A vision model flags unsafe images before they upload. An NLP classifier scores comments for toxicity and policy violations. Both run silently in the background — hard-block on violations, no queue for your team.
We pick the best-fit components based on your data volume, latency needs, and privacy requirements.
Images (NSFW, violence, weapons, hate symbols), text (toxicity, harassment, spam), and video keyframes. We can add custom detection for domain-specific violations (e.g. financial fraud, medical misinformation, brand-safety rules).
Yes. Base models handle standard categories out of the box. On top of that we layer your policy — for example, some clients allow depictions of alcohol, others don't. Rules are managed via an admin panel.
Depends heavily on rule strictness and content type. For NSFW image detection, precision is typically 96-98%. For toxicity, 88-92%. We tune during onboarding using a labeled sample of your actual content.
Under 500ms per image, under 200ms per comment. Fast enough to happen invisibly at upload time — the user never sees a review window.
Yes. For clients with data-sovereignty requirements, we deploy the moderation stack on your own VMs. No content ever leaves your infrastructure.
Vision models generalize — image recognition beyond moderation.
Explore →Deploy the moderation stack privately on your own hardware.
Explore →Wire moderation into your existing content pipeline.
Explore →Bring us the type of content you're currently reviewing manually — image uploads, forum comments, product reviews — and we'll scope a moderation model with the right precision-recall trade-off for your risk tolerance.
