Data Labeling
Turn raw data into learning fuel.
We provide high-quality, human-in-the-loop data labeling for AI teams that care about accuracy, speed, and context.
At CuriousAI, our mission is simple – turn intelligent technology into practical tools that help teams move faster and think bigger.
What We Offer?
Text & NLP Labeling
From sentiment and entity tagging to intent classification and summarization, we label large-scale text datasets with accuracy and consistency.
- Named entity recognition, sentiment, and topic labeling
- Multi-label classification, QA pairs, summarization tasks
- LLM fine-tuning and RAG optimization datasets
Image & Video Annotation
We provide pixel-perfect annotations for training computer vision models across industries—ecommerce, mobility, healthcare, and more.
- Object detection, segmentation, bounding boxes
- Frame-by-frame video labeling and activity tagging
- Custom classes and edge-case handling
Audio & Speech Labeling
Improve your speech models with labeled voice data—timestamped, transcribed, and tagged for acoustic insights.
- Speaker diarization, intent tagging, emotion labeling
- Time-aligned transcription and noise annotation
- Language-specific data sourcing and labeling
Quality Control & Feedback Loops
We don’t just label—we ensure every dataset meets the accuracy your models deserve, with robust QC and continuous improvement.
- Multi-pass QA with inter-annotator agreement
- Feedback-based re-labeling workflows
- Model-in-the-loop annotation refinement
Where Data Labeling Adds Value
Give your language models high-quality inputs that reflect your domain and use cases.
Instruction tuning datasets with curated prompts
Retrieval-augmented generation (RAG) context labeling
Safety, bias, and refusal-case annotation
Train AI to recognize products, trends, and behaviors visually and contextually.
Product image tagging and categorization
Visual search and style similarity annotations
Customer review sentiment analysis
Enable AI in healthcare with accurate, privacy-compliant data pipelines.
Radiology image segmentation and annotation
Doctor-patient transcript labeling
Clinical NER and medical coding datasets
Fuel perception models with high-quality visual and temporal data.
Lane, object, and pedestrian segmentation
Multi-camera and LiDAR annotation support
Video-based motion tracking and scene understanding
We work with AI and ML teams across sectors to power intelligent systems with clean, contextual data.
Why CuriousAI?
High-performing AI depends on high-quality data—and we take that seriously. At CuriousAI, we blend deep domain understanding with efficient labeling ops. Whether you’re building in healthcare, retail, or frontier AI, we tailor our workflows to your needs. With humans-in-the-loop, QC automation, and an eye for edge cases, we deliver the training fuel your models deserve.
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Our amazing clients are industry experts around the world.
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We're here to answer all your questions.
What is data labeling, and why is it important?
Data labeling is the process of tagging or annotating raw data (like text, images, audio, or video) to make it understandable for machine learning models. Labeled data is essential for supervised learning, where AI models learn by identifying patterns in annotated datasets.
