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Content moderation & Reinforcement Learning

Increase the reliability and alignment of your generative models through rigorous human evaluation and expert content moderation. Innovatiana supports you in the continuous optimization of your IAs (LLM, VLM, RAG, conversational agents, etc.)

GIF animation of luxury fashion bags being moderated by data annotators... the illustration is a bit stylish, with 3 or 4 different colors only

Our annotators are at the heart of the process of RLHF (Reinforcement Learning from Human Feedback) to refine the answers of your AI. They assess their relevancy, consistency and alignment with human intentions

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Moderation of AI-generated content

RLHF — Learning through human feedback

Ethical compliance

Contextual moderation

Moderation of AI contents

We moderate the content generated by your AIs to reinforce its quality, security and relevance, thanks to human and technological expertise adapted to each sector. In this way, you increase the impact of your models while controlling risks.

Image of an AI producing content and segments / labels done by annotators to label or moderate this content

Detecting hate speech

Identify, annotate, and filter content generated by AI models that include violent, discriminatory or hostile speech in respect of groups or individuals.

⚙️ Process steps:

Manual or assisted annotation of AI answers containing problematic remarks

Fine classification of the types of hate speech (direct, implicit, inciting, humorous, etc.)

Construction of data sets for training or evaluating automatic filters

Quality review by annotators trained in context detection

🧪 Practical applications:

Chatbots & AI assistants — Automatic blocking or reformulation of responses generated at risk

Pre-publication moderation — Monitoring of outputs generated by text-to-text or text-to-image models

Anti-toxicity filter training — Improving conversational security in AI systems

2d image showing a form with AI-generated content. Toxic / inappropriate content is highlighted in red, with a checklist used by a moderator to control this content and flag it as inappropriate

Inappropriate content

Locate, annotate, and control responses produced by AI systems that may contain items shocking, offensive, vulgar or inappropriate to the context of use.

⚙️ Process steps:

Definition of risky content categories (vulgarity, suggested nudity, sensitive remarks, sexual insinuations, etc.)

Manual or semi-automated review of AI responses generated in different contexts

Annotation of severity levels (mild, moderate, critical) and types of inconvenience

Development of datasets to train content filters or scoring models

🧪 Practical applications:

Text or image generation systems — Filtering NSFW or offensive content before posting

Conversational assistants — Preventing slip-ups in responding to ambiguous requests

AI products for the general public (young people, families) — Secure interactions for all ages

2d drawing with a magnifying glass, a human anntoator, a checklist and a form with potential issues in its content. To illustrate human review of AI produced content

Human review of sensitive outputs

Submit answers generated by an AI in contexts to specialized annotators or moderators strong ethical, legal or reputational challenge. This validation step ensures that the content broadcast is appropriate, reliable and compliant, especially when they touch on critical areas.

⚙️ Process steps:

Identification of sensitive scenarios (health, justice, religion, religion, politics, gender, minors, etc.)

Human review with an evaluation grid: factual, tone, clarity, bias, potential danger

Annotation of levels of sensitivity or risk (erroneous information, tendentious comments, poorly formulated response, etc.)

Reporting or removing non-compliant content + reformulation if necessary

🧪 Practical applications:

Regulated areas (finance, insurance) — Validation of AI content before publication or integration into a client tool

General chatbots — Monitoring AI responses to sensitive or provocative prompts

Generative content moderation — Adding a level of human validation to sensitive interactions (“Human-in-the-Loop”)

2d image with AI and an angry emoji, with a scale showing 4.5 - which is a toxicity scoring scale

Response toxicity scoring

Quantify the degree of harmfulness, aggressiveness or danger of a response generated by an AI model, in order to assess its relevance, to guide automatic moderation or to feed correction loops (RLHF, filtering, reformulation). This score allows a objective and repeatable measurement the ethical quality of the content produced.

