We simplify data preparation and labelling in energy
Infrastructure inspection, consumption forecasting, failure detection... We structure your images, videos, documents and time series to train AIs capable of automating, predicting and securing energy production and distribution.
You work in the energy sector
We annotated thousands of files from solar, wind, water and distribution network projects. Visual inspection of panels or turbines, data extraction on energy projects, segmentation of regulatory documents... our datasets are used to develop robust AIs to meet the challenges of the energy transition.
Optimizing smart electrical networks
AI plays a crucial role in the intelligent management of electrical networks by predicting consumption peaks and optimizing energy distribution. It contributes to energy efficiency and cost reduction
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Prediction and management of renewable energies
Thanks to the analysis of weather data in real time, AI optimizes the production of energy from renewable sources, such as wind or solar, by anticipating their production and by effectively integrating these sources. intermittent in the electrical network.
Predictive maintenance of energy infrastructures
Artificial intelligence makes it possible to anticipate failures in energy infrastructures, by analyzing data from sensors installed on equipment. This limits interruptions and significantly reduces maintenance costs.
Data outsourcing with meaning and impact
We believe in ethical outsourcing in the service of artificial intelligence—far from the impersonal crowdsourcing of our competitors. Our commitment: actively investing in the training and continuous development of our Data Labelers & AI Trainers, ensuring lasting expertise and a dedicated team focused on the success of your projects.
Ethical outsourcing
We build our teams directly in Madagascar, providing them with rigorous training in best practices for data processing and annotation for artificial intelligence. We ensure fair compensation, excellent working conditions, and real opportunities for professional growth
Inclusive model
We champion an inclusive model, firmly believing that human diversity is essential to the quality of data for AI. Madagascar—considered one of the most culturally, linguistically, and biologically diverse islands in the world—offers a unique foundation for creating rich, representative, and reality-grounded datasets
Hands-on management
Each project entrusted to us is overseen by a Manager specialized in AI dataset creation—your dedicated point of contact. They coordinate a team of Data Labelers assigned specifically to your objectives and ensure clear, realistic timelines
Competitive pricing
We offer simple and transparent pricing, designed to adapt to your actual needs. You only pay for what you use—task-based, with no subscriptions or hidden fees—featuring custom volumes and formats, all at the best quality-to-price ratio
Those building the AI of tomorrow trust us
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.
Use cases in the industry
We annotated thousands of photos of power plants, solar farms and wind turbines to identify critical equipment (turbines, panels, transformers, lines). Thanks to precise annotations (bounding boxes, polygons, segmentation), these data sets are used to train models for visual supervision, predictive maintenance or automated inventory.
Fuel your AI models with high-quality, expertly crafted training data!
