# Technology Overview > **DeepSnitch AI will be powered by a modular, decentralized surveillance stack that fuses advanced machine learning, graph analytics, and real-time data ingestion across multiple blockchains and social platforms. Each agent will be built on a robust AI tech stack and custom infrastructure, ensuring actionable intelligence that is fast, reliable, and exclusive.** *** ## 📊 Custom Data Pipeline DeepSnitch will operate a **proprietary data pipeline** that connects directly to blockchain nodes via custom RPC endpoints. This enables high-frequency ingestion of raw transaction data, mempool activity, and smart contract events across supported chains. *** ## 🤖 AI/ML Engine The core analytics layer leverages a mix of **supervised and unsupervised machine learning models**, including clustering, anomaly detection, graph neural networks (GCN, GAT), and natural language processing (NLP) frameworks. Models will be trained and updated continuously to adapt to evolving market behaviors and attack vectors. *** ## 🔗 Integration Layer The platform **will aggregates on-chain data with off-chain intelligence** from social media (Twitter, Telegram), using APIs and custom crawlers to enrich signals and correlate behavioral anomalies with narrative shifts. *** ## 🔐 Security & Privacy All user data shall be **encrypted (AES-256)**, and the system employs decentralized identity and zero-knowledge proofs for privacy-preserving analytics. *** --- # 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://deepsnitch-ai.gitbook.io/deepsnitch/whitepaper/technology-overview.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.