Open an address. Read the exposure.
Address analysis, transaction analysis, wallet relationships and fund flow across supported networks — with the reasoning behind every figure visible rather than asserted.
What is blockchain intelligence?
Blockchain intelligence turns raw ledger data into analysable structure: which addresses cluster together, which entities they connect to, how value flowed between them, and how confident each of those conclusions is. The reasoning behind every figure should be inspectable rather than asserted as a bare number.
What an address profile shows.
Three synthetic addresses, three different risk pictures. Switch between them to see how the score decomposes.
Eight readings on one address.
Address analysis
Balance history, first and last activity, transaction cadence and dormancy — the shape of how an address behaves.
Transaction analysis
Inputs, outputs, internal transfers, token movements and contract calls decoded into something a person can read.
Wallet relationships
Addresses grouped where chain-specific heuristics support it, with the heuristic recorded per cluster.
Exposure analysis
Direct and indirect exposure to categories of counterparty, measured by hop distance and value share.
Risk signals
The individual signals that move a score, each with its own weight and evidence.
Entity attribution
Links to known services where a source and evidence trail exist, and an explicit 'unattributed' where they do not.
Graph visualisation
Fund flow rendered as a directed graph you can expand, filter and follow to intervention points.
Multi-chain view
Cross-chain transfers followed through bridges where the corresponding transaction can be identified.
The fields, and what each one means.
| Field | Type | How to read it |
|---|---|---|
| Risk score | 0–100 analytical indicator | A weighted sum of observable signals. Not a finding of unlawful conduct. |
| Risk level | Low / Elevated / High | A banding of the score, provided so teams can write policy against it. |
| Transaction count | Integer | Transactions involving this address on the selected network. |
| Total volume | Fiat estimate | Valued at the time of each transfer where pricing is available; an estimate, not an accounting figure. |
| Counterparties | Integer | Distinct addresses that have transacted with this address. |
| Risk exposure | Percentage | Share of value with a path to a category flagged as high risk, within the configured hop limit. |
| Known entity exposure | Integer | Number of attributed entities in the exposure path. |
| Investigation status | Enum | Whether this address is part of an open case in your organisation. |
A risk score is an analytical indicator derived from observable on-chain activity. It does not independently establish unlawful conduct, and attribution is asserted only where a source, evidence trail and confidence level exist.
What address analysis cannot tell you.
Stating this plainly is not a weakness of the product. It is the difference between an analytical tool and a fortune teller.
Attribution errors are expensive. An address wrongly linked to a person can end an account relationship, trigger a report to a regulator, or be quoted in a filing. Every attribution the platform asserts carries a source, an evidence reference and a confidence level so a reviewer can disagree with it.
No. A risk score is an analytical indicator derived from observable on-chain activity. It reflects exposure to categories of counterparty, not a determination about any person. Treat it as a prompt to look further, not as a conclusion.
Exposure is measured within a configurable hop limit, because value dilutes as it travels and an unbounded search returns exposure to everything. The hop limit used is recorded on every result so two analysts comparing scores know they are comparing the same measurement.
Where a bridge transaction can be matched to its corresponding transaction on the destination chain, yes. Where it cannot be matched with confidence, the trace is marked as ending at the bridge rather than continued on a guess.
Yes. New activity, new attributions and new list designations all move a score. Scores are versioned, so a score quoted in a report can be reproduced with the model and data that produced it.