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Overview

Klyra provides powerful AI-powered moderation models that can analyze and flag potentially harmful content across multiple modalities. Our models are continually trained on diverse datasets to ensure high accuracy and minimal bias.

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Available Models

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Text Moderation

Our text moderation model can analyze text content in over 50 languages for harmful or inappropriate content.

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Supported Categories:

  • Toxic: Hateful, aggressive, or insulting content
  • Harassment: Bullying, threats, or intimidation
  • Self-harm: Content promoting self-harm or suicide
  • Sexual: Sexually explicit or adult content
  • Violence: Graphic or violent descriptions
  • Hate speech: Content targeting protected groups
  • Spam: Unsolicited promotional content
  • Profanity: Swear words and offensive language

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Sample Request:

{
  "content": "Your text content here",
  "settings": {
    "categories": ["toxic", "harassment", "hate_speech"],
    "threshold": 0.7,
    "language": "en"
  }
}

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Model Selection

Klyra automatically selects the appropriate moderation model based on the content type you submit. You can also explicitly specify which model version to use: 
{
  "content": "Your content here",
  "settings": {
    "model": "klyra-text-v2",
    "categories": ["toxic", "harassment"]
  }
}

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Available Model Versions

Model NameContent TypeDescription
klyra-text-v2TextLatest text moderation model with improved multilingual support
klyra-image-v3ImageHigh-precision image moderation with 98.7% accuracy
klyra-audio-v1AudioAudio transcription and moderation
klyra-video-v1VideoFrame-by-frame video analysis

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Confidence Scores

All moderation results include confidence scores between 0.0 and 1.0 for each category:
  • 0.0: No detection of the category
  • 1.0: Highest confidence that the content belongs to the category
You can set a custom threshold for flagging content in the request settings. The default threshold is 0.7 for most categories.

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Model Ethics & Bias Prevention

Klyra is committed to providing fair and unbiased moderation systems. Our models are:
  • Trained on diverse, representative datasets
  • Regularly audited for demographic biases
  • Continuously refined based on customer feedback
  • Transparent in confidence scoring and decision making
Read more about our ethics principles and bias prevention practices.