We craft datasets to train, fine-tune and power your AI models
Supercharge your AI with high-quality datasets prepared by domain-trained teams and expert data labelers across 20+ sectors. Maximize the performance of your models (ML, DL, LLM, VLM, RAG, RLHF) through an ethical, responsive partner—built for speed, scale, and real-world impact
Why choose Innovatiana for your Data Labeling tasks?
Many companies
claim to provide “fair” data
Creating datasets for AI is much more than just stringing together repetitive tasks — it's about building ground truth with rigor, meaning, and impact. At Innovatiana, we value annotators, professionalize Data Labeling and defend responsible outsourcing — structured, demanding but fair and deeply human — far from low-cost approaches that neglect quality as well as working conditions
Inclusive model
We recruit and train our own teams of specialized Data Labelers and Domain Experts based on your project requirements. By valuing the people behind the annotations, we deliver high-quality, reliable data tailored to your needs.
Ethical outsourcing
We reject impersonal crowdsourcing. Our in-house teams ensure full traceability of annotations and contribute to a responsible, meaningful approach. It’s outsourcing with purpose and impact — for datasets that meet the ethical standards of AI.
Proximity management
Each project is overseen by a dedicated manager who structures the annotation process and streamlines production. They coordinate the team, adapt methods to your objectives, and implement automatic or semi-automatic quality controls to ensure reliable data — all while meeting your deadlines.
Clear & transparent pricing
We charge per task or per dataset delivered, depending on the volume and complexity of your project. No subscriptions, no set-up fees, or hidden costs. You only pay for the work done, with total visibility on the budget.
Security & Responsible AI
We protect your data while integrating responsible AI principles. Rigorous structuring, balancing datasets, reducing biases: we ensure ethical uses. Confidentiality, compliance (RGPD, ISO) and governance are at the heart of our approach.
Uncompromising quality
Our Data Labelers follow a rigorous methodology supported by systematic quality controls. Each project is closely monitored to deliver reliable datasets, ready to be used for training your AI models.
We structure your data, you train your AI
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Data Labeling x Computer Vision
Our Data Labelers are trained in best practices for annotating images and videos for computer vision. They participate in the creation of large supervised data sets (Training Data) intended to train your Machine Learning or Deep Learning models. We work directly on your tools (via an online platform) or on our own secure environments (Label Studio, CVAT, V7, etc.). At the end of the project, you retrieve your annotated data in the format of your choice (JSON, XML, Pascal VOC,...) via a secure channel.
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Data Labeling x Gen-AI
Our team brings together experts with varied profiles — linguists, developers, developers, lawyers, business specialists — capable of collecting, structuring and enriching data adapted to the training of generative AI models. We prepare complex data sets (prompts/responses, dialogues, code snippets, summaries, explanations, etc.) by combining expert manual research with automated checks. This approach guarantees rich, contextualized and directly usable datasets for the fine-tuning of LLMs in various fields.
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Content Moderation & RLHF
We moderate the content generated by your AI models in order to guarantee its quality, security and relevance. Whether it is a question of identifying excesses, evaluating factual situations, recording responses or intervening in RLHF loops, our team combines human expertise and specialized tools to adapt the analysis to your business challenges. This approach reinforces the performance of your models while ensuring better control of risks associated with sensitive or out-of-context content.
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Documents Processing
Optimize the training of your documentary analysis models through accurate and contextualized data preparation. We structure, annotate and enrich your raw documents (texts, PDFs, scans) to extract maximum value, with tailor-made human support at each stage. Your AI gains in reliability, business understanding and multilingual performance.
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Natural Language Processing
We support you in structuring and enriching your textual data to train robust NLP models, adapted to your business challenges. Our multilingual teams (French, English, and many others) work on complex tasks such as named entity recognition (NER), classification, segmentation or semantic annotation. Thanks to rigorous and contextualized annotation, you improve the accuracy of your models while accelerating their production.
