Introducing Customer: Voxel

Voxel is a global leader in workplace safety, empowering worksites by providing them with the data they need to protect workers and gain insight into workplace activities. Their mission is to protect the people who power our world.

We spoke with Anurag Kanungo, the co-founder and CTO, about why he decided to transition to Encord to manage their machine learning pipeline and computer vision projects.

Problem: Operational Challenges in Data Accessibility and Model Scalability

As Voxel grew, they encountered several challenges that hampered their ability to deliver on their mission effectively.

The initial approach to data gathering and analysis wasn't sufficient for scale, leading to difficulties in finding relevant data and a lack of dataset diversity. The frequent changes in work environments, such as uniform updates, posed challenges in accurately updating models with new, unseen data. Also, addressing model edge cases and efficiently scaling the data labeling and analysis process became a prominent issue.

Initially, Voxel trained pipelines using open-sourced tools like CVAT for object detection in videos. While sufficient on a small scale, as Voxel grew and required more complexity, the limitations of these tools became evident. Among others, they faced challenges with the user interface, backend data management, interpolation issues, and label exports. Despite being a good starting point, these tools proved inadequate for scaling operations effectively.

“…as we started growing and adding more customers and more people using the tool there were certainly a bunch of challenges that came in, like CVAT kept running out of disk, so we had to start doing maintenance ourselves. We had to start editing the code and diverging from the main branch, which we really didn’t want to do…because we wanted to focus on our product.” - Anurag Kanungo

As Voxel scaled, they sought a more robust solution that had critical features such as video support and image classification.

Solution: Transitioning to Encord for Scalable and Efficient Video Analysis

The decision to transition to Encord marked a significant turning point for Voxel. Encord's video-first approach addressed their need for robust video support, while its innovative features, such as image group classification, stood out. Moreover, Encord's exceptional support and technical design resonated with Voxel's needs, offering a seamless and efficient solution that aligned perfectly with their vision for enhancing workplace safety.

"We went through a bunch of vendors and one of the things that stood out about Encord was the video first support, which other vendors do not have. Specifically understanding how the video works behind the scenes: the encoding, the frame indexes and square pixel ratios."- Anurag Kanungo

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Results: Impact of Encord on Voxel’s Operations

One of the key requirements for Voxel was the ability to integrate their existing data pipelines into a new solution, which Encord was able to provide seamlessly. This enabled their team to continue to focus on their end solution without being preoccupied with the handover. 

Voxel were impressed by the robustness of the platform, enabling them to utilise many of the advanced features enabling them to address the safety issues and ergonomic concerns more effectively, aligning with their overarching mission to reduce workplace risks and ensure a safer environment for all workers

Overall, the adoption of Encord has significantly aided Voxel's approach to workplace safety and efficiency. The platform's integration and its capabilities have empowered Voxel to address safety concerns and optimize operations effectively. With Encord's ongoing support, Voxel is well-equipped to navigate future challenges and drive innovation in workplace safety, setting new standards for operational excellence.