AI Models and Algorithms

C-3PO Robot’s AI stack powers its core functionalities, including market prediction, whitepaper analysis, user interaction, and emotional support, through a combination of machine learning, natural language processing, and sentiment analysis.

• Machine Learning for Market Prediction: The system uses trend-following models to identify short-term price trends by analyzing indicators like moving averages and relative strength. For example, if a token shows a consistent upward trend without being overbought, the model suggests a buying opportunity with a confidence score. Mean-reversion models detect price deviations, flagging oversold tokens with potential rebound likelihood. To enhance accuracy, these models are combined using a weighted approach, trained on five years of BSC token data covering price, volume, and liquidity metrics for thousands of tokens. Risk prediction models analyze on-chain data to identify scam patterns, such as developer sell-offs or liquidity withdrawals, providing a risk score based on historical scam incidents.

• Natural Language Processing for Whitepaper Analysis and Interaction: For whitepaper decoding, a fine-tuned language model extracts key information like token supply and team allocation, achieving high accuracy through pre-training on thousands of crypto whitepapers. It also identifies risks, such as missing audit details, by comparing extracted data against a risk rulebase. For user interaction, a conversational model generates empathetic, context-aware responses, fine-tuned on crypto-related dialogues from community platforms. This ensures users receive clear, supportive answers, such as a cautious warning about a token’s potential risks based on whitepaper analysis.

• Sentiment Analysis for Emotional Support: Sentiment analysis detects user emotions to tailor responses, classifying emotions like anxiety or optimism with high accuracy. Trained on millions of social media posts, the model identifies distress in user inputs and adjusts its tone, offering soothing advice during market downturns. For example, an anxious user receives a calming response with historical data to support a rational decision. The system continuously improves by incorporating new user interactions for retraining.

• Performance Metrics: Prediction models achieve strong accuracy for short-term price movements, whitepaper extraction performs reliably for key entities, and sentiment classification ensures precise emotion detection. Inference is optimized for speed, delivering responses in milliseconds using high-performance hardware.