Access diverse datasets quickly and on a 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 more intelligently and address edge cases that generic data pipelines can’t manage across the entire data loop, including collection, annotation, evaluation, and training environments, all 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 specialised 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 modelling, 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 organisational and technical safeguards.
Ensure reliability and performance
Leverage our experience training over 20,000 AI models. Gain 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 labelling?
Audio labelling tags voice and audio data to help machine learning models recognise speech, music, and effects, enabling applications such as voice assistants, speech-to-text, and sound event detection.
What is video labelling?
Video labelling 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 labelling?
Image labelling assigns meaningful tags or annotations to images, helping machine-learning models recognise objects, scenes or patterns for applications like autonomous vehicles, facial recognition and medical imaging.
What is text labelling?
Text labelling annotates data with tags to help machine learning models understand it, enabling tasks such as 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