Create precise, high-quality data
Leverage advanced tools, human expertise, and AI combined with on-demand services to produce highly accurate datasets tailored for robotics applications.
Leverage advanced tools, human expertise, and AI combined with on-demand services to produce highly accurate datasets tailored for robotics applications.
Ensure that your data, whether for tasks like dexterous manipulation, vision-based navigation, or sim-to-real transfer, is perfectly suited to your project’s requirements with Databrewery’s tailored solutions.
Benefit from our network of highly skilled operators who bring real-world insights into teleoperation and evaluate complex AI models to ensure they meet your performance goals.
Work directly with internal and external labelers, receiving real-time feedback on labels and quality through the Databrewery platform
AI is leading the charge in robotics innovation, making it possible for intelligent systems to interact with the physical world. Whether it's teleoperation or working towards AGI-level capabilities, training AI with a diverse and high-quality dataset is essential to mastering tasks like dexterous manipulation, navigating intricate environments, or overcoming the gap between simulation and real-world application.
Developing AI models for robotics, especially in areas like teleoperation and real-world adaptability, comes with its own set of challenges. The variety of physical limitations, hardware configurations, and ever-changing environments require highly skilled operators who can capture meaningful data. To succeed, you need a robust platform, continuous expert input, and ongoing refinement.
Databrewery supports AI teams in building top-tier training datasets. Our global network of expert operators offers both on-site and remote teleoperation, ensuring continuous engagement. With a phased approach that starts with on-site work and expands remotely while fine-tuning the process, Databrewery fast-tracks your journey toward robotics innovation.
Train robots for precise manipulation by capturing details like object poses, grasp success or failure, and contact points, ensuring they can perform tasks that require fine motor skills.
Enable robots to navigate complex environments by annotating key elements such as semantic segmentation, depth, and trajectory paths, boosting their autonomous movement capabilities.
Improve a model’s ability to transition from simulated environments to the real world by identifying and addressing differences, assessing movement success, and pinpointing edge cases to enhance adaptability.
Enhance remote control and responsiveness by evaluating operator inputs, ensuring real-time adjustments improve robotic control for more accurate performance.
Enable robots to process live speech and contextual audio, allowing them to understand and participate in ongoing conversations, making them more interactive and responsive.
Convert live audio streams into instant translations in any language, making communication across language barriers seamless for robotic tasks.