Data labeling software that makes your AI smart
— Data labeling tool for various data types
— Automatically label up-to 95% of your dataset using Machine Learning and Active Learning
— Manage training data in one place. Control quality, and privacy


The results are automatically and manually verified. We make sure you get only the high-quality labels.


Configure for different use-cases and different data types. It takes 10 minutes to set up and run.


Labeling big datasets can become tedious and expensive. Heartex can automatically label similar data samples.

The only thing that comes before Heartex is getting the data

Heartex is an annotation management system with configurable UI interface for your specific needs. Start using it and minimize the amount of time your entire team spends on preparing and analyzing datasets for machine learning.

  • Nested Labeling
  • Data manager with search
  • Statistics and performance metrics
  • Organizations and teams
  • REST API and Python SDK
  • Machine Learning and Active Learning
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Annotations for different types of data


Use Heartex for analysing photos, CCTV footage, ecommerce, and other visual information.


Label audio files to filter out ads, transcribe speech, identify music genres and more.


Parse human input, moderate messages, train chatbots for context recognition, build analytics over textual information

Time series

Label time series and train different models to work with sensor signals

Custom datasets

Set up models to work with any type of datasets you have — let us know of your objectives and we'll help you start either with our AI models or connect your own

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What else you get

Auto pre-labeling

We make suggestions based on what has already been processed before. You only need to approve or correct the suggestions while labeling your own datasets.

Native Active Learning

Heartex uses cluster annotation and active learning to train your model on diverse examples first. You can fine-tune the model by labeling similar objects later to optimize the model’s quality score.

Transparent process

If your data set has missing variables, or your collaborator labeling results differ too much, you'll see that early enough. Make adjustments and save time and money on labeling everything — just monitor the model's quality score at the early stages of training.

Easy to get started

  1. Sign up for Heartex and add your dataset objects
  2. Connect your AI model through Heartex API or choose one of our pre-trained models
  3. Label a small batch of objects, yourself or collaboratively with your team members
  4. See your model’s quality score improve as you go through the dataset
  5. Start using your model as soon as you want. If necessary, you can keep improving its quality score as you go.
Start annotating your data
You're building something great, let us help you do it faster and remove the boring parts
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