs.

‍