Data is diverse, and so has to be a data annotation tool. Heartex is an annotations 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.
Use Heartex for analysing photos, CCTV footage, ecommerce, and other visual information.
Label audio files to filter out ads, transcribe audiobooks, identify music genres and more.
Parse human input, moderate messages, train chatbots for context recognition
Label time series data and train your or Heartex models to work with sensor signals.
Set up Heartex 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.
Integrate your AI model with Heartex through our API and see how its quality score grows as you label the dataset. Such model integration approach allows you to see results faster — in days, not months — and process only as much data as necessary.
Our client maIns saved $1500 by using Heartex pre-trained model to evaluate insurance cases. Instead of spending hours on making decisions manually, they have their customers take pictures of car crash damage on-site and the AI does the rest in a matter of seconds.
Human+, a smart devices company, provides workforce analytics to construction companies. It uses Heartex to label data gathered from devices worn by builders. Insights lead to real work processes optimisation and cutting costs.
We're working on a project that uses Heartex AI models to process information about commercially caught fish and saves time & money on identifying types of fish on images taken at the site of catch.
We make suggestions based on what has already been processed before. You only need to approve or correct the suggestions when labeling your own datasets.
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.
If a dataset is missing variables, or your collaborators 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.