Content Moderation & RLHF
Our data moderation specialists analyze your structured and unstructured data to refine the capabilities of your AI (including LLM), including reinforcement learning systems based on human feedback (RLHF), where manual intervention refines the learning process of the AI agent based on human expertise. We can provide experts who are available around the clock 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 judgment into the learning process of your AIs. Our experts, with in-depth expertise in RLHF, intervene to refine and validate your AI decisions, by providing 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 scenarios. Logical errors are identified and re-annotated to create additional training data sets for fine-tuning generative models.
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Moderation of unstructured content and the constitution of training data for LLM fine-tuning
By analyzing your unstructured data (examples: data from social networks, collected on the Internet), we develop prompt and comprehensive responses taking into account various dimensions such as tone, presentation format, justification, and much more. We identify the optimal distribution of data to be created to build a basic training data set 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 judgment into the learning process of your AIs. Our experts, with in-depth expertise in RLHF, intervene to refine and validate your AI decisions, by providing 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 scenarios. Logical errors are identified and re-annotated to create additional training data sets for fine-tuning generative models.
Moderation of unstructured content and the constitution of training data for LLM fine-tuning
By analyzing your unstructured data (examples: data from social networks, collected on the Internet), we develop prompt and comprehensive responses taking into account various dimensions such as tone, presentation format, justification, and much more. We identify the optimal distribution of data to be created to build a basic training data set 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 judgment into the learning process of your AIs. Our experts, with in-depth expertise in RLHF, intervene to refine and validate your AI decisions, by providing 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 scenarios. Logical errors are identified and re-annotated to create additional training data sets for fine-tuning generative models.
Nuestro método
Un equipo de etiquetadores de datos profesionales, dirigido por profesionales, para ayudarle a crear y mantener conjuntos de datos de calidad para sus necesidades de externalización de IA(anotación de datos para modelos de Machine Learning, Deep Learning o NLP).
Estudiamos sus necesidades
Le ofrecemos apoyo a medida, teniendo en cuenta sus limitaciones y plazos. Le asesoramos sobre su infraestructura de etiquetado, el número de Data Labelers necesario para satisfacer sus necesidades y el tipo de anotaciones que debe utilizar.
Llegamos a un acuerdo
En 48 horas, realizaremos una prueba (gratuita) para ofrecerle un contrato adaptado a sus necesidades. No bloqueamos el servicio: sin abono mensual, sin compromiso. Cobramos por trabajo.
Nuestros etiquetadores de datos tratan sus datos
Estamos movilizando un equipo de Data Labelers en nuestro centro de servicios de Majunga (Madagascar). Este equipo anglófono y francófono está dirigido por uno de nuestros responsables: su interlocutor privilegiado.
Realizamos una revisión de la calidad
Como parte de nuestro enfoque de garantía de calidad, revisamos el trabajo de nuestros etiquetadores de datos. Esta revisión se basa en una serie de comprobaciones manuales (pruebas de muestras) y automatizadas para garantizarle el máximo nivel de calidad.
Entregamos los datos
Le proporcionamos los datos preparados( diversosconjuntos de datos: imágenes o vídeos anotados, archivos estáticos revisados y mejorados, etc.), según las condiciones acordadas con usted (transferencia segura o datos integrados en sus sistemas).
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¡Estás hablando de nosotros!
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 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 encarna exactamente lo que queremos promover en el ecosistema de anotación de datos: un enfoque experto, riguroso y decididamente ético. Su capacidad para capacitar y supervisar a anotadores altamente calificados, al tiempo que garantizan condiciones de trabajo justas y transparentes, los convierte en un modelo en su clase.
Etiquetado de datos Subcontratación ética
Somos profesionales del etiquetado ético de datos
Muchas empresas que prestan servicios de etiquetado de datos operan en países de renta baja sobre una base contractual y a menudo impersonal. Los etiquetadores de datos no siempre reciben una remuneración justa ni trabajan en condiciones dignas. En contra de esta "tendencia" del mercado, queremos ofrecer una externalización que tenga sentido e impacto.
Externalización ética
Rechazamos las denominadas prácticasde "crowdsourcing": creamos puestos de trabajo estables y valorados para ofrecerle una subcontratación que tenga sentido e impacto, así como transparencia sobre el origen de los datos utilizados para la IA.
Tarifas competitivas
Ofrecemos condiciones flexibles, con precios adaptados a sus necesidades y recursos. Cobramos por trabajo (ejemplo: "etiquetar 50.000 imágenes con recuadros delimitadores"): sin suscripción ni costes de configuración.
Un modelo integrador
Contratamos a nuestro propio equipo en Madagascar y les formamos en técnicas de procesamiento de datos y etiquetado para IA. Les ofrecemos un salario justo, buenas condiciones de trabajo y oportunidades de desarrollo profesional.
Un futuro mejor
Queremos contribuir al desarrollo de ecosistemas virtuosos en Madagascar (formación, empleo, inversión local, etc.).
Sus datos seguros
Prestamos especial atención a la seguridad y confidencialidad de los datos. Evaluamos el carácter crítico de los datos que desea confiarnos y aplicamos las mejores prácticas de seguridad de la información para protegerlos.
Hacia la adopción de la IA en Europa y Francia
Queremos acelerar la adopción de técnicas de Inteligencia Artificial en Francia y Europa. Creemos en una IA construida éticamente e invertimos en nuestros equipos dedicados al etiquetado de datos.
Alimente sus modelos de inteligencia artificial con datos de entrenamiento de alta calidad.
