Video classification
Assign category labels to video content based on scenes, actions, objects, topics, or visual patterns to help models recognize and organize video data.
Turn moving visual content into structured signals for classification, conversation analysis, quality evaluation and natural-language understanding.
We align video-level labels, dialogue context and natural-language descriptions with your model and evaluation goals.
Assign category labels to video content based on scenes, actions, objects, topics, or visual patterns to help models recognize and organize video data.
Track conversation flow, speaker intent, and dialogue context within video content to support conversational AI, user behavior analysis, and multimodal model training.
Review video content, annotation quality, visual relevance, and labeling consistency to ensure datasets meet training and evaluation requirements.
Create natural language descriptions for video content to support video understanding, search, accessibility, closed captioning, and multimodal AI applications.
We define scene, action, dialogue and caption rules around the behavior your system needs to recognize or explain.
A calibrated workflow keeps temporal context, captions and classification decisions consistent across footage.
Align classes, time units, dialogue rules, captions, volume and acceptance criteria.
Qualify reviewers with representative footage and guideline feedback.
Run controlled production with temporal checks, audits and issue resolution.
Provide structured video labels with quality findings and documentation.
Share your sample footage, target labels and quality requirements. We’ll propose a focused workflow with clear review criteria.
Discuss your video annotation project ↗