Data annotation services - quicker, easier, smarter


Move faster and earn more with cutting-edge data labelling and annotation services
Data Annotation specialist

Discover the value of quality data annotation labelling

Quality data has been described as the new oil. There are rich insights and real value hidden in images, sound, video, and text. By taking advantage of AI data annotation services, you can tap into that value to achieve supercharged results.

Expert labeling & classification services for meaningful data


The annotation process requires precision and focus, but can deliver wide-ranging benefits
  • Data Labeling & Structuring

    Structure massive data sets for better organization, easier access & deeper insights

  • Improve Customer Experience

    Increase the efficiency of machine learning & AI services for enhanced end user experiences

  • Improve Output Accuracy

    Intuitive labelling of different data sets ensures higher accuracy and actionable data insights

  • Accelerate internal processes

    Develop smart tools to reduce time spent on admin and logistics tasks and increase productivity

carlens app interface

CarLens - recognize & collect your favourite cars with an augmented reality app

How Netguru used data annotation and machine learning to develop an app for detecting car models in real time

CarLens is a mobile application for iOS and Android that lets you recognize cars you find on the street.

It uses augmented reality and an AI-based solution to detect cars you see through the smartphone camera.

While creating CarLens, we created a custom data annotation software, leveraging smart mobile solutions for a simple app that allows immediate recognition.

Our experience with CarLens taught us that solving Machine Learning problems is not easy. The data annotation is a crucial issue behind a model’s accuracy. We’ve learned how important a quality data set is and how important is also the way you manage it. That's our key to success.
Jackowski Krzysztof

Krzysztof Jackowski

Mobile Deputy Engineering Manager, Netguru

The different types of data annotation


Multichannel services and platforms are now the norm, which means there are a variety of data sets you can use to benefit your business. As full-service Data Science Experts, we can analyze all major data types in one place.
  • Image

    Extra flexibility with binary and multiple choice photo sets

  • Video

    A challenging task made easy with simple, clean video data labeling

  • Sound

    Richer analysis options with sound file annotation based on type, author, genre, and more

  • Text

    Go beyond language learning projects with enhanced text annotation services

Schedule a free expert session

How we work?

  1. Choose the data you want to suit your business project - and collect as much as you need

  2. Define categories for suitable data classification, such as car models, music genres and more

  3. Create a rule book for each data class by writing up their characteristics to help with tricky cases in the future

  4. Time to get to work for our team of annotators. Manual annotation takes time, but it’s worth getting right!


Data Annotation at Netguru

Data Annotation at Netguru

Everything you need to know about data annotation

What is data annotation?

Data annotation is the name given to the process of labeling different types of data, like text, images, and sound.

Data labeling and annotation services are important in the development of AI and machine learning (ML) technologies because they enable ‘supervised learning’.

In supervised learning, data is preprocessed and labeled, which helps the machine to understand and recognize recurring patterns. This is useful for future cases where the algorithm is presented with un-annotated data.

In basic terms, data annotation and labeling helps improve the efficiency and accuracy of machine learning tools. It can be applied to multiple data formats and requires precision and expertise.

What are the types of data annotation?

Text annotation

The most common type of data annotation, text annotation services (or semantic annotation) help AI machine learning languages develop new concepts by using labeled text data as a reference.

Another form of text labeling is entity annotation, the process of labeling unstructured data with useful information that helps the machine learning program make sense of it.

Text annotation can be used to optimize chatbot services, category classification and search engine relevancy, among other things.

Audio annotation

Speech recognition tools need annotated audio data to efficiently process sound for applications like virtual assistants or chatbots (think of Siri or automated telephone menus that operate on voice).

Audio annotation can be applied to any sound or speech file metadata. Labels can be added to help define sound types (intonations, phrase types) or be based on author, genre, category etc.

Image Annotation

Image annotation services are growing in popularity with the rise of autonomous vehicles and the need for automated content monitoring (e.g. on social media sites).

As with text annotation, useful information is added to the image metadata to train machine learning algorithms to recognize features you want it to process automatically in the future.

Image annotation can be used to help block sensitive content or guide autonomous vehicles/devices in physical spaces.

Video annotation

Video annotation is similar to image annotation, but the process is more complicated because there are so many more images to look at.

Labels you might add to a video could include bounding boxes around a certain part of the video frame or full segmentation, in which each pixel is tracked and labeled with semantic meaning.

What are the advantages of data annotation?

Data annotation in machine learning is becoming more common because it offers benefits of efficiency, accuracy, and output.

With annotated data, AI and machine learning applications can recognize and understand previously obscure data, enabling for continual improvement and more accurate output for the end user.

An example is in search results, where relevant data annotation can enable search engines to produce the desired search for users with only a few characters. Data annotation for eCommerce can also produce more relevant product recommendations.

Better accuracy means better end user experience, which translates to the ability to attract and retain customers. Data annotation software in AI and machine learning helps to build seamless processes in communications, retail, research and manufacturing, to name a few.

This involves real-time issue tracking and feedback, as well as workflow processes like labeling consensus.

Workforce management

Even piece AI and machine learning software requires a human workforce to manage. Human involvement is needed to handle exceptions and quality assurance, so great AI data annotation solutions will also offer workforce management capabilities, such as task assignment and productivity analytics.

This can help in measuring the time your workforce spends completing tasks and levels of accuracy.

Integrated labeling services

Real life data annotation use cases

Data set management

Automated data annotation involves marking and categorizing data using machine learning. This can help improve efficiency of data management and deliver richer data insights to improve overall business models.

