So many ideas, but not enough labeled data

Does this sound familiar to you?

From our own experience as Data Scientists, we know the pains of gathering high-quality datasets. We incorporated our knowledge and the knowledge of many interview partners into our product that we believe will make the life of a Data Scientist just that much easier.

We believe that Labeling should be ...


Debugging your model is extremely difficult if you don’t know where poor model performance stems from. Labels come with insightful metadata, allowing you to understand label quality issues.


Labeling for a day is interesting, as you get to know the data. Labeling for weeks is just exhausting. We make labeling both convenient and explorative, so that you find the data you need to see.


We faced that requirements can change in a Machine Learning project. This also accounts for labeling. That is why we built our platform to treat labeling as an evolving procedure.


Every person that labels data will develop some internal rules to classify the given data point. By expressing these rules as a labeling functions, you’re essentially building a labeling documentation.


Labeling is a bottleneck in many use cases. With us, this is no longer the case. We provide large-scale progammatic labels, which help you implement highly beneficial use cases.


Labeling tasks can be very time costly which delays the deployment of machine learning solutions. We aim to assist you in speeding up the data labeling process so you can build models in days, not weeks.

That's what we offer. Both on Cloud and On-Prem.

You and your team know your data best. That is why we built a lightweight platform that has much to offer but is still highly customizable to your needs. You can bring custom embeddings, enrich unlabeled data with attributes of your choice and then use them to programmatically label your dataset in a modern fashion. Every component of onetask was built with the Data Scientist as a user in mind.

Become a labeling pioneer now!

Join our Closed Beta