Trace the money. Map the network. Build the case.
Sherlock is OCINT's AI-assisted cross-chain investigation engine. It combines machine-learning models, on-chain heuristics, and investigator-defined rules to follow funds across supported chains, bridges, swaps, and service endpoints — while preserving the sources, confidence, and analyst decisions behind every conclusion.
What Sherlock does
Automates supported transaction paths across chains and bridges, and flags ambiguous transitions for investigator review.
Combines machine-learning-assisted clustering, on-chain heuristics, and metadata to surface likely wallet relationships, with supporting sources attached and investigator review required before attribution.
Flags peel chains, mixers, chain-hopping, and consolidation as funds try to disappear.
Identifies deposit addresses likely tied to exchanges, gambling sites, and darknet markets — with source and confidence recorded.
Produces a replayable graph and timeline, every claim tied to its source and date.
Feeds verified findings into SwiftReport for evidence schedules, referrals, and preservation or freeze-request drafts.
Cross-chain and attribution coverage depends on the asset, chain, bridge, and data source involved. We walk through current coverage, scope, and limitations in every demo.