Applied machine learning for on-chain risk — anomaly detection and intelligence built as systems you can trust, not notebooks.
DeFi generates a firehose of on-chain data and an unforgiving environment to act in: exploits unfold in seconds, treasury exposure shifts block by block, and a model that flags risk a minute late is useless. The hard part is not a clever notebook — it is the engineering around the model: low-latency inference over streaming chain data, anomaly detection tuned to avoid alert fatigue, and the observability and cost control that make it dependable enough for a treasury or compliance team to rely on.
Anomalous on-chain activity is surfaced fast enough for treasury intervention
Risk models run with evals and observability, so behavior is measured not hoped for
Alerts are tuned against labeled incidents to cut noise and avoid fatigue
Legal, finance, and engineering reason from the same risk picture
Turning models into reliable production systems — streaming inference over chain data, tuned anomaly detection, evals, observability, and cost control — not a one-off script that breaks under real load.
We tune classifiers against labeled incidents and measure precision with evals, so the system surfaces high-confidence signals worth acting on rather than overwhelming the team. Ledger-Grid used this to cut compliance review cycles by 67%.
Yes. We surface on-chain risk through one intelligence layer so legal, finance, and engineering reason from the same source of truth instead of scattered dashboards and wallets.
