Whole Accounts
Catch the actor, not just the action.
Your model learns from your own team’s decisions, flagging accounts the way your platform defines abuse, not with a shared cross-customer network’s average. It reads thousands of signals per account (behavior, content, connections, device, history, and more) to catch ban evaders, coordinated rings, account takeovers, bots, spam, and scams. Once it knows what a bad account looks like for you, it flags new sign-ups before they ever post.
At Bluesky, accounts Musubi actioned drew 75% fewer user reports, because users never saw the content in the first place.
Matching...
Repeated Messages
Unverified ID
Multiple Accounts, Same Device
Suspicious IP
Risk Score
36
Learning Loop
Catch more bad actors while flagging fewer good ones.
Every call your moderators make improves your model. It learns from consistent patterns across your whole team rather than any single reviewer on an off day, so our customers see up to 89% fewer false positives and ban appeals than human-only queues.
Adapts Daily
The tactic caught yesterday is automated today.
The model retrains every night behind automated quality gates, so the synthetic-ID ring that surfaced yesterday is caught today, not next quarter. When a pattern is high-confidence, matching cases get actioned automatically in around 10 seconds.
For Bluesky, that took median time-to-action from over four hours to under four minutes at 99.8% accuracy and 85% lower cost than human moderation alone.
