In a digital landscape flooded with user-generated content, maintaining brand integrity is more critical than ever. Our Brand Safety Annotation service helps you train robust AI models to detect and avoid unsafe, inappropriate, or unsuitable content—ensuring your ads appear only in the most brand-aligned environments.
We offer end-to-end annotation solutions designed to help media platforms, ad tech firms, DSPs, and social platforms scale safely while meeting global advertising standards.
Customizable Brand Safety Frameworks
Your brand is unique—so your safety thresholds should be too. We build custom taxonomies aligned with your risk tolerance, industry requirements, and regulatory needs. Whether you’re working within GARM standards or have proprietary brand safety protocols, we tailor our annotation to reflect your brand’s voice and values.
Multi-Level Content Classification
We offer granular, context-aware labeling at multiple levels:
01
Page-Level
Understand the overall sentiment, intent, and risk profile of a webpage
02
Ad-Slot-Level
Evaluate exactly where on a page your ad might appear—down to banners, pop-ups, and embedded videos.
03
Article-Level
Annotate specific sections for nuanced content decisions.
This ensures your programmatic ad placements are intelligent, safe, and contextually relevant.
Sentiment & Tonal Alignment
Beyond risk detection, we assess tonality and sentiment—to ensure that even safe content still aligns with your brand’s emotional and communication standards. We annotate content tone (sarcastic, neutral, aggressive, promotional, etc.) and help your models make judgment calls that go beyond keyword-based filtering.
Built for Scale and Security
We operate in secure environments with enterprise-grade data protection, allowing seamless ingestion of your content across formats: webpages, videos, social posts, user reviews, and more. Our annotation workforce is trained on domain-specific protocols and backed by multi-tiered QA to ensure top-tier precision—every single time.
Designed for AI Model Training
Our labeled datasets are built with machine learning in mind—providing the signal-rich, high-quality input your models need to evolve. Whether you’re detecting hate speech, misinformation, adult content, or polarizing opinion pieces, we help you reduce false positives and capture subtle edge cases.