Content Moderation

No jargon, no math

Every piece of user content enters the same pipeline. First, screening: classifiers score the content against your policy categories (violence, fraud, hate, spam), and databases of known-bad material match by hash before a model even runs. Second, the fast path: content far from any line publishes immediately; content clearly over it is removed and logged. Third, the grey zone: borderline cases go to trained human reviewers, who see the content plus the model's score as context, never as the verdict. Fourth, the loop: every human decision is logged and returns as training data, so the classifiers learn your specific edge cases over time. Where it breaks: the model inherits the biases of its training data, context is hard (a weld photo trips the violence filter, a sarcastic review trips the fraud filter), and volume spikes force triage. Moderation is a triage system, not a justice system: its job is consistent, reviewable decisions at scale, and the appeal path matters as much as the filter.