Access diverse datasets fast and at global scale
Accelerate model training and time-to-market, with AI outputs that scale accurately across global audiences
Unlock the power of your LLMs
Train smarter and solve edge cases that generic data pipelines can't handle across the full data loop, including collection, annotation, evaluation, and training environments, on one platform.
Acquire high quality datasets
Fuel high-performing AI models with diverse, real-world datasets, including point-of-interest (POI) data, audio, video, image, and text, collected and annotated at scale by our global network of over 10 million contributors and specialized experts.¹
Train with expert human feedback
Benchmark and fine-tune your models with structured reinforcement learning from human feedback (RLHF), preference ranking, and reward modeling, delivered through custom reinforced learning (RL) gyms built around your specific use case.
Own your data
Your proprietary data and IP remain protected. We do not use your data to train Uber's general purpose internal AI models and it is protected by organizational and technical safeguards.
Ensure reliability and performance
Leverage our experience training over 20,000 AI models. Get key performance insights, streamlined testing, and high-impact quality assurance across multiple operating systems and over 3,000 test devices.
Frequently asked questions
What is audio labeling?
Audio labeling tags voice and audio data to help machine learning models recognize speech, music, and effects, enabling applications like voice assistants, speech-to-text, and sound event detection.
What is video labeling?
Video labeling annotates frames with tags to help machine learning models detect objects, actions, and events, enabling applications like surveillance, autonomous driving, and content recommendation.
What is image labeling?
Image labeling assigns meaningful tags or annotations to images, helping machine learning models recognize objects, scenes, or patterns for applications like autonomous vehicles, facial recognition, and medical imaging.
What is text labeling?
Text labeling annotates data with tags to help machine learning models understand it, enabling tasks like sentiment analysis, entity recognition, and intent classification for AI-driven chatbots, search, and recommendations.
Let’s build better AI together
Tell us about your project. We'll show you the data that gets you there.
Let’s build better AI together
Tell us about your project. We'll show you the data that gets you there.
¹ As of March 2026
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