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 tools to predict viewer engagement and deliver insights on targeting viewers more precisely.
The client required specialized data labeling support, 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 so it could effectively predict audience reactions and target the most receptive segments.
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:
Successfully scaling the data labeling process to achieve the necessary precision across a vast and varied library presented several significant hurdles:
To fulfill the client's demands for high-volume data labeling services and precise storyline 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 and video labeling 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 |
Improved Client's AI Model Accuracy by 65%
Enabled Market Expansion into Spanish and German Territories
Reduced Content Categorization Errors by 60%
Accelerated the Client's Product Development Timeline by 4 Months
Our team delivers accurate text, image, and video annotations customized for your specific AI use case. From model training to ongoing performance refinement, we help you power smarter, more reliable AI systems.
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