Proprietary ML Models

Large Language Models (LLMs): Radar AI harnesses the power of GPT-4, Claude, and LLaMA, fine-tuned for crypto-specific queries. Trained on blockchain data, transaction patterns, and sentiment trends, these models deliver precise, contextual insights, redefining crypto intelligence.

Transaction Pattern Recognition Models: Radar AI employs advanced graph neural networks (GNNs) and sequence models like LSTMs and Transformers to analyze blockchain transaction graphs. These models detect patterns such as:

• Whale Movements: Using GNNs to identify high-value transfers and wallet consolidations.

• Insider Wallet Activities: Leveraging sequence models to track developer wallet behaviors, including stealth buys or sells.

• Anomalous Patterns: Applying anomaly detection algorithms like Isolation Forests and Autoencoders to flag suspicious wallet clusters and irregular transaction flows.

Sentiment Analysis Models: Powered by advanced NLP frameworks like BERT and RoBERTa, Radar AI decodes social sentiment from platforms like Telegram and Twitter. It assigns sentiment scores, tracks hype cycles, and identifies influential voices impacting token performance.

Custom AI Pipelines: Radar AI’s proprietary pipelines merge on-chain and off-chain data to create actionable insights. They prioritize metrics like volatility, wallet activity, and token fundamentals, delivering streamlined, decision-ready intelligence that evolves with market dynamics.