Our client is a prominent US-based enterprise specializing in predictive analytics and machine learning. Their core business involves analyzing how consumers engage with entertainment content (including full-length features, trailers, and series) and forecasting shifts in audience tastes. Moving beyond conventional survey methods, they utilize advanced AI systems to predict future viewer engagement, leveraging web research services and structured data labeling services to refine audience insights.
The client required a specialized data labeling service, including text labeling services and video labeling services, to significantly boost the performance and accuracy of their proprietary machine learning models. The scope demanded resources with a strong understanding of narrative structure, cinema genres, and storytelling to provide high-quality metadata tagging. Our task was to assign precise, context-specific keywords (data tagging) to every narrative asset, serving as crucial input features for the client's AI to predict audience appeal and target groups accurately.
Essentially, any asset carrying a narrative—whether video or text—requires data labeling with keywords that describe genre, emotion, theme, character archetypes, and viewer resonance. This catalog included:
Our operational mandates were:
Achieving the required scale and precision for Content Labeling across a massive, diverse content library presented several obstacles:
To fulfill the client's demands for high-volume data labeling services and precise content annotation, we assembled a specialized team of 25 dedicated resources: 20 data labelers (equipped with content analysis and web research expertise), one German language specialist, one Spanish language specialist, and three senior QA analysts.
Our methodology for accurate content labeling and metadata tagging was multi-layered:
Each piece of content (synopsis, trailer, description) was systematically dissected into its fundamental narrative components:
This thorough process ensured that annotators grasped the essence of the content before assigning keywords. Where cultural themes or narrative points were nuanced, annotators used web research to cross-check interpretations and refine keywords for precise audience alignment.
To identify and assign the most relevant keywords for each title, our team employed a semantic mapping strategy. Under this approach, tags were carefully selected to capture two dimensions of the narrative:
Tagging both dimensions ensured that the annotated dataset accurately reflected not only what the content featured but also why it would appeal to specific viewer segments, which is crucial for predictive analytics.
We engineered a structured data tagging hierarchy that functioned as a unified dictionary and classification guide. This standardized keyword ontology organized key terms into structured parent categories (e.g., genres, themes, moods), preventing annotators from creating redundant or non-standardized labels.
For example, related terms like "Investigation" and "Detective" were grouped under the parent category "Crime/Thriller." This framework ensured accuracy and provided the consistency required for scalable labeling across thousands of titles.
We established a multi-tier text labeling service and video labeling service workflow where initial keyword tagging was validated by peers and finalized by QA specialists for contextual accuracy.
We implemented stringent protocols to ensure end-to-end security throughout the data labeling project:
With scalable, narrative-focused video labeling services, text labeling services, and metadata tagging, we delivered measurable outcomes that directly enhanced both AI model accuracy and operational efficiency for the client.
| Metric | Before SunTec | After SunTec | Improvement |
|---|---|---|---|
| Labeling Accuracy | 85% (Internal Benchmark) | 98-99% | +13-14% |
| Daily Throughput | ~60 assets per day | ~100 assets per day | +65% |
| Turnaround Time | 3-4 days per batch | 24-48 hrs | 2x faster |
We provide text, image, and video labeling services tailored to your unique use case, supporting your AI projects across all stages—from initial machine learning model training to continuous optimization.
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