Data quality control

Machine learning data annotation ensures the data processed by AI programs is of a high quality.

Machine learning tools can only perform at a high level if the data they use is of premium quality. Data annotation tools help to manage the quality control (QC) and verification process.

With such a broad range of data annotation services and applications, a great data annotation platform should offer integrated labeling services so you can make use of the range of possibilities data annotation offers.

How is data annotation used in machine learning?

Semantic annotation

Semantic annotation is the process of labeling various concepts within text data, like people, objects, product names & types.

Machine learning tools use semantically annotated metadata to learn how to categorize concepts when new text is fed into the algorithm. As mentioned above, this can help to improve search engine relevance and chatbot features.

Text categorization

Text categorization (sometimes referred to as text classification) assigns categories or tags to text data and organizes it according to content type.

It is a fundamental task to help machine learning models with natural language processing (NLP) and can be used for topic labeling, spam detection or sentiment analysis.

Entity annotation

Entity annotation labels unstructured data with machine-readable information. It is used in several machine learning processes. One example is named entity recognition, which classifies named titles in test formats and can cover any predefined classification, such as person, organization or place.

Intent extraction

An offshoot of entity annotation is intent extraction, which uses sequential segmentation to help train models to recognize user intent. This enables the optimization of feedback features and chatbots.

For example, it can identify whether a user intends to return a product or unsubscribe from a service, giving you the ability to develop resolution models and respond to negative feedback with more context.

Phrase chunking

Phrase chunking is the process of tagging parts of speech or text with their relevant grammatical or linguistic meanings. An example is the classification of words or phrases into their language types, like verbs or nouns.

Phrase chunking is useful when you want your machine learning model to extract specific types of information, like locations or a person’s name.

Image & video annotation

Image annotation is the process of labeling or classifying an image using text and annotation tools to show the data features you want your model to recognize, adding metadata to a dataset.

Image annotation is used to recognize objects and boundaries within an image for greater understanding of the image. There are four main types of image annotation: classification (in the output can detect the presence of an object in the image), object detection (in which the output can detect the presence, location and number of objects in the image), semantic segmentation (in which the output can detect the presence and location of an object within certain segments of the image) and instance segmentation (in which the output can detect the presence, location, number, size, and shape of an object within the image).

World-class data labeling and annotation services

Our leading Data Science team is second to none when it comes to offering integrated data and labeling services. We constantly invest in R&D and new technologies to help optimize AI solutions.

Our partners about the cooperation with Netguru

  • My experience working with Netguru has been excellent. Outstanding software teams are resilient, and our developers at Netguru have certainly proven to be that. Our Netguru friends have become as close to team members as possible, and I am grateful for the care and excellence they have provided.
    Gerardo Bonilla

    Gerardo Bonilla

    Product Manager at Moonfare
  • Netguru has been the best agency we've worked with so far. Your team understands Kelle and is able to design new skills, features, and interactions within our model, with a great focus on speed to market.
    Adi Pavlovic Keller Williams

    Adi Pavlovic

    Director of Innovation at KW
  • Working with Netguru has been a fantastic experience. We received a lot of support in terms of thinking about how we track metrics, how we design this properly, and how we build the architecture. We are extremely grateful for making our platform what it is today.
    manon

    Manon Roux

    Founder at Countr

Netguru in numbers

  • 12+

    Years on the market

  • 800+

    People on Board

  • 900+

    Projects Delivered

  • 67

    Our Current NPS Score

Delivered by Netguru


We are actively boosting our international footprint across various industries such as banking, healthcare, real estate, e-commerce, travel, and more. We deliver products to such brands as solarisBank, IKEA, PAYBACK, DAMAC, Volkswagen, Babbel, Santander, Keller Williams, and Hive.
  • Self-care mobile app that lets users practice gratitude

    Shine

    $5M Granted in funding

  • Investment platform that enable to invest in private equity funds

    Moonfare

    $28M Granted in funding

  • Data-driven SME lending platform provider

    Finiata

    $20M Granted in funding

  • Lead generation tool that helps travelers to make bookings

    Tourlane

    $47M Granted in funding

Frequently asked questions


The most common queries about data annotation
What does a data annotator do?

Data annotators help to categorize content. They work with different data formats and materials like text, images, video, and sound.

By assessing the content and assigning tags, they can train machine learning models to recognize these features in the future, which can then be used to improve services like facial and speech recognition.

What are the data annotation techniques?

Data annotation begins, first of all, with collecting data. You choose the right data sets to suit your project and collect as much as you need.

Once you’ve got this data, you need to define it into classes/categories, e.g. car models or place names. Then you write up characteristics for each class and create a rule book, which data annotators can use when they run into obscure cases.

Then you can begin the process of labeling and tagging the data to feed it to a machine learning model, which will improve with richer data annotation.

What are the advantages of data annotation?

Data annotation is becoming a key aspect of application development for a few reasons: it helps to boost the accuracy of output for machine learning models. This translates to a more efficient and seamless experience for the end user.

Successful data annotation also helps to speed up internal processes and organize large datasets into structured and classified categories for easy access and recognition.

If you want to learn more about how data annotation can help your business, drop us a line.

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Grzegorz Mrukwa

Grzegorz Mrukwa

Machine Learning Manager

Drawing from years of commercial and academic experience, Grzegorz helps our clients discover how AI could empower their business.

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