Wallet clustering
Heuristic + ML
Groups wallets that share operational patterns or ownership.
Map wallet relationships, clustering, and transaction flows to uncover counterparties and hidden networks. Focus on smart contract security, counterparty risk, and operational safeguards for treasuries and protocols. Detect anomalies, suspicious approvals, and governance changes before funds are at risk. Teams operating in 24/7 markets with globally distributed contributors.
Wallet clustering
Heuristic + ML
Groups wallets that share operational patterns or ownership.
Case exports
GraphML, JSON, PDF
Share investigation results with stakeholders or regulators.
Ethereum Mainnet profile
Deepest liquidity and broadest institutional adoption.
Base fees typically 15–40 gwei outside of NFT drops; priority fees ~1–2 gwei.
Graph visualizations connecting wallets via token transfers, LP shares, and governance activity. Entity tagging with metadata (team, treasury, exchange, MEV, mixers). Flow timelines to trace funds across chains and protocols.
Proactively identifying malicious approvals and upgrade patterns is tedious. Fewer false positives in alerting systems.
Uniswap, Lido, EigenLayer, and Aave drive high-value flow and require precise gas timing. Live monitoring of contract upgrades, proxy changes, and admin key usage.
Live monitoring of contract upgrades, proxy changes, and admin key usage. Requires tooling that supports asynchronous collaboration, multi-timezone monitoring, and localized communications.
Token scanner sweeps of newly listed assets interacting with your treasury. Requires tooling that supports asynchronous collaboration, multi-timezone monitoring, and localized communications.
Mapping transacting wallets to known entities for counterparty risk scoring. Requires tooling that supports asynchronous collaboration, multi-timezone monitoring, and localized communications.
Yes. Upload CSV or JSON cluster definitions to align with internal intelligence.
We overlay public attribution datasets and allow custom enrichment via API.
Graph rendering dynamically loads segments to keep performance stable even with thousands of nodes.