Data Annotation for AI Solutions

Manage and enrich datasets, optimize and unleash the power of AI models.

Specialized in data annotation and labeling, our company provides comprehensive expertise in management and optimization of datasets for machine learning and artificial intelligence. We handle the preparation of datasets, ensuring their cleaning, normalization, and enrichment to maximize their efficiency in AI solutions.

We deploy dedicated and skilled teams, utilizing manual and automated methods tailored to the specific needs of each project. We collaborate closely with development teams to integrate and refine the detection and prediction capabilities of AI models.

Through a personalized approach and continuous improvement of our processes, we guarantee the quality of the processed data and ensure optimal alignment with the strategic objectives of our clients to help them design increasingly effective solutions.

A comprehensive Data Processing Service for
AI Professionals

Accurate and comprehensive annotation and labeling of your data sets is fundamental to the proper functioning of AI models.

Whether you have your own tools on a cloud platform or prefer to work with a third-party application, our team is specifically trained to work with your technology, in accordance with your standards, following your processes and incorporating specific requirements of your industry.

With Qwanteos teams, you can optimize your costs and focus on the potential of your business. We are by your side to help you achieve your goals.


Tracking movements
and collecting game
events data for
Sports Industry


Identifying, annotating, and
labeling large datasets
to optimize detection
performances of AI models

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Testing, monitoring, and
optimizing the complete
set of dialogue rules
for LLMs industry

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Deploying and managing
dedicated teams of data
processing and training
experts for AI solutions

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We provide solutions for a wide variety of industries

Our teams are specially trained to meet the specific expectations of different industries. For your machine learning needs, we can produce your datasets faster, more accurately and at optimized costs.

AI is used in almost all areas of sports from predicting game plan to coaching the players and helping to win the game.
Examples of AI labeling and tagging tasks applied to sports:
  • Sports event classification: classifying different sporting events into categories like basketball, football, ice hockey...
  • Object detection in sports images and videos: identifying players, ball, goals…
  • Player pose estimation: estimating the posture of a player in a sports video, allowing for tracking of their movements and actions on the field.
  • Event recognition and classification: identifying different types of actions performed by players in a game (shooting, dribbling, tackling, sprinting…).
  • Named Entity Recognition (NER): extracting player and team names, locations, and other relevant information.
  • Player emotion analysis: analyzing the emotions of players in a sports video (frustration, excitement, anger, tiredness….).
  • Sentiment analysis: determining the public opinion towards a team, player, or game.
AI is increasingly being applied to environmental projects to support data analysis and decision making. AI annotation tasks involve the use of machine learning algorithms to automatically categorize, classify, and label large amounts of environmental data, such as satellite imagery, remote sensing data, and field observations.

Examples of AI labeling and tagging tasks applied to environmental projects :

  • Land use and land cover classification: mapping and monitoring changes in land use and vegetation patterns.
  • Water quality analysis: analyzing satellite imagery and remote sensing data to monitor water quality and identify potential waste pollution.
  • Climate change impact assessment: analyzing data on temperature, precipitation, and other climate variables to assess the impact of climate change on ecosystems and human communities.
  • Object detection and tracking: detecting and monitoring wildlife or flora, such as endangered species.
  • Forest inventory and monitoring: monitoring and quantifying changes in forest cover and biomass.
AI and machine learning are contributing in multiple healthcare areas such as disease prevention and control, medical research or diagnosis, patient treatment, and management. AI annotation tasks include identifying specific medical entities such as diseases, symptoms, and treatments, as well as extracting structured information from unstructured data sources such as electronic medical records and clinical notes. The goal of AI labeling and tagging in healthcare is to improve the efficiency, accuracy, and speed of data processing and analysis.

