Use Case

AI Content Moderation for LLM Applications

Filter LLM inputs and outputs. Protect users from harmful model responses and your model from malicious prompts with a complete moderation harness.

The Challenge

LLM-powered applications are vulnerable to prompt injection, jailbreaks, and generating harmful content. Relying on a single provider's built-in filters creates a single point of failure.

The OpenModeration Solution

Defense in depth. Layer multiple AI providers for both input and output filtering. Use the Action Engine to define custom safety policies without writing complex moderation logic.

The Result

Ship your AI application with confidence. Maintain strict policy compliance, prevent brand-damaging outputs, and keep your development team focused on building features, not moderation infrastructure.

The fastest-growing moderation need

Every LLM-powered application needs content moderation on two fronts:

Input filtering: Block harmful, illegal, or manipulative user prompts before they reach the LLM.
Output filtering: Catch dangerous or inappropriate model responses before users see them.

OpenModeration lets you layer multiple providers for defense in depth, all managed from a single dashboard.

Input → Moderated → LLM

text

User prompt -> {"input": prompt} -> POST /v1/moderation ->
-> if not flagged -> forward to GPT-4, Claude...
-> if flagged -> return "Content policy violation"

LLM Output → Moderated → User

text

LLM response -> {"input": response} -> POST /v1/moderation ->
-> if not flagged -> display to user
-> if flagged -> show fallback response

Related Resources

Guide

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