AI Data Labeling Services
Hansem Global labels and annotates AI training data in text, image, audio, and video formats for large-scale projects in Asian languages.
Why Hansem Global?
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Built for continuous, ever-changing projects.
AI data work arrives nonstop and never looks the same: text one day, audio the next, evaluation after that. Some projects run for months while others must ship in two days. We're structured to absorb that flow without quality dropping, so no single task depends on any single worker.
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Trained specialists, not basic labelers.
We staff projects with people selected for language judgment and domain understanding, organized in L1–L3 proficiency tiers and matched to each task's complexity. This is a different model from anonymous crowd labor priced to the bottom.
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A specialized operation, not just a platform.
Years of production taught us where this work is won or lost: PM-led instruction design, rigorous workforce vetting, and an RMS built to run high-volume labeling. This operational depth is far harder to build than a normal translation project, and it's what clients are really paying for.
AI Data Services Offerings
Text Labeling
Text is the most widely used data type in AI training, and the type we handle most. We tag user intent, sentiment, context, and key entities so models can accurately interpret natural language, drawing on expert linguists across Asian languages and beyond. This kind of high-quality training data strengthens NLP applications like chatbots, sentiment analysis, and information extraction.
Tagging intent, sentiment, context, and key entities.
Audio Labeling
Spoken language varies by environment, intonation, dialect, and speaker. Our audio labeling prepares speech data for voice AI by transcribing audio accurately and labeling metadata such as emotion, language, and dialect. We also identify non-speech sounds, such as a glass breaking, to broaden recognition coverage. This kind of data supports ASR and voice-assistant development, and is also used in security and incident-response systems.
Accurate transcription with emotion, language, and dialect labeling.
Image Labeling
Image labeling provides the training data behind computer vision, facial recognition, and other visual AI. We label objects, facial landmarks, and image-level classes so models can detect and classify visual information reliably. This kind of dataset is applied across autonomous driving, security monitoring, manufacturing quality inspection, and facial recognition development.
Labeling for object detection, classification, and facial landmarks.
Video Labeling
Video labeling turns dynamic, frame-by-frame visuals into training data for vision AI. We track object motion across frames and label human and object actions and key events. This kind of data matters for models that must work in real-world conditions: autonomous driving, action recognition, CCTV surveillance, and event detection.
Object tracking with action and event labeling across frames.
Explore More
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