Content Moderation Training Data
Fuel Safer Platforms with Smarter Moderation Datasets
In today’s digital landscape, user-generated content (UGC) flows faster than ever—across comments, posts, images, livestreams, and forums. Platforms must act in real-time to detect harmful, offensive, or policy-violating content without disrupting user experience or freedom of expression.
We help you build accurate, scalable, and policy-compliant training datasets for content moderation models—across text, image, video, and audio formats. Whether you’re training AI for automated moderation or supporting human review workflows, our annotated datasets provide the ground truth you can trust.
Multi-Modal Content Labeling
Go beyond text—moderate the entire user experience.
We annotate content across formats including:
- Text: Comments, reviews, usernames, bios, messages
- Images: Memes, profile pictures, uploads
- Audio: Livestreams, voice notes, podcasts
- Video: Short-form clips, full-length streams, background detection
Each format is tagged based on custom moderation taxonomies that can include:
- Hate speech
- Bullying or harassment
- NSFW content
- Misinformation or fake news
- Dangerous challenges or incitement
- Spam and scam content
- Deepfakes or manipulated media
Example: We detect a seemingly harmless meme that contains embedded coded hate speech or regional slurs—flagged by combining visual + text overlay annotation.
Real-Time Content Flagging Support
Train your models to act in milliseconds.
We build datasets optimized for latency-sensitive environments:
- Streaming content platforms (e.g., live gaming, podcasts)
- User comments and replies in real time
- In-app reporting systems
Our annotations support:
- Immediate remove triggers
- Model fine-tuning for ambiguous cases
- Threshold setting for human-in-the-loop review
Example: Our datasets help reduce model confusion between edgy humor and genuine threats—making real-time filters smarter and more accurate.
Region-Specific Compliance
Stay compliant across markets and laws.
We support moderation taxonomies tailored to major data protection and platform regulations, including:
- EU Digital Services Act (DSA)
- Children’s Online Privacy Protection Act (COPPA)
- IT Rules 2021 (India)
- Global platform-specific content policies (Meta, YouTube, TikTok, etc.)
Whether you’re launching in a new geography or aligning with updated rules, we ensure your datasets are:
- Culturally aware
- Legally aligned
- Ethically annotated
Example: For a child-focused app in the EU, we tag content for potential grooming risks, age-inappropriate ads, or misleading promotional language in compliance with COPPA and DSA
Key Use Cases
01
AI Moderation Model Training
Curate balanced datasets that cover edge cases and avoid over-flagging. Train your ML models to detect subtle violations in multiple formats and languages.
02
Human Review Workflow Enrichment
Use our high-quality labeled datasets to support human moderators with better decision-making tools, training material, and edge-case calibration examples.
03
Policy Testing & Risk Simulation
Before rolling out a new policy or moderation model, test it against our annotated content to simulate outcomes, identify gaps, and reduce unintended bias.
04
Platform Localization
Moderate based on local norms and context, not just global policy. Our region-aware tagging accounts for linguistic nuance, symbolic gestures, and cultural references.
Why Our Annotation Framework Works
✅ Multi-format fluency (text + image + audio + video)
✅ Culturally intelligent, policy-aligned taxonomies
✅ Scalable pipelines—from 1,000 to 10 million+ data points
✅ Secure data handling with enterprise-grade SLAs
✅ Human-in-the-loop quality assurance
Ready to Build Smarter Moderation Engines?
Let’s work together to create responsible, compliant, and user-friendly online spaces
Contact us to see sample datasets, explore annotation pipelines, or co-create a moderation taxonomy for your platform.
