AI Optimization Engines

Route Optimization

Our hybrid approach uses supervised pre-training on historical swap data combined with continuous reinforcement learning.

Pre-training provides initial estimates for price impact and slippage across different DEX combinations.

Live feedback from executed trades updates the model, ensuring adaptive performance as liquidity conditions change.

This removes the burden of manual path comparison and guarantees near-optimal swap execution.

Gas Forecasting and Scheduling

Gas prices fluctuate based on network congestion and transaction volume.

We employ a time-series model—such as LSTM—to forecast short-term gas trends using on-chain metrics like pending transaction count and block times.

When submitting transactions, the system dynamically sets gas prices or batches multiple swaps to minimize overall cost.

An optional “Accelerate Mode” prioritizes faster confirmations by slightly overbidding predicted prices.