Data Labeling x Computer Vision
Our Data Labelers are trained in best practices for annotating images and videos for computer vision. They participate in the creation of large supervised data sets (Training Data) intended to train your Machine Learning or Deep Learning models. We work directly on your tools (via an online platform) or on our own secure environments (Label Studio, CVAT, V7, etc.). At the end of the project, you retrieve your annotated data in the format of your choice (JSON, XML, Pascal VOC,...) via a secure channel.
Data Labeling x Gen-AI
Our team brings together experts with varied profiles — linguists, developers, developers, lawyers, business specialists — capable of collecting, structuring and enriching data adapted to the training of generative AI models. We prepare complex data sets (prompts/responses, dialogues, code snippets, summaries, explanations, etc.) by combining expert manual research with automated checks. This approach guarantees rich, contextualized and directly usable datasets for the fine-tuning of LLMs in various fields.
Content Moderation & RLHF
We moderate the content generated by your AI models in order to guarantee its quality, security and relevance. Whether it is a question of identifying excesses, evaluating factual situations, recording responses or intervening in RLHF loops, our team combines human expertise and specialized tools to adapt the analysis to your business challenges. This approach reinforces the performance of your models while ensuring better control of risks associated with sensitive or out-of-context content.
Documents Processing
Optimize the training of your documentary analysis models through accurate and contextualized data preparation. We structure, annotate and enrich your raw documents (texts, PDFs, scans) to extract maximum value, with tailor-made human support at each stage. Your AI gains in reliability, business understanding and multilingual performance.
Natural Language Processing
We support you in structuring and enriching your textual data to train robust NLP models, adapted to your business challenges. Our multilingual teams (French, English, and many others) work on complex tasks such as named entity recognition (NER), classification, segmentation or semantic annotation. Thanks to rigorous and contextualized annotation, you improve the accuracy of your models while accelerating their production.
Our method
A team of professional Data Labelers & AI Trainers, led by experts, to create and maintain quality data sets for your AI projects (creation of custom datasets to train, test and validate your Machine Learning, Deep Learning or NLP models, ... or for the fine-tuning of LLMs!)
We study your needs
We offer tailor-made assistance taking into account your constraints and deadlines. We offer advice on your Data Labeling process and infrastructure, number of professional Labeles and Domain Experts required according to your needs or the nature annotations required.
We reach an agreement
Within 48 hours, we assess your needs and carry out a test when necessary, in order to propose an approach that is adapted to your challenges. We don't lock down the service: no monthly subscription, no commitment. We charge per project!
Our Data Labelers prepare your data
We mobilize a team of Data Labelers or AI Trainers, supervised by a Data Labeling Manager, your dedicated contact person. We work either on our own tools, chosen according to your use case, or by integrating ourselves into your existing annotation environment.
We carry out a quality review
As part of our Quality Assurance approach, annotations are reviewed via manual sampling checks, inter-annotator agreement measures (IAA) and automated checks. This approach guarantees a high level of quality, in line with the requirements of your models.
We deliver your dataset
We provide you with the prepared data (various datasets: annotated images or videos, revised and enriched static files, etc.), according to terms agreed with you (secure transfer or data available in your systems).
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They tested, they testify
In a sector where opaque practices and precarious conditions are too often the norm, Innovatiana is an exception. This company has been able to build an ethical and human approach to data labeling, by valuing annotators as fully-fledged experts in the AI development cycle. At Innovatiana, data labelers are not simple invisible implementers! Innovatiana offers a responsible and sustainable approach.
Innovatiana helps us a lot in reviewing our data sets in order to train our machine learning algorithms. The team is dedicated, reliable and always looking for solutions. I also appreciate the local dimension of the model, which allows me to communicate with people who understand my needs and my constraints. I highly recommend Innovatiana!
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Innovatiana helps us to carry out data labeling tasks for our classification and text recognition models, which requires a careful review of thousands of real estate ads in French. The work provided is of high quality and the team is stable over time. The deadlines are clear as is the level of communication. I will not hesitate to entrust Innovatiana with other similar tasks (Computer Vision, NLP,...).
