The launch of our private, custom-deployed instance of CVAT.ai — a powerful, open-source annotation tool designed for computer vision teams. This marks a significant step in streamlining our data labeling workflows for machine learning and AI model development.

  • Secure and Role-Restricted. Built with security at the forefront, our deployment restricts access to only authorized project personnel. Whether you’re an annotator, reviewer, or admin, the system is tailored to your role and responsibilities.
  • Project-Focused Precision. Each annotation task is linked to a clearly defined project structure, allowing for organized collaboration, consistent labeling schemas, and targeted performance metrics.
  • Custom Tooling for High-Quality Results. From polygons to segmentation masks, our instance supports all critical annotation types. We’ve also integrated project-specific labeling schemas to align with our training data requirements.
  •  Productivity Meets Oversight. Annotator activity is tracked in real-time, and all annotations are versioned and auditable. This means better oversight, fewer errors, and higher-quality data to feed into our ML pipelines. 
  • Built for Scale and Integration. With full API access and webhook support, our CVAT instance connects smoothly with the rest of our AI infrastructure, ensuring annotation tasks can be automated and scaled as needed.

This launch reflects our continued investment in secure, scalable, and smart infrastructure to support advanced AI development. By combining CVAT’s flexibility with our team’s expertise, we’re pushing the boundaries of what’s possible in machine learning workflows.