Content moderation provides access to specialized models for evaluating text safety and brand risk. These models classify content across various safety categories, supporting use cases from general-purpose text moderation to domain-specific brand safety assessment.

Available models

SeekrFlow provides multiple content moderation models for different content types and contexts:

Seekr ContentGuard

ContentGuard is a purpose-built model for podcast transcript analysis. It evaluates diarized audio chunks for brand safety risk and civility scores.

Classification types:

• GARM brand safety (13 categories)
• Civility and hostility scoring

Best for: Podcast content moderation, brand safety assessment for spoken audio content

Meta Llama Guard 3

Llama Guard 3 is a general-purpose text moderation model supporting all content types. It classifies text using the MLCommons 22-category taxonomy.

Classification types:

• Safe/unsafe binary classification
• MLCommons taxonomy (22 categories)

Best for: LLM output filtering, agent guardrails, general text moderation

Use cases

Content moderation supports several workflows:

LLM guardrails – Filter agent outputs and user inputs for safety violations

Brand safety – Assess content suitability for brand association and advertising

Compliance – Screen content for regulatory requirements and platform policies

User protection – Detect harmful content in user-generated text

Model selection

Choose models based on content type and classification needs:

Integration

Content moderation models are deployed as endpoints and accessed through the SeekrFlow API. Models accept text input and return classification results with category labels and confidence scores.