Resources
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.
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.
A practical comparison of the three automated content-moderation approaches — rule-based, ML classifiers, and LLM-based systems — where each excels, where each breaks down, and how to choose for your platform.
Can you tell which online comments were written by a bot? We scored 500 of them across eight dimensions and a library of 60+ AI-writing patterns. The answer changed what we think platforms should be optimizing for.
A practical guide to LLM content moderation for T&S teams: model selection, integration architecture, bias mitigation, golden datasets, and human oversight. Real deployment pitfalls and solutions from production systems.
When AI moderation drifts in production, the signal often comes too late. This post walks through the Agreement Observability tool we built to track model-moderator agreement in real time and simulate threshold tradeoffs before they become production problems.
Leadership pushing your T&S team to be AI-first? Practical guide to skills your team actually needs (prompt engineering, systems thinking, AI literacy) and how to build buy-in without engineers. From operators who've done this transition.
Musubi partners with NVIDIA to provide an integration between PolicyAi and NeMo Guardrails, allowing developers to use plain language to steer custom LLM content labeling and AI guardrails.
AI agents are changing the threat landscape for Trust & Safety. Musubi's agentic AI detection gives platforms the visibility they need to identify agent activity and decide what to do about it.
We built an open-source GIST implementation and benchmarked it on T&S datasets. GIST matched classifiers trained on 5x more data, helping teams do more with smaller labeling budgets.
Musubi goes full stack. Get AI-powered moderation and human review tools together, whether you're a startup or scaling to millions of users.
We analyzed 5,000 posts from an AI-agent social network and uncovered coordinated spam campaigns, prompt injection attacks, crypto exploitation, and surprisingly sophisticated philosophical discourse—all detected in minutes using behavioral clustering.
We're excited to announce a partnership between Musubi and Tremau, which enables us to continue our shared mission of delivering smart, scalable solutions that keep online spaces safe.
Your moderation system may be 2x more likely to flag certain users unfairly. Learn how to identify bias and fix it with practical testing frameworks.
A new idea for Content Radar, which enables Trust & Safety teams to spot coordinated spam in real time by clustering comments, flagging anomalies, and revealing new abuse patterns before they scale.
Senior T&S leaders share practical strategies for getting resources, using them wisely, and navigating constraints. Free playbook from Musubi's 2025 workshop.
Learn how to build golden datasets for content moderation evaluation. Practical guidance on dataset size, composition, labeling, and measuring what matters for T&S teams.
Most companies treat T&S like a cost center staffed by disposable contractors. Here's how to position T&S as strategic partners: building exec relationships, demonstrating ROI, and creating collaborative workflows with product/legal/ops. Includes implementation playbook.
With strong prompts and diverse training data, LLMs can distinguish harassment from banter, sexual content from sex ed, and satire from hate speech. Requires context examples, edge case coverage, policy engineering, and human calibration. Practical guide with real examples.
