Data Annotation, some explanations

You may have heard of data annotation, but do you know what it is? Netino by Webhelp is here to explain it to you!

| AI and Data Annotation

Data annotation is the process of labeling available data in various formats such as text, video, or images. Labeling refers to classifying, categorizing, organizing, and ordering this data.

There are 2 types of data annotation: manual and automatic.

Manual annotation is a task that requires extreme concentration and qualified personnel. It makes the process both time-consuming and expensive. That’s why there is automatic data labeling by AI. Once the annotation task is specified, a trained machine learning model can be applied to a set of unlabeled data. The AI will then be able to predict the appropriate labels for new and unseen data sets. This is called machine learning.

However, if the model fails to label correctly, humans can intervene, review and correct the incorrectly labeled data. The corrected and revised data can then be used to train the labeling model again.

At Netino by Webhelp, we believe that AI and humans are complementary.

| Human in the Loop – the importance of humans in AI projects

The most critical element of any AI project is the one that is rarely taken into account: humans.

AI will always need humans because it imitates human intelligence. The amount of manual annotation will never be zero, as models are constantly improving.

The effectiveness of AI depends on humans, who are responsible for extracting large amounts of data and training AI algorithms to perform the intended tasks. More and more companies are turning to AI and machine learning to update their customer experience and back-office operations. That’s why human involvement in data capture and system maintenance over time is extremely important.

| Data Annotation at Netino by Webhelp


Our Data Annotation production centers based in the EU (Bucharest – RO) and outside the EU (Antananarivo – MG) guarantee short onboarding and training processes, supervised by expert professionals.


Thanks to a logic of pooling and our pool of backup agents, we adapt the team size to your volume fluctuations and can launch a project in a few days.


A Quality Management team is responsible for monitoring the relevance of annotations compared to the guidelines set during the entire project lifecycle.


We correct, improve, and enrich your existing AI models through verification processes by human teams after implementation.

| Examples and case studies

Example 1 of data annotation

In the sports industry, we provide human assistance to train a sports data analysis solution through machine learning: recording and correcting events during a football match (passes, shots, tackles, etc.).

Example 2 of data annotation

In the energy industry, our client was looking for a partner capable of improving the tracking of their gas cylinders during their movement outside their warehouses.

If you want to learn more about data annotation, do not hesitate to contact us to make an appointment with one of our experts on the subject!

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"Data Annotation, some explanations"