Normalizes non-standard feeds and on/off-ramp data into one real-time schema, while crypto-native models cut through volatility to flag wash trades, spoofing, and insider flow, even amid extreme price swings or cross-venue price gaps.

Monitors manipulation across both on- and offchain, from insider trading on DEXs, cross-venue schemes spanning spot/derivatives or CeFi/DeFi to native onchain threats throughout the asset life cycle.

Real-time alerting surfaces risk instantly, enabling timely intervention in a global, 24/7 “always-on” market and instant settlement that legacy batch processing can’t offer

Unifies trades, transactions, KYC, and behavioral signals in one view—uncovering risks siloed systems miss like account takeovers, new-account scams, and transactions that appear legitimate in isolation but raise suspicion when analyzed against broader trading behavior.

Machine-learning sentiment analysis distills signals from messy, unstructured data across Reddit, X, Telegram, and news sources – flagging symbol-level sentiment shifts in real time.

Venue-agnostic approach to market data delivers high speed and scalability across all digital and traditional asset classes, with fallback mechanisms for uninterrupted surveillance even without full order-book depth.