Improve your data labeling workflow and management

Work more efficiently, and make data labeling a breeze. makes it easy to streamline your process, and provides you with powerful tools for managing and optimizing your workflow.

Build a better data labeling process


Multi-sensor - point cloudMulti-sensor - image labeled

Label 3D first, then 2D

The optimal labeling workflow is counter-intuitive

While it may seem more logical to label your 2D objects first, at, we advise you to start with 3D labels first.

2D bounding boxes are fast to annotate, but you have to draw them in every single frame. A single traffic sign can mean 300 2D bounding boxes if you have three different cameras — a labeling task that can cost hours of your time.

When you start with 3D though, you can leverage the power of scale. The same traffic sign only requires a single 3D cuboid, making it 100 times more efficient than the 2D bounding boxes. You can then project that cuboid onto the 2D data and automatically fill in the labels.

Create clear labeling instructions

Stop data labeling mistakes before they slow down your workflow. When you create clear, comprehensive annotation instructions, you can easily increase your team’s productivity and ensure consistency across labelers.

To help, we’ve created a free guide to creating labeling guidelines. In it, you’ll learn:

Why a clear process matters
What elements you need for a better process
Tips and tricks on how to improve your labeling guidelines

Get your copy today for free. Just click the button below to download your free guide.

Multi-sensor data labeling guide - front cover

Data labeling roles and permissions

Define data labeling roles

Simplify your collaboration with personalized accounts for each team member.

The shared organization account lets you seamlessly collaborate on datasets, while also defining clear roles for everyone on the project.

Optimize the labeling queue

Maintain control of the labeling review process with a customized labeling queue.

In, you have the flexibility to assign specific labelers or reviewers to individual samples, so you can prioritize samples or delegate them based on team expertise. Then, assign priorities to samples, so your team can work most efficiently.

data labeling managing the queue labelers and reviewers

Quickly resolve issues

Effective communication is key to resolving labeling mistakes.’s issue management system lets you quickly create issues to address concerns or uncertainties in the labeling process.

You can attach images or screenshots to issues to highlight specific labeling mistakes or give clear guidance on corrections.

Export data easily

Use the API or SDK to export your data in the required format, and save time converting data from one platform to another.

  • Capture snapshots of your labeled data at specific points in time.
  • Access finished labels and generate download links for further analysis or integration into your machine-learning pipelines.
  • Export options to popular frameworks like PyTorch, TensorFlow, and Hugging Face 🤗, ensuring the smooth integration between your labeled data and model training.

insights in labeling process: username, edited, total label time and average label time. split per reviewer or labeler

Spot bottlenecks in your workflows gives you detailed data about what’s happening in your workflows, so you can understand how to make them more efficient.

Get insight into how fast each labeler works, or simply get an overview of the entire process. You’ll have data on a per-user level, but can also see the label- and review-related metrics for the entire workflow. You can even get custom plans that deliver more data directly to your dashboard.

Built by machine learning engineers, for machine learning engineers

We built because we saw first-hand how painstaking labeling could be.

Autonomous robotics in a warehouse

Simplify multi-sensor labeling

Say hello to faster labeling, better quality data, and more time for your engineering team.
You’ve got access to a 14-day free trial to test it for yourself.