Clear the path for authentic experiences.

Bad actors don’t slow down. They continue to get faster and more elusive. Musubi is how innovative brands stay ahead.

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AI tooling for trust & safety, content moderation and fraud detection. Start with one. Stack the rest when you’re ready.

See how it works

Customer Impact

Bluesky removes spam 60x faster with 99.8% accuracy. Feeld catches 83% of fraud in 1.4 seconds. Stocktwits cut moderation costs by over 90%.

Building blocks for Safety and Fraud teams shipping AI.

Custom models, agents, workflows, policies, and observability, knit into one powerful system that learns from your team.

The Musubi Way

Complex problems need solutions built around you.

No black boxes. No one-size-fits-all. No siloed tools. No waiting on engineering. We give you the tools you need, how you need them.

Human-in-the-loop

Human-in-the-loop

Catch issues early, and fix them. Observe. Iterate. Improve. Every step of the way.

Customization

Customization

This is your system, not ours. Build it with your expertise at the foundation.

Steerability

Steerability

Your policies, your call. As nuanced as you need them to be. Updated in real-time with no need to bother any engineers.

Benchmarking

Benchmarking

What works best for your content? To find out, you can test the latest frontier LLMs and fine-tuned safety models.

Multi-Modal

Multi-Modal

The full view of what a user is doing across text, image, video and audio – and multimodal. In 150 languages and counting.

Tools for Observability and Insights

Getting ahead of harm can make it so that harm never happens.

View all Observability Tools
Cluster visualization of platform activity
Summarize the biggest clusters

Monitoring & Insights

Know what's happening on your platform and why. Data analytics and clustering visualization, so you can run with the insights.

Risk-matching detection of suspicious account signals

Matching...

Repeated Messages

Unverified ID

Multiple Accounts, Same Device

Suspicious IP

Risk Score

36

Proactive Detection

Spot fraudulent users and harmful content at the source. Not after a report lands in the queue.

Tools for Action and Enforcement

Insights are only as good as what you can do with them.

View all Action Tools
Content moderation queue labeling messages by policy

Content Moderation

Choose from top-tier LLMs to label and moderate any type of content (yep, multimodal, too) across 150 languages and counting.

Decision outcomes routing flagged content to ban, skip, or approve

Fraud Enforcement

Act on fake accounts, scam rings, and coordinated abuse. Collaborative machine learning that gets sharper every time your team makes a call.

Aaron Rodericks
“Everyone was trying to do AI the exact same way — hey, we can look at one piece of content and make an accurate policy assessment for you. Musubi was literally the only example that I had looked at in which you were doing a whole account assessment.”
Aaron RodericksHead of Trust & Safety, Bluesky

Our tooling stack, or yours.

Musubi fits into your workflow, however you need it to. We plug into the moderation console you already use — in-house or partner.

Starting from scratch? Musubi Coop, our hosted open-source platform, has you covered.

See Musubi Coop
Tremau

Tremau

Nvidia

Nvidia

Musubi Coop

Musubi Coop

In-house Tooling

In-house Tooling

850+ million people experiencing what was intended

BumbleFanvueRakuten ViberPie$5B+ Social Platform$50B+ Gaming Platform$5B+ AI Platform

Browse the library every practitioner should bookmark.

AI Literacy for Trust & Safety and Fraud Teams: How Models Behave, Fail, and Mislead
AI Literacy for Trust & Safety and Fraud Teams: How Models Behave, Fail, and Mislead

AI Literacy for Trust & Safety and Fraud Teams: How Models Behave, Fail, and Mislead

A confident, fluent answer that happens to be wrong is the most dangerous thing an AI model will hand you. This guide covers how LLMs behave, where they fail, how they mislead, and what T&S and fraud teams can do to steer them.

How to audit your fixed ML classifier
How to audit your fixed ML classifier

How to audit your fixed ML classifier

Four diagnostic exercises for identifying the hidden performance gaps and costs in a fixed ML content-moderation classifier — and how to tell which gaps are fixable versus structural.

How to use LLMs for Content Moderation (in 2026)
How to use LLMs for Content Moderation (in 2026)

How to use LLMs for Content Moderation (in 2026)

A lot has changed in how T&S teams use LLMs for content moderation. This is a practitioner's guide to what's working in 2026: model selection, policy engineering, agentic workflows, and the operational practices that separate mature systems from experimental ones.

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Less scramble.
More signal.

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