2. Technical Architecture Overview

2.1 Core AI Modules

Our platform utilizes neural-network-based models designed to operate on high-throughput blockchain data. Key features include:

• Multilayer Perceptrons (MLP) for wallet clustering and label propagation.

• Graph Neural Networks (GNN) for transaction graph analysis, identifying suspicious transaction flows.

• Auto-Encoders for anomaly detection, used to flag irregular contract behaviors and potential hacks.

2.2 Layered Infrastructure on Solana

Solana’s high-performance protocol provides a robust base layer with its Proof of History (PoH) consensus mechanism. We supplement it by building:

• Decentralized data oracles that feed real-time data into our AI modules.

• Intermediate L2 protocols that store ephemeral analytics results for quick retrieval, enhancing system scalability.

2.3 Data Pipelines & Analytics

Real-time data from the Solana blockchain is streamed into our analytics engine using advanced event listeners. We then process the data with:

• Batch ingestion: Large data sets are continuously aggregated and indexed for historical pattern analysis.

• In-memory computing: High-priority operations (e.g., contract scanning) are performed in memory for lightning-fast detection.