AI text moderation for user-generated content

Lasso's four-layer pipeline automates 99.9% of moderation decisions. Each ML classification carries a confidence score, high-confidence violations are actioned immediately, and ambiguous cases pass to the AI Moderator before any human sees them. The remaining edge cases reach your team with full context, and every decision they make retrains the system.

Keyword and blocklist filters match exact strings, so they miss evasion like leet speak and unicode tricks and they flag harmless messages that happen to contain a banned word. Lasso adds ML classification, your own custom rules, and an AI Moderator that weighs context such as sarcasm and coded language. The result is fewer false positives and far more caught evasion than a blocklist can manage.

Text is classified in real time, inline, before your users see it. The ML layer scores high-confidence content immediately, and only genuinely ambiguous messages move through the later layers. You moderate through the API and the dashboard, so flags reach your queues as content is posted.

200+ languages with automatic language detection. Supports Latin, Cyrillic, CJK, Arabic, and other script families. Each language includes toxicity, hate speech, profanity, and spam detection.

Detection across multiple evasion techniques: leet speak, unicode substitution, character spacing, phonetic swaps, and symbol replacements. The AI Moderator adds context-aware detection for novel evasion attempts that pattern matching alone would miss.

Four sequential layers. ML models handle high-confidence classification. Custom rules enforce your platform standards. The AI Moderator resolves ambiguous content with context awareness. Human moderators handle the final edge cases and retrain the system. Result: 99.9% automation.

Yes. AI models detect grooming language patterns and CSAM indicators. CSAM detections trigger automatic reporting to authorities. Detection reads behavioral patterns across the whole conversation rather than matching isolated keywords. Zero-tolerance enforcement is built into the pipeline.

Yes. Custom models are trained on your platform's specific context and community norms. A gaming community has different language patterns than a marketplace or dating app. Models improve continuously as human moderators make decisions on edge cases.