Data Moderation & RLHF
Our data moderation specialists analyze your structured and unstructured data to fine-tune your AI's capabilities (including LLM), including reinforcement learning from human feedback (RLHF) systems, where manual intervention refines the AI agent's learning process based on human expertise. We can provide continuously available experts for your most specific tasks.
RLHF: validation of data created by generative models using human feedback
Reinforcement Learning from Human Feedback (RLHF) integrates human intelligence and discernment into the learning process of your AIs. Our experts, with their advanced RLHF expertise, step in to refine and validate your AI's decisions, bringing a level of judgment and nuance that only human intelligence can offer. Synthetically generated images or other data are analyzed to ensure that they correspond to real-life scenarios. Logical errors are identified and re-annotated to create additional training datasets for fine-tuning generative models.
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Moderation of unstructured content and creation of training data for LLM fine-tuning
By analyzing your unstructured data (examples: data from social networks, collected on the Internet), we create comprehensive prompts and responses, taking into account various dimensions such as tone, presentation format, justification, and much more. We identify the optimal distribution of data to create a core training dataset, or to refine an existing language model.
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RLHF: validation of data created by generative models using human feedback
Reinforcement Learning from Human Feedback (RLHF) integrates human intelligence and discernment into the learning process of your AIs. Our experts, with their advanced RLHF expertise, step in to refine and validate your AI's decisions, bringing a level of judgment and nuance that only human intelligence can offer. Synthetically generated images or other data are analyzed to ensure that they correspond to real-life scenarios. Logical errors are identified and re-annotated to create additional training datasets for fine-tuning generative models.
Moderation of unstructured content and creation of training data for LLM fine-tuning
By analyzing your unstructured data (examples: data from social networks, collected on the Internet), we create comprehensive prompts and responses, taking into account various dimensions such as tone, presentation format, justification, and much more. We identify the optimal distribution of data to create a core training dataset, or to refine an existing language model.
RLHF: validation of data created by generative models using human feedback
Reinforcement Learning from Human Feedback (RLHF) integrates human intelligence and discernment into the learning process of your AIs. Our experts, with their advanced RLHF expertise, step in to refine and validate your AI's decisions, bringing a level of judgment and nuance that only human intelligence can offer. Synthetically generated images or other data are analyzed to ensure that they correspond to real-life scenarios. Logical errors are identified and re-annotated to create additional training datasets for fine-tuning generative models.
Our method
A team of professional Data Labelers, led by professionals, to help you create and maintain quality datasets for your AI outsourcing needs(data annotation for Machine Learning, Deep Learning or NLP models)
We study your needs
We propose a tailor-made assistance, taking into account your constraints and deadlines.We offer advice on your Labeling infrastructure, the number of Data Labelers required according to your needs and the type of annotations to be used.
We find an agreement
Within 48 hours, we do a test (free of charge). We find an agreement which is convenient for you. We do not lock the service: no monthly subscription, no commitment. We bill by the job!
Our Data Labelers process your Data
We are mobilizing a team of Data Labelers at our service center in Majunga (Madagascar). This English- and French-speaking team is led by one of our Managers: your privileged contact.
We carry out a Quality Review
As part of our Quality Assurance process, we review the work of our Data Labelers. This review is based on a series of manual (sample tests) and automated checks in order to guarantee you the highest level of quality!
We deliver the Data
We provide you with the prepared Data( variousdata sets: annotated images or videos, revised and enriched static files, etc.), according to the terms agreed with you (secure transfer or data integrated into your systems).
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You are talking about us
I've worked with Innovatiana on a variety of labeling and data cleaning activities - work that requires rigor and must remain manual... because quality is key! What I appreciate most about Innovatiana is the assurance that my data is prepared ethically, by a Labeling Studio whose team works regular hours and is paid fairly!
Innovatiana is very helpful in reviewing our datasets 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 interact with people who understand my needs and constraints. I highly recommend Innovatiana!
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Innovatiana helps us perform data labeling tasks for our classification and text recognition models, which requires a thorough review of thousands of French real estate ads. The work provided is of high quality and the team is stable over time. The deadlines are clear as well as 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. All of them work together to build innovative AI products. I appreciate the expertise of the Innovatiana team, which has managed to provide me with a team of medical students with the knowledge of anatomy needed to prepare the quality data required to train my AI models.
Innovatiana is part of the 4th promotion of our impact gas pedal. Its model is based on positive impact outsourcing with a service center (or Labeling Studio) located in Majunga, Madagascar. Innovatiana focuses on the creation of local jobs in under-served or underserved areas and a transparency/valuation of working conditions!
Ethical Data Labeling Outsourcing
We are the pros of ethical Data Labeling
Many companies providing Data Labeling services operate in low-income countries on a contractual and often impersonal basis. Data Labelers are not always paid fairly or work in decent conditions. Contrary to this market "trend", we want to offer outsourcing that has meaning and impact!
Ethical outsourcing
We refuse the so-called"crowdsourcing" practices: we create stable and valued jobs to offer you Outsourcing that has meaning and impact as well as transparency about the origin of the Data used for AI.
Competitive rates
We offer flexible conditions, for a pricing adapted to your stakes and to your means. We charge by the job (example: "label 50,000 images with bounding boxes"): no subscription, no set-up fees.
An inclusive model
We recruit our own team in Madagascar and train them in Data Processing and Labeling techniques for AI. We offer them a fair salary, good working conditions and career development opportunities.
A brighter future
We want to contribute to the development of virtuous ecosystems in Madagascar (training, employment, local investments, etc.).
Your Data secured
We pay particular attention to Data Security and Confidentiality. We assess the criticality of the Data you wish to entrust to us and deploy the best Information Security practices to protect it.
Towards the adoption of AI in Europe and France
We want to accelerate the adoption of Artificial Intelligence techniques in France and in Europe. We believe in ethically built AI and invest in our dedicated Data Labeling teams.
Fuel your AI models with High Quality Training Data!
