Most Telegram moderation bots run on keyword blocklists — easy to bypass, high false-positive rate, zero context awareness. ModerAI takes a different approach: you describe your group's topic in plain text, and the NLP pipeline uses that as a context window when classifying messages. The 15-layer AI stack handles the rest — no rule-building, no regex. How does a natural-language topic description feed into a spam classification pipeline at scale?
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