Custom AI fraud detection, trained on your moderators.

Rules are static and gameable. Bad actors poke at them until they find the edge, and you’re always writing the next rule after something already slipped through. A model trained on your moderators’ decisions works differently: it generalizes from behavior across thousands of signals instead of firing on fixed thresholds, so it catches the variations of a tactic, not just the exact pattern you hand-coded.

And because it retrains regularly on your team’s latest calls, a scheme your analysts caught yesterday is something the model recognizes today, without anyone writing a rule for it.

It’s a behavioral ML model trained for you, on your data, never a shared classifier that pools everyone together. It retrains regularly on your latest moderation decisions, so it keeps reflecting how your team actually calls things. Those decisions train your model and only yours; cross-customer training is off by default and never turned on without an explicit signed agreement, and the platform is SOC 2 Type II certified and GDPR compliant.

We pride ourselves on playing well with others. Musubi’s systems stack nicely on top of what you already run.

Yep! Account-level fraud detection works entirely on its own, and so does content moderation; neither requires the other. Most teams actually start with one and add the other later. They are stronger together, since account-level signals can sharpen content decisions and vice versa, but there’s no bundle you’re forced into. Start where the pain is and expand when it makes sense.

An account-level decision lands in just a few seconds. Each one is a full assessment of the whole account (its behavior, content, connections, device, and history) not a quick check on a single post. End to end, high-confidence cases get caught and actioned automatically in around ten seconds, which is fast enough to stop an attack while it’s still in progress rather than after the damage is logged. For context, at Bluesky that took median time-to-action from over four hours down to under four minutes.

That’s the right question to ask of any fraud model, because wrongly banning real users is often worse than missing a few bad ones. Because Musubi learns from consistent patterns across your whole team rather than any single reviewer on an off day, customers see up to 89% fewer false positives and ban appeals. And you hold the accuracy-versus-automation dial: you decide how confident the model has to be before it acts on its own, and everything below that line routes to a human.

If your problem is payment fraud and chargebacks, tools like Sift or Forter are built for that world. Musubi is built for integrity and behavioral fraud, like fake accounts, scam and romance rings, bot networks, ban evasion, and coordinated abuse, where the harm lands on your users and your platform, not just a transaction. The deeper difference is that the judgment the model runs on is yours. It learns from your own moderators’ decisions rather than scoring against a shared cross-customer network, so it reflects how abuse actually shows up on your platform instead of an industry average.