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From one-pagers to how-to guides to webinars, go behind the scenes to discover how Uber AI Solutions delivers high-quality data labelling, product testing and localisation for Generative AI applications, AI/ML, LLMs, ADAS, mapping, NLP, AR/VR, computer vision, robotics and so much more.

Introducing Uber AI Solutions

With over 9 years of expertise in managing large-scale data labelling operations, we offer 30+ advanced capabilities, including image and video annotation, text labelling, 3D point cloud processing, semantic segmentation, intent tagging, sentiment detection, document transcription, synthetic data generation, object tracking and LiDAR annotation.

Our multilingual support spans 100+ languages, covering European, Asian, Middle Eastern and Latin American dialects, ensuring comprehensive AI model training for diverse global applications.

Our solutions include:

Data annotation and labelling: Expert, precise annotation services for text, audio, images, video and many more technologies
Product testing: Efficient product testing with flexible SLAs, diverse frameworks and 3,000+ test devices, all streamlined for an accelerated release cycle
Language and localisation: World-class user experience for everyone, everywhere

Human-in-the-Loop Validation for Physical AI

In the race to deploy robots, drones, and autonomous vehicles, speed matters — but safety and trust matter more. A single mis-labeled object can lead to costly failures or safety incidents. That’s why leading AI companies are turning to Human-in-the-Loop (HITL) validation to ensure their models behave reliably in unstructured environments.

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Data labelling for generative AI: A comprehensive guide

This guide will explore the significance of data labelling in generative AI, the types of data that need to be labelled, and how accurate labelling can enhance your AI models' creative capabilities. Whether you're generating realistic images, text or code with the AI you build, understanding how to label data effectively is key to producing high-quality outputs.

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How Scalable 3D Sensor Fusion Labeling Powers the Next Wave of Physical AI

Every robot that navigates a factory floor, every autonomous vehicle that detects a pedestrian, and every drone that lands on a moving target relies on one thing: high-quality labeled data. Yet as physical AI becomes more complex, so does its data pipeline. Robotics and autonomous systems must make sense of inputs from cameras, lidars, radars, IMUs and GPS sensors — often in real time. This is where 3D sensor fusion labeling becomes mission-critical.

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Explore our resource topics

Whether you're an AI/ML enthusiast or you lead a team focused on data labelling, product testing or localisation, or you're interested in partnering with us – you'll find the right resource for you.