AI TAGGING
AI TAGGING
Smart AI For Tagging
AI Tagging uses advanced algorithms to intelligently analyze visual content, identifying and labeling thousands of objects, people, scenes, and actions. By generating rich metadata, it enhances content understanding, streamlines categorization, and enables faster, more efficient search, organization, and moderation across digital platforms.
Supported Moderation
Every image, video, or text is checked instantly no risks slip through.
What is AI Tagging?
Organize and Protect with Smart Tags
AI Tagging adds intelligent labels to every piece of content, enabling faster moderation, precise categorization, and more relevan... Read more
Automated Tagging with Precision
Automatically assign accurate content tags—such as promotional, explicit, or sensitive—at scale. This ensures inappropriate conten... Read more
Safeguard Young Audiences
AI-generated tags help identify age-sensitive material, enabling platforms to restrict or flag content to ensure children and teen... Read more
Enhance Trust Through Transparency
By tagging and classifying content proactively, your platform gains control over visibility, relevance, and compliance—bolstering ... Read more
How our Moderation Works
AI scans content instantly to detect and block nudity and explicit content.
Why use MediaFirewall.ai's AI Tagging?
Simply put, our filter is the best value for money out there
Operational Efficiency at Scale
Automate AI-driven tagging across millions of uploads in real-time. Reduce depen... Read more
Real-Time Tagging Across Streams
Classify and label live content—including visuals, text, and audio—without delay... Read more
Seamless Platform Integration
Deploy tagging via API or SDK with customizable labels by category, geography, o... Read more
Custom Rules for Smart Enforcement
Apply dynamic tagging policies across different content types and user segments.... Read more
Related Solutions
AI Tagging FAQ
The AI Tagging is an advanced machine learning module that automatically labels content with relevant tags based on visual, textual, and contextual cues. It helps platforms organize, moderate, and surface content more intelligently and efficiently.
The filter uses a combination of computer vision and natural language processing (NLP) to analyze media—including images, videos, and text—and assigns semantic tags that describe the content’s themes, objects, context, and compliance attributes.
It can generate a wide range of tags, including content category (e.g., fashion, travel), compliance labels (e.g., explicit, promotional, PII), sentiment tags, age-appropriateness, and more—tailored to platform needs.
Our AI uses contextual modeling to differentiate subtle or borderline cases—like distinguishing between satire and misinformation—though human review can be integrated for added precision.