From exchanges to custodians to treasuries, Web3Firewall and OraBrain handle the decisions that move money and carry risk. Here is where they are used, and how we measure them against generic models and legacy tools.
Wherever digital-asset value moves, Prax Labs evaluates the decision before it happens, in the context of who you are and what you are responsible for.
Screen every transaction before it is broadcast, intercept scams and fraud in real time, and keep AML and sanctions checks always on.
Assess signing-phase risk on the most sensitive holdings with analysis that never leaves your environment.
Track counterparty exposure and screen inbound and outbound flows against your own risk policy.
Catch exploit patterns, bridge and mixer exposure, and behavioral anomalies before a contract is drained.
Turn raw alerts into auditable, evidence-backed decisions with Travel Rule support and SAR-ready reasoning.
Bring on-chain intelligence into the regulated stack as digital assets reach traditional finance.
Prax Labs is built around pre-execution. The same loop runs whether the trigger is a transaction, an alert, or an autonomous agent action.
A signal appears: a transaction at signing, an on-chain event, or a behavioral anomaly.
OraBrain models intent in your context, on-chain and traditional data together, inside your perimeter.
It returns an evidence-backed verdict, stating what is known and naming what is missing.
Web3Firewall enforces: allow, hold, quarantine, or escalate, before anything is broadcast.
We do not test trivia. We evaluate OraBrain against generic cloud LLMs and legacy rule-based tools on the scenarios institutions actually face, and we score the dimensions that decide real outcomes.
Given partial transaction context, reach a defensible filing decision instead of hedging.
Triage an infrastructure anomaly and separate the real signal from the noise.
Catch fraud patterns on a live wallet without drowning analysts in false positives.
We publish methodology and we show real results. The evaluation below is one example, graded by frontier models acting as judge. Full results against your own scenarios are shared during evaluation, so you can verify them rather than take them on faith.
We posed hard AML and SAR determinations to OraBrain alongside leading frontier models, and had frontier models assess the reasoning. What stood out was not a score. It was how OraBrain worked the case.
OraBrain behaves like an investigator gathering evidence rather than a rules engine reaching a conclusion.Frontier model used as judge
What the evaluation surfaced about how OraBrain reasons
Commits to a conditional but clear call and states what would change it, rather than handing the decision back to the analyst.
Flags explicitly what it did not have, instead of quietly assuming its way to an answer.
Returns operational actions, freeze, scan, out-of-band verify, not just a verdict left for someone to interpret.
Reasons over mixers, bridges, cross-chain flows, and exploit signatures, the context a general model does not carry.
On a separate infrastructure-triage question, OraBrain's fast Instant tier diagnosed the issue correctly and chose the most economical first move, reading the error logs, but it under-committed on the final operational decision, which the Deep tier is built to make. We would rather show that than hide it.