AI Content Moderation Template for AI Products

For most products, start with a vendor API (OpenAI Moderation, Google Perspective, Azure Content Safety) and add custom classifiers for domain-specific policies. Building from scratch only makes sense if you have proprietary training data and policies that are genuinely unique. The vendor APIs handle common categories (hate speech, spam, adult content) well, while your custom layer handles product-specific rules.

Route low-confidence classifications in unsupported languages to human review queues staffed by native speakers. Track accuracy by language and set per-language confidence thresholds. For launch, it is acceptable to have human-only moderation for low-volume languages while you improve model coverage.

For most platforms, keep the false positive rate below 2% for auto-removal actions. For less severe actions (labeling, downranking), up to 5% may be acceptable. The key is pairing auto-actions with a fast, accessible appeal process. Users tolerate occasional mistakes if they can resolve them quickly.

Assume bad actors will test your system. Include adversarial examples in your test suite: unicode tricks, leetspeak, image-text overlays, context switching, and prompt injection. Run red-team exercises quarterly. Keep rule-based blocklists as a fast first-pass filter that catches known evasion patterns before content reaches the model.

Calculate based on: (daily content volume) x (% routed to human review) / (reviews per reviewer per hour) / (hours per shift). A typical reviewer handles 200-400 text reviews per hour or 50-100 image reviews per hour. Add 20% buffer for calibration, training, and wellness breaks. Staff for peak volume, not average.