Keylabs.ai introduces new tools for data annotation that reflect how the enterprise labeling process is evolving

Keylabs.ai launches version 2.5 with advanced video annotation, project forecasting, workflow analytics, and quality-control tools for enterprise data labeling.

NEW YORK, NY, UNITED STATES, July 16, 2026 /EINPresswire.com/ -- Keylabs.ai, an annotation platform developed by Keymakr, introduces new capabilities designed to support end-to-end data annotation processes - including team management tools that provide unprecedented visibility across the entire workflow.

The 2.5 release responds to the growing scale and complexity of computer vision projects, giving teams faster ways to work with video, plan resources, monitor quality, and keep large datasets organized.

Advanced video annotation
Keylabs.ai now decodes videos directly in the browser, helping teams process files faster and reducing the storage required for large video projects. This is a difference-maker for large operations that often use standardized hardware, which is rarely powerful enough to work with video efficiently.

The platform also introduces expanded playback controls, including frame-by-frame navigation, synchronized timeline scrubbing, and speed adjustments.

These tools make it easier to review long recordings, inspect short actions, and work with fast-moving scenes.

Temporal labeling, in particular, is gaining popularity in the industry. Annotators can select specific frame ranges on the timeline and assign attributes to actions, events, or states occurring during those intervals. Labeled segments are displayed as colored blocks, allowing users to quickly move between scenes and edit their attributes to later train models.

Better visibility across the entire pipeline
The new forecast report estimates project completion dates based on the number of objects in a dataset and the established labeling speed. Managers can adjust the number of annotators, verifiers, and working hours to model different production scenarios and see how changes in resources affect expected delivery timelines. The report also includes a burn-down chart and an indicator that reflects changes in task complexity.

New team analytics provide a more detailed view of the annotation and verification performance across different labeling stages. Managers can compare current speed with established targets, review individual performance trends, and analyze productivity by object class.

Both reports can be easily shared, making it easier to communicate progress with stakeholders.

Quality-control capabilities have also been expanded with frame-view tracking for verification stages. Verifiers can see which parts of a file they have already reviewed, while managers can monitor how many frames each participant has seen. Depending on project requirements, the platform can allow users to continue without checking every frame, display a warning, or block access to the next file until all frames have been viewed.

Michael Seldin, CTO of Keylabs.ai and Keymakr, commented:
“As annotation projects become more complex, the challenge is no longer limited to drawing accurate labels. Teams need to understand what is happening throughout the workflow and identify problems before they affect delivery. We designed these capabilities to make the entire process easier to manage. This gives teams greater confidence in both the quality of the data and the progress of the project .”

Teams interested in exploring the latest capabilities of Keylabs.ai can request a demo.

About Keylabs.ai:
Keylabs.ai is an enterprise-grade labeling and data management platform developed by Keymakr, a global provider of high-quality AI training data solutions. Together, Keylabs and Keymakr support organizations developing computer vision, multimodal AI, physical AI, robotics, and LLM-based systems by delivering high-quality datasets through a human-in-the-loop approach and proprietary annotation technologies.
Built by annotation experts, Keylabs.ai streamlines complex data operations and supports all major annotation types. The platform combines automation, model-assisted labeling, customizable QA workflows, and scalable operations management to help teams process large and complex datasets efficiently and accurately.

Olena Butenko
Keymakr
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