Examples of AI labeling and tagging tasks applied to health:

  • Named Entity Recognition (NER) in medical texts: identifying specific medical entities such as diseases, symptoms, treatments, and anatomical parts from clinical notes and electronic medical records.
  • Medical concept extraction: extracting structured information from unstructured medical data, such as demographic information, diagnoses, and treatment plans.
  • Sentiment analysis in patient feedback: analyzing patient feedback and classifying it into positive, negative, or neutral sentiment to understand patient experience.
  • Image labeling in medical imaging: labeling and categorizing medical images (X-rays, CT scans, MRI scans…) to assist radiologists and other healthcare professionals in their diagnosis and treatment.
  • Coding and classification of medical billing codes: categorizing and coding medical procedures, treatments, and diagnoses into standardized billing codes to improve the accuracy and efficiency of medical billing processes.
AI is commonly used in the media and entertainment industry to categorize and organize large amounts of data such as images, videos, audio files, written content.AI labeling and tagging tasks help media and entertainment companies to improve their content discovery and search capabilities, as well as to better understand their audience preferences and behavior.

Examples of AI labeling and tagging tasks applied to media and entertainment:

  • Image tagging: assigning labels to images based on their content such as people, objects, scenes, emotions…
  • Video classification: categorizing videos into different genres, themes, or moods based on visual and audio features.
  • Audio labeling: assigning labels to audio files based on their content such as music, speech, sound effects...
  • Text categorization: assigning categories or tags to written content based on their topics, sentiments, or styles.


We have the ability to quickly adapt to changing environments and proactively meet our clients’ expectations and requirements while maintaining a high level of professionalism, combined with operational optimization and human vision. Being an agile company, allows us to constantly adjust our workflows and processes to address new challenges and opportunities, while continuously improving our overall performance and delivery outcomes.
Quality & Cost <br>efficiency

Quality & Cost

Our vision of service implies a high level of prevention and awareness, conformity of our deliverables, best IT environment (hardware, software, bandwidth), permanent evaluation of our production, continuous improvement of our processes and responsibility of our employees. Cost efficiency is also a top priority for us to make sure our clients achieve their expected results and outcomes while minimizing the amount of resources required to achieve them. It involves finding ways to reduce costs and improve productivity without sacrificing quality or effectiveness.

Data privacy & Security

Sensitive data and information require a high level of protection and confidentiality to safeguard our clients and individuals' personal information from cybercriminals, data breaches, and other privacy violations. We strictly comply with all contractual confidentiality rules agreed with our clients, in a fully secure physical and virtual environment. This involves proper handling, storage, and management of data to prevent from security threats and potential cyberattacks.
Corporate Social Responsibility

Corporate Social Responsibility

We consider CSR has one of the key pilars of an organization and it should be a long-term commitment. Therefore, we always try to operate in a manner that is socially responsible, ethical, and environmentally sustainable. By doing so, we demonstrate our commitment to behave as a corporate citizen, and to use our resources and influence to make a positive difference to the world. Our mission is also to help our employees to grow in a secure and challenging environment, by encouraging a culture of innovation and continuous learning.

Why choosing Qwanteos?

Artificial Intelligence
and Human Intelligence
are not opposed, they
complement each other.

New tools offered by AI Technologies require more than ever a new generation of experts able to extract data that will multiply our vision of the world and human activities.

AI cannot do everything, it needs to learn (Machine Learning), it must identify objects (Annotation and Labeling) to be able to analyze data.

Qwanteos provides AI Professionals and Companies that design these new tools, a dedicated team to enable them to exploit Artificial Intelligence at the maximum of its possibilities.

Qwanteos, a socially responsible company committed to overcoming your challenges

Qwanteos is a French and Malagasy company, which was created in 2022 by two former classmates who have a solid professional experience and a proven entrepreneurial background with a passion for technology, innovation and inventive projects.

Convinced by the rapid expansion of AI and the impact it will have on our daily lives, they decided to create a company with the ambition of supporting the entire AI ecosystem by providing dedicated resources and manpower to foster the emergence of AI based applications.

Qwanteos has a strong appetite for projects that priorities humanity’s best interests and have a positive impact on the way we live, work, consume... and especially those that care about society and the environment (AI for good). At Qwanteos, we also strongly believe in people and we are convinced by the idea that any success cannot be achieved without the involvement of motivated and passionate people who share the same values and are rewarded for the quality of their tasks and efforts.

This is why, as a socially responsible company, we invest in people for the long term and we help our employees to grow with us by ensuring that they always have the best working conditions, continuous access to training, and both easy and smooth communication to enable them to perform their tasks with the highest level of efficiency and professionalism.