Get production-grade datasets, expertly built for your AI
Expert, human-verified data labeling for the most ambitious AI models, delivered by specialists across 20+ sectors at every stage: pre-training, fine-tuning, RLHF, and evaluation
Human expertise built for production-scale AI
We build and run high-performance data operations designed around your models, quality standards and production goals — combining expert human judgment, rigorous QA and automation where it matters.
Vetted, trained and managed by our teams — no anonymous crowdsourcing, no uncontrolled clickwork.
Delivered across computer vision, NLP, generative AI, audio and multimodal workflows.
Production-ready datasets built, enriched and validated for model training, fine-tuning and evaluation.
Multi-layer QA, project-specific validation rules and continuous performance monitoring.
Specialist teams adapted to your linguistic, cultural and domain-specific requirements.
From project scoping to a trained, qualified team ready to start your pilot or scale into production.
Why choose our data labeling services?
Human Intelligence behind
Better AI
We turn complex real-world data into reliable ground truth through skilled human judgment, rigorous quality systems and scalable operations.
We treat Data Labeling as a discipline, with trained teams, clear standards and responsible working conditions, not low-cost microtasking.
Better data for AI teams. Better work for the people behind it.
Expert-led teams
We build dedicated teams of trained Data Labelers and Domain Experts around the needs of each project. The result is consistent human judgment, deep task understanding and reliable ground truth built to your specifications.
Responsible workforce
Our teams are recruited, trained and managed in-house, giving us full traceability over the people and processes behind your data. No anonymous crowdsourcing, just skilled professionals working within a structured and responsible model.
Dedicated delivery
Every project is led by an experienced delivery manager who oversees onboarding, production, quality and timelines. Workflows continuously adapt to your requirements, with the right combination of human review and automation.
Transparent pricing
Simple, predictable pricing based on the work delivered. No hidden platform fees, complex subscriptions or unexpected costs. You always know what you are paying for and how your budget is being used.
Secure by design
Security, confidentiality and responsible AI principles are embedded into our delivery model. We support demanding data environments with rigorous controls aligned with GDPR, ISO standards and evolving AI regulation.
Quality without compromise
Quality is engineered into every stage of delivery through clear guidelines, calibration, gold standards, multi-level QA and continuous monitoring. The result is dependable training and evaluation data ready for production AI systems.
Need training data,
but not a fully custom project yet?
For a POC, a first pilot, a small volume or a limited budget, get started in self-service with Dataset Finder.
Find. Organize.
Enrich your datasets.
Search across tens of thousands of AI datasets using natural language,
compare their annotations, licenses, formats and volumes, then
centralize your datasets in a single workspace.
Need custom data?
Launch your pilots or annotation projects directly in self-service
and have your data enriched by qualified human teams.
brought to you by Innovatiana!
Unlocking frontier AI, with impact.
We prepare the data thatadvance your AI
We prepare the data that advance your AI
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Data Labeling x Computer Vision
We create production-grade image and video datasets for computer vision models, combining trained human expertise, rigorous quality control and seamless integration with your existing data stack. Reliable ground truth, delivered in the format your models require.
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Data Labeling x Gen-AI
We create high-quality, domain-specific datasets for training, fine-tuning and evaluating generative AI models. Our expert teams combine linguistic, technical and business expertise to produce rich, contextual data across prompts, responses, dialogues, code and complex reasoning tasks.
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Content Moderation & RLHF
We provide high-quality human feedback to train, evaluate and align AI systems. From preference ranking and response evaluation to content moderation and safety review, our trained teams deliver the judgment and contextual understanding your models need.
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Document Processing
We transform complex documents into high-quality training data for document AI models. Text, PDFs and scans are structured, annotated and enriched with the context your models need to perform reliably across business use cases and languages.
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Natural Language Processing
We create high-quality multilingual datasets for training and evaluating NLP models. Our teams deliver precise, context-aware annotation across NER, classification, segmentation and semantic tasks, adapted to complex business use cases.
Data Labeling x Computer Vision
We create production-grade image and video datasets for computer vision models, combining trained human expertise, rigorous quality control and seamless integration with your existing data stack. Reliable ground truth, delivered in the format your models require.
Data Labeling x Gen-AI
We create high-quality, domain-specific datasets for training, fine-tuning and evaluating generative AI models. Our expert teams combine linguistic, technical and business expertise to produce rich, contextual data across prompts, responses, dialogues, code and complex reasoning tasks.
Content Moderation & RLHF
We provide high-quality human feedback to train, evaluate and align AI systems. From preference ranking and response evaluation to content moderation and safety review, our trained teams deliver the judgment and contextual understanding your models need.
Document Processing
We transform complex documents into high-quality training data for document AI models. Text, PDFs and scans are structured, annotated and enriched with the context your models need to perform reliably across business use cases and languages.
Natural Language Processing
We create high-quality multilingual datasets for training and evaluating NLP models. Our teams deliver precise, context-aware annotation across NER, classification, segmentation and semantic tasks, adapted to complex business use cases.
From raw data to production-ready datasets
We help Frontier AI Labs and Enterprise AI teams turn complex real-world data into reliable training and evaluation datasets.
We combine human expertise, rigorous quality methods and workflows designed around your model to deliver reliable, consistent data your AI teams can use with confidence.
We start by understanding your data needs
We review your raw data, objectives and constraints to define the right annotation strategy — including taxonomy, guidelines, tooling, contributor profiles, quality metrics and delivery process.
