# AI Engine **Model Type:** * SynaptAI uses a hybrid AI model combining: * **Natural Language Processing (NLP)** for conversational interactions * **Time-Series Forecasting Models** (ARIMA, Prophet, and LSTM variants) for price predictions * **Sentiment Analysis Models** powered by transformer-based architectures (e.g., BERT, RoBERTa) * **Reinforcement Learning** for model fine-tuning based on feedback loops **Data Sources:** * Live price data (via Chainlink, Binance APIs) * On-chain analytics (wallet movements, token distribution) * Social sentiment (Twitter, Reddit, Telegram, news feeds) * Historical chart patterns * Technical indicators (RSI, MACD, Volume, MA, etc.) The models are continuously retrained using updated market data to improve forecast accuracy and responsiveness. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://synaptai.gitbook.io/synaptai-docs/technology-stack/ai-engine.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.