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Our workforce creates all kind of training data for your Machine Learning & AI projects

Spend more time building Data Pipelines and Machine Learning models

And less time working on creating Training Data and Data sets. Let us take care of all the data labeling, annotation and image classification work.

We perform image collection, annotation, labeling and validation on all of your image data (e.g. invoices, pictures, medical images, receipts and so on).

Our image annotations services range from 2D and 3D bounding boxes to polygons and image categorization. The training data is used as input in your Computer Vision models.

Our workforce builds datasets based on text for all your Natural Language Processing (NLP) projects, whether it is for training a chatbot or for an OCR solution. 

We provide Text Classification, Semantic Analysis, Intent Classing, Entity Linking, Linguistic Annotation, Content Moderation and Text Summarization.

At Zentosa, we provide video labeling for projects in Automotive (self-driving vehicles, Driver Safety), Healthcare, Technology, Sports and Agriculture and more. 

We do Video Collection, Video Classification, Video Data Analysis; Pose Detection, Action Detection, Sensor Fusion, Sport Analytics.

Teach your Machine Learning models to understand video inputs, detect objects, and make decisions based on our datasets.

We provide audio labeling that enable better and more intelligent Customer Services.

Let us create training data and data sets based on your historical Support calls. The data can be used as input in smart Chatbots that can speak and listen to voices.  Your Customer Service can have intelligent 24/7 support.

Zentosa provides annotation from your sensor data. The training data that we build based on sensor data can be used to make decisions from your AI-models.

We are experts at cleaning data and strengthening it for training and data sets.

Zentosa performs actions such as finding and dealing with missing values, eliminating duplicates, correcting inaccuracies, merging data sets,  normalizing data, data type conversions and more. Cleaning data is important to ensure that the model that you train gets proper data that is usable and enables your business goals.

Synthetic data is data generated by a computer for different purposes. It is being increasingly more common to use instead of costly data masking or anonymization tools. The synthetic data is fake data that represents and follows the same conditions as real data. 

At Zentosa, we understand that data can be sensitive and difficult to transfer. Because of that we are one of the first companies to create synthetic data for our customers. For example we can generate names, home addresses, social security numbers, credit card numbers, IP-adresses, e-mail addresses, occupation and much more. The data can be represented in the way that you prefer (such as invoices, application forms, claims forms, etc.).

The synthetic data that we generate can be used as your initial data set for Machine Learning and AI projects, we then benchmark it with your actual real-data.

How does it work?

  • 1. Define your requirements

    Meet with one of our experts and let us help to specify your needs and project requirements. We identify the business problems that you want solved. Zentosa analyzes the relevant data and gives a propsal for the next step.

  • 2. Pilot

    We do a small data labeling test to ensure that we can deliver high-quality data before scaling up to production.

  • 3. Project set up

    Our workforce of human data experts get to work cleaning, annotating, labeling or classifying your data according to your specifications.

  • 4. Continuous improvements

    Zentosa continuously works to improve the results over time on your data leading to the highest quality training data, used as input in your Machine Learning models.

  • 5. Delivery

    Our team validates, packages and formats the training data to fit your requirements before sending it to you for approval. The result is a Quality-Assured dataset that can be used straight away in your Machine Learning models.


Most frequent questions and answers

Based out of Sweden and Scandinavia, we understand that we need to know our customers language by heart. Because of this we are dedicated to matching our local workforce with your needs and requirements. For example, if you have data in Finish – then we will provide our workforce from Finland or with Finnish-speakers.

Our competitors mainly work with English-speaking staff and are not multilingual. Our aim is to focus on projects where local language knowledge is needed – and using Google Translate is not good enough in our experience.

Furthermore, our aim is to provide work for skilled students and others who want an interesting job within the growing AI environment. 

We let you choose the pricing model that suits your needs. You can either pay based on volume or per hour. Your choice!

We have a strict Quality Assurance (QA) process that is mandatory in all of our projects.

Quality assurance plays a critical role in the accuracy of Machine Learning models and should not be overlooked. It is vital to monitor the quality of training data in order to ensure successful AI Projects.

Our successful way-of-working uses pre-answered rows of your data to qualify high-performing contributors, remove low-performing ones, and continuously training contributors to improve their understanding of your data. This way you can trust that our workforce only has high-performing contributors working on your data.

Furthermore, we have multiple contributors from our team label each row of your data. This way, we can ensure agreement is reached and any individual bias is removed.​

Finally, Zentosa ensures that we have an audit trail of each contributor in our workforce. We use the audit trail to evaluate performance.