Several Data Labelers from the Innovatiana team are integrated full time into my team of surgeons and Data Scientists. I appreciate the technicality of the Innovatiana team, which provides me with a team of medical students to help me prepare quality data, required to train my AI models.
Innovatiana is part of the 4th promotion of our impact accelerator. Its model is based on outsourcing with a positive impact with a service center (or Labeling Studio) located in Majunga, Madagascar. Innovatiana focuses on the creation of local jobs in areas that are poorly served or poorly served and on transparency/valorization of working conditions!
Innovatiana is deeply committed to ethical AI. The company ensures that its annotators work in fair and respectful conditions, in a healthy and caring environment. Innovatiana applies fair working practices for Data Labelers, and this is reflected in terms of quality!
In a context where the ethics of AI is becoming a central issue, Innovatiana shows that it is possible to combine technological performance and human responsibility. Their approach is fully in line with a logic of ethics by design, with in particular a valuation of the people behind the annotation.
Working with Innovatiana has been a great experience. Their team was both reactive, rigorous and very involved in our project to annotate and categorize industrial environments. The quality of the deliverables was there, with real attention paid to the consistency of the labels and to compliance with our business requirements.
Innovatiana embodies exactly what we want to promote in the data annotation ecosystem: an expert, rigorous and resolutely ethical approach. Their ability to train and supervise highly qualified annotators, while ensuring fair and transparent working conditions, makes them a model of their kind.
Why outsource your Data Labeling tasks?
Today, small, well-labeled datasets with ground truth are enough to advance your AI models. Thanks to SFT and targeted annotations, quality now takes precedence over quantity for more efficient, reliable and economical training.
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Artificial intelligence models require a large volume of labelled data
Artificial intelligence relies on annotated data to learn, adapt, and produce reliable results. Behind each model, whether for classification, detection or content generation (GenAI), it is first necessary to build quality datasets. This phase of the AI SDLC involves Data Labeling: a process of selecting, annotating and structuring data (images, videos, text, multimodal data, etc.). Essential for supervised training (Machine Learning, Deep Learning), but also for fine-tuning (SFT) and the continuous improvement of models, Data Labeling remains a key step, often underestimated, in the performance of AI.
Human evaluation is required to build accurate and unbiased models.
In the age of GenAI, data labeling is more essential than ever to develop models that are reliable, accurate and free of bias. Whether it is traditional applications (Computer Vision, NLP, Moderation) or advanced workflows such as RLHF, the contribution of domain experts is essential to ensure the quality and representativeness of datasets. Ever more stringent regulatory frameworks require the use of high-quality data sets for”minimize discriminatory risks and outcomes” (European Commission, FDA). This context reinforces the key role of human evaluation in the preparation of training data.
“Data Labeling is an essential step to train AI models that are reliable and efficient. Although it is often perceived as manual and repetitive work, it nevertheless requires rigor, expertise and organization on a large scale. At Innovatiana, we have industrialized this process: structured methods, automated quality controls and the use of domain experts (health, legal, software development, etc.) according to your projects.
This approach allows us to process large volumes while ensuring relevant and high quality data. We help you optimize your costs and resources, so your team can focus on what matters most: your models, use cases, and products.
But beyond performance, we are carrying out an impact project: create stable and rewarding jobs in Madagascar, with ethical working conditions and fair wages. We believe that talent is everywhere — and opportunities should be, too. Outsourcing data labeling is a responsibility, and we turn it into a driver of quality, efficiency, and positive impact for your AI projects.“
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Compatible with
your stack
We use all major data annotation platforms to adapt to your needs — even your most specific requirements.
Data secure
We pay particular attention to data security and confidentiality. We assess the criticality of the data you want to entrust to us and deploy best information security practices to protect it.
No stack? No prob.
Regardless of your tools, your constraints or your starting point: our mission is to deliver a quality dataset. We choose, integrate or adapt the best annotation software solution to meet your challenges, without technological bias.
Feed your AI models with high quality training data!