We align the team with your quality standards
Before scaling, we calibrate the workflow on a representative sample. Edge cases are identified, instructions are refined and acceptance criteria are validated with your team.
A dedicated team transforms your raw data
Our Data Labelers and AI Trainers are selected and trained specifically for your project. Led by a Data Labeling Manager, they annotate, evaluate and enrich your data against the agreed standards.
Quality is measured at every stage
Human review, sampling, gold-standard data, inter-annotator agreement and automated checks help us identify deviations early and continuously improve output quality.
Data that is ready to power your models
We deliver validated datasets in the required format, ready to plug into your workflows. Feedback from your teams and model performance can then feed directly into the next iteration.
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Tested and approved by our clients
In an industry too often marked by opaque practices and insecure working conditions, Innovatiana stands out. The company has built an ethical, people-first approach to data labeling, recognising annotators as skilled experts who play an essential role in AI development. Their expertise is valued, their contribution is visible, and their work is at the heart of a more responsible and sustainable approach to AI.
Innovatiana helps us review and improve the datasets we use to train our machine learning models. With their support, we’ve reviewed over 50,000 annotations and reduced review time by 30%. The team is dedicated, reliable, and always ready to find solutions. I particularly appreciate the personal approach and direct communication with people who understand our needs and project constraints. I wholeheartedly recommend Innovatiana!
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Innovatiana has annotated over 20,000 French property listings for us to train our text classification and recognition models. Their meticulous work achieved 98% compliance with our annotation guidelines, with 100% of batches delivered on time. We appreciate the quality of their work and the consistency of a team that knows our requirements well. Timelines are clearly defined, and communication is smooth throughout the project. We would gladly entrust them with future NLP or computer vision projects.
Several Innovatiana annotators work full-time as part of our team of surgeons and data scientists. Together, we have annotated over 67,000 images from more than 60 surgical procedures, covering around 30 categories of instruments. I value the team’s technical rigor and the commitment of the medical students supporting our projects. Their understanding of the medical context and attention to detail help us build high-quality datasets to train our AI models.
Innovatiana is part of the fourth cohort of our impact accelerator. Through its data annotation studio in Majunga, Madagascar, the company puts impact sourcing into practice: creating local jobs in communities with limited employment opportunities, recognising annotators’ expertise, and making transparency and fair working conditions central to its model. A concrete example of how the AI industry can create meaningful social impact.
At Innovatiana, ethical AI starts with respect for the people who make it possible. Fair working conditions, a supportive environment, and recognition of annotators’ expertise are central to the company’s approach. That investment in people is reflected in the care they bring to their work and the quality of the data they deliver. It shows how social responsibility and technical excellence go hand in hand.
Innovatiana shows that high-performing AI is built on a strong commitment to people. The company puts ethics into practice from the earliest stages of data preparation, recognising the essential role and expertise of its annotators. This is ethics by design, with respect for people at the heart of technological progress.
Innovatiana helped us annotate over 30,000 pipe inspection images across more than 50 classes, using a classification framework based on EN 13508-2. The project required careful image analysis and precise distinctions between closely related categories. The team was responsive, meticulous, and committed to understanding our technical requirements. We particularly valued the consistency of the annotations and the quality of the deliverables throughout this complex project.
Innovatiana sets a strong example for the data annotation industry, combining technical expertise, rigorous quality standards, and a clear commitment to ethical practices. They invest in their annotators through training, expert guidance, and fair, transparent working conditions. The result is an approach that values both the quality of the data and the people behind it.
Why outsource your Data Labeling?
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.
Artificial intelligence models require a large volume of labelled data
Artificial intelligence depends on high-quality data to learn, adapt and deliver reliable results. Whether a model is designed for classification, object detection or content generation (GenAI), its development starts with carefully prepared datasets. Data labeling plays a central role in the AI development lifecycle, turning images, videos, text and multimodal content into structured, annotated data.
From supervised learning to fine-tuning (SFT) and model evaluation, it provides a foundation for AI performance - yet its importance is often underestimated. Each use case calls for precise guidelines, careful calibration and tailored quality checks. We combine the care of a craftsperson with the capacity to scale, supporting you from your first dataset through to large-scale production. The result is consistent, well-documented data, ready to integrate into your workflows.
👉 Learn more: discover how to build a high-quality dataset for artificial intelligence
Human evaluation is required to build accurate and unbiased models
In the age of generative AI (GenAI), data labeling is more important than ever for improving model reliability and accuracy and reducing bias. Whether for traditional applications such as computer vision, NLP, and content moderation or advanced workflows such as RLHF, domain experts help ensure the quality and representativeness of datasets. Increasingly stringent regulatory frameworks also reinforce the need for quality data.
Comparing responses, ranking preferences for RLHF, checking facts, or evaluating outputs against your business criteria: each task follows shared guidelines and includes ongoing quality checks. This approach helps you understand your models’ limitations, identify where corrections are needed, and build training and evaluation datasets that reflect your use cases. Our standard remains the same at every stage: rigorous, traceable human judgment that delivers practical value.
👉 Learn more: explore the role of human judgment in LLM development with RLHF
“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.) for your more advanced 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
Nous utilisons les principales plateformes d'annotation de données du marché pour nous adapter à vos besoins et à vos demandes les plus spécifiques !
Secure Data
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!
