AI Content Moderation with Fenic — Understand Context, Not Keywords (in 120 Seconds)
AI content moderation that understands context, not just keywords.
In this 2-minute demo, we show how Fenic powers context-aware content moderation, capable of catching scams, harassment, and spam without relying on keyword lists or human review teams.
Traditional moderation depends on keywords or armies of human reviewers, which are slow, expensive, and inaccurate. Fenic replaces both with structured AI moderation that understands intent and context.
We define a clear moderation schema — is_safe, violation_type, and reason — and let Fenic’s semantic.extract() automatically classify each message. From romance scams to crypto pitches to genuine conversations, the model correctly identifies violations while minimizing false positives.
🔑 What you’ll see:
💬 Real dating app messages (legit + scammy)
🧠 Structured AI moderation with semantic.extract()
🛡️ Catches scams, harassment, spam — no rules or filters
💰 Reduces costs by eliminating large manual review teams
This is AI-native content moderation — accurate, contextual, and cost-efficient.
👉 Try the Colab demo: https://colab.research.google.com/git...
👉 GitHub: https://github.com/typedef-ai/fenic/t...
👉 Docs: https://docs.fenic.ai/latest
👉 Join the community: / discord
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