Awign

When we started building data pipelines for Physical AI a few months ago, one thing became clear - it’s easy to get started, but extremely hard to scale. At scale, everything breaks. You need massive diversity - a large number of workers across thousands of commercial and household environments, without over indexing on any one setup. You need the right multimodal hardware for accurate 3D hand pose capture. You need strong models and pipelines for annotation. But most importantly, this is not a marketplace business. You can’t just match workers and walk away. You have to own quality and throughput end to end, which means operations becomes the core. Without that, quality starts dropping the moment you go beyond even ~500 hours/day. And when you introduce high-fidelity or custom hardware, it becomes even harder - coordination, training, and even something as basic as data transfer at scale becomes non-trivial. We SOLVED all of this! Over the last few months, we’ve built through these constraints end to end and are now operating at 10K+ hours/day, fully bespoke and exclusive for each client. We’re on track to become one of the largest data labs for Physical AI globally - solving for raw data creation, data creation using high fidelity custom hardware, 3D hand pose capture, and semantic annotation, while bringing together best in class hardware and model partners to augment our core strength of deploying and managing a large workforce end-to-end. One thing that has guided us over the last 10 years - and continues to - is simple: Focus on creating value for customers. Obsess over improving it every single day. Let’s connect with Gio and Annanya Sarthak(sk) .

When we started building data pipelines for Physical AI a few months ago, one thing became clear - it’s easy to get started, but extremely hard to scale. At scale, everything breaks. You need massive diversity - a large number of workers across thousands of commercial and household environments, without over indexing on any one setup. You need the right multimodal hardware for accurate 3D hand pose capture. You need strong models and pipelines for annotation. But most importantly, this is not a marketplace business. You can’t just match workers and walk away. You have to own quality and throughput end to end, which means operations becomes the core. Without that, quality starts dropping the moment you go beyond even ~500 hours/day. And when you introduce high-fidelity or custom hardware, it becomes even harder - coordination, training, and even something as basic as data transfer at scale becomes non-trivial. We SOLVED all of this! Over the last few months, we’ve built through these constraints end to end and are now operating at 10K+ hours/day, fully bespoke and exclusive for each client. We’re on track to become one of the largest data labs for Physical AI globally - solving for raw data creation, data creation using high fidelity custom hardware, 3D hand pose capture, and semantic annotation, while bringing together best in class hardware and model partners to augment our core strength of deploying and managing a large workforce end-to-end. One thing that has guided us over the last 10 years - and continues to - is simple: Focus on creating value for customers. Obsess over improving it every single day. Book a call with Gio : https://lnkd.in/gJVvbv8f Gurpreet ,Praveen, Anirudh ,Kshitij Gio Gujarati ,Kanishka ,Anchal Kumar ,Sudeep, Prashant, Shreyas, Runam, Achin, Arvind, Sahil, Harshil, Prince , Santhuan , Alphones, Chetan, Himanshu, Pratik, Nagarjuna, Jaiprakash meena , Ranjit, Vivek . #HumanoidRobotics #PhysicalAI #EmbodiedAI #RobotLearning #Robotics #AI #MultimodalAI #EgocentricData #FirstPersonData #EgocentricVideo #RobotTraining #RobotVision #HumanRobotInteraction #TactileGripper #Gripper #UMI #DataAnnotation #DataCollection #Awign

When we started building data pipelines for Physical AI a few months ago, one thing became clear - it’s easy to get started, but extremely hard to scale. At scale, everything breaks. You need massive diversity - a large number of workers across thousands of commercial and household environments, without over indexing on any one setup. You need the right multimodal hardware for accurate 3D hand pose capture. You need strong models and pipelines for annotation. But most importantly, this is not a marketplace business. You can’t just match workers and walk away. You have to own quality and throughput end to end, which means operations becomes the core. Without that, quality starts dropping the moment you go beyond even ~500 hours/day. And when you introduce high-fidelity or custom hardware, it becomes even harder - coordination, training, and even something as basic as data transfer at scale becomes non-trivial. We SOLVED all of this! Over the last few months, we’ve built through these constraints end to end and are now operating at 10K+ hours/day, fully bespoke and exclusive for each client. We’re on track to become one of the largest data labs for Physical AI globally - solving for raw data creation, data creation using high fidelity custom hardware, 3D hand pose capture, and semantic annotation, while bringing together best in class hardware and model partners to augment our core strength of deploying and managing a large workforce end-to-end. One thing that has guided us over the last 10 years - and continues to - is simple: Focus on creating value for customers. Obsess over improving it every single day.

When we started building data pipelines for Physical AI a few months ago, one thing became clear - it’s easy to get started, but extremely hard to scale. At scale, everything breaks. You need massive diversity - a large number of workers across thousands of commercial and household environments, without over indexing on any one setup. You need the right multimodal hardware for accurate 3D hand pose capture. You need strong models and pipelines for annotation. But most importantly, this is not a marketplace business. You can’t just match workers and walk away. You have to own quality and throughput end to end, which means operations becomes the core. Without that, quality starts dropping the moment you go beyond even ~500 hours/day. And when you introduce high-fidelity or custom hardware, it becomes even harder - coordination, training, and even something as basic as data transfer at scale becomes non-trivial. We SOLVED all of this! Over the last few months, we’ve built through these constraints end to end and are now operating at 10K+ hours/day, fully bespoke and exclusive for each client. We’re on track to become one of the largest data labs for Physical AI globally - solving for raw data creation, data creation using high fidelity custom hardware, 3D hand pose capture, and semantic annotation, while bringing together best in class hardware and model partners to augment our core strength of deploying and managing a large workforce end-to-end. One thing that has guided us over the last 10 years - and continues to - is simple: Focus on creating value for customers. Obsess over improving it every single day. Let’s connect with Gio and Sarthak(sk) .

From cutting vegetables to folding clothes - Awign helps you capture and label real-world hand-object interactions for robot training. Our 500K+ Creators & Annotators ensure accuracy above 98%, delivering multimodal, QA’d data at scale. Accelerate your humanoid or imitation learning pipeline today

From cutting vegetables to folding clothes — Awign helps you capture and label real-world hand-object interactions for robot training. Our 500K+ Creators Annotators ensure accuracy above 98%, delivering multimodal, QA’d data at scale. Accelerate your humanoid or imitation learning pipeline today.