Our client is a US-based company that uses machine learning and predictive analytics to study how people consume entertainment content (like trailers, shows, or movies) and how audience preferences are changing. Instead of relying on outdated survey-based research, they utilize AI tools to forecast audience engagement and guide content creators/distributors on how to effectively reach the right viewers.
The client sought specialized data labeling services to enhance the accuracy of their machine learning models. The project required resources with a deep knowledge of cinema, storytelling, and genres to ensure high-quality metadata tagging. Our scope of work included assigning precise, context-specific keywords to each storyline, which served as critical inputs for the client’s AI models, enabling the prediction of target audiences.
Essentially, anything that carried a narrative/storyline—whether in video or written form—needed to be annotated with keywords about genre, themes, emotions, characters, and audience appeal. This included -
Our team had to:
To meet the client’s requirement of large-scale text data labeling and keyword tagging, we deployed a team of 25 dedicated resources - 20 data labelers(having relevant entertainment industry knowledge and content analysis & web research expertise), 1 German language expert, 1 Spanish language expert, and 3 senior QA analysts.
We followed a multi-layered methodology for accurate content analysis and keyword tagging. The approach involved:
Each trailer, synopsis, or show description was broken down into narrative layers:
This ensured that annotators fully understood the essence of the content before assigning keywords. Where themes were nuanced or culturally rooted, annotators supplemented content review with web research to cross-check interpretations and refine keyword choices for precise audience targeting.
To identify and assign relevant keywords for each content piece, our team utilized a semantic mapping approach. Under this approach, keywords or tags were carefully selected to capture:
By assigning both types of tags for each content type, we ensured that the annotated dataset reflected not just what the content was about, but also why it would resonate with specific audiences.
We developed a keyword ontology framework (organizing key terms into a structured hierarchy of genres, moods, and themes) that served as both a dictionary and a roadmap for content classification. Instead of leaving room for annotators to invent their own terms, this standardized keyword set ensured labeling consistency.
For example, terms like “Detective” and “Investigation” were placed under the broader parent category “Crime/Thriller.” This framework provided a unified reference point, enabling accurate and scalable labeling across thousands of titles.
We implemented a multi-tier text, image, and video labeling workflow where initial keyword tagging was followed by peer validation and final review by QA specialists for contextual accuracy.
We ensured end-to-end security throughout the data labeling project by implementing strict protocols:
With scalable, narrative-focused video and text labeling services, we delivered measurable outcomes that directly impacted both operational performance and AI model accuracy 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 Expansion into Spanish and German Markets
Reduced Content Categorization Errors by 60%
Accelerated the Client's Product Development Timeline by 4 Months
We provide text, image, and video labeling services that adapt to your unique use case and support your AI projects across all stages — from initial model training to ongoing optimization.