Technology

Additive AI is built on a hybrid architecture that leverages on-chain monitoring, off-chain AI computation, and client-side deployment for real-time responsiveness and scalability.

Core Components:

• Behavioral Threat Engine: Utilizes transformer-based ML models and graph neural networks (GNNs) to analyze transaction intent, code behavior, and anomaly patterns.

• Neural Signature Network (NSN): A proprietary AI framework that fingerprints attack patterns and evolves with the threat landscape, learning from millions of historical and live Web3 transactions.

• ChainWatch Protocol: A multi-chain scanning protocol ingesting data across EVM, Solana, TON, and others to feed threat detection models in real-time.

• Zero-Knowledge Privacy Layer: Adds user privacy by ensuring no personal wallet data is stored or analyzed beyond the client-side.

• Fail-Safe Simulator: Simulates transactions within a sandboxed virtual EVM or Solana runtime to predict harmful outcomes before signature.

Security and AI Infrastructure:

• Hosted on redundant cloud and decentralized networks.

• Codebases regularly audited and verified.

• AI models trained with reinforcement learning on curated datasets from known scam history.