⚙️ Process steps:

Definition of a toxicity grid (violent, insulting, insulting, discriminatory, sarcastic language, etc.)

Human annotation of the responses generated, according to their tone, target and potential severity

Analysis of discrepancies between AI and human judgment to refine filtering models

Creation of labeled datasets to train or calibrate toxicity classifiers

🧪 Practical applications:

AI assistant monitoring — Evaluate responses to sensitive or diverted prompts

Development of content filters — Feed models for detecting unacceptable speech

Online reporting tools — Improvement of moderation systems based on toxicity thresholds

Image of content types (legal for instance) text, and folders, illustrating categorisation of content

Content categorization

Organize training data for AI or responses generated by an AI by thematic or functional categories (e.g. sport, politics, health, marketing, etc.), in order to facilitate their moderation, filtering, personalization, or analysis.

⚙️ Process steps:

Definition of a category repository adapted to the use case

Manual annotation of AI responses according to the target classification (mono or multi-label)

Construction of labelled datasets for the training of supervised classifiers

Quality check (inter-annotator, ambiguities, close classes)

🧪 Practical applications:

Structuring the corpora generated for analysis or evaluation — Thematic organization facilitated

Preparing for sectoral moderation — Identify answers in sensitive areas (legal, medical, etc.)

Benchmark of generative models — Measure the thematic distribution of the responses produced

Image with an AI agent with a smiley face, and a human annotator, illustrating human moderation of AI agents

Moderation of AI agents

Supervise, control and correct the behaviors or responses of virtual assistants (chatbots, voicebots, co-pilots, etc.) to avoid drifts, biases or clumsiness in interactions with users.

⚙️ Process steps:

Definition of moderation rules according to the context of use (sector, language, target, tone)

Monitoring AI conversations via targeted human review

Escalation of critical cases to human moderators (validation or correction)

Creation of training sets to refine model behaviors via RLHF or fine-tuning

🧪 Practical applications:

Health or insurance agents — Verification that the AI does not issue medical or legal recommendations

Online assistants from top brands — Alignment of responses with brand tone and internal policies

Multilingual interactions — Verification of the coherence and neutrality of speech in each language

RLHF

We moderate the content generated by your AIs to reinforce its quality, security and relevance, thanks to human and technological expertise adapted to each sector. In this way, you increase the impact of your models while controlling risks.

Image of a form with rankings of AI responses

AI response ranking

Present several responses generated by one or more models from the same prompt, and Classify them according to their perceived quality. To identify the most useful, relevant, safe, or appropriate formulations, and provide training data for preferred models (SFT, RLHF, rerankers...).

⚙️ Process steps:

Manual annotation by trained moderators or annotators

Definition of preference criteria

Qualitative or comparative scoring

Quality control by double annotation or consensus

🧪 Practical applications:

Reinforcement training (RLHF) — Creation of preferential data to refine LLM behaviors

Construction of “oracles” datasets — Create references to guide or evaluate other models

Linguistic or sectoral benchmarking — Compare the performance of models according to languages, styles or business areas

Image of multimodal data (images, text, rankings, scoring, URLs, etc.) illustrating human preference annotation

Annotating human preferences

Gather qualitative judgments from annotators on AI-generated responses, depending on what a human would deem to be most useful, clear, relevant, or appropriate. Allows you to train or adjust generative models according to real expectations and preferences of end users.

⚙️ Process steps:

Selection or generation of several responses for the same prompt (2 or +)

Presentation to a human annotator with preference instructions (quality, respect for the prompt, style, etc.)

Supervision by precise guidelines to avoid subjective biases

Quality control via double annotation or arbitration

🧪 Practical applications:

RLHF model training — Integrate the human signal to guide generative behaviors

Customizing AI assistants — Adapt responses to a specific audience, style, or context

Continuous improvement of conversational AI — Integrate human feedback into learning cycles

2d image showing a text with a pencil, used as an illustration of manual revision of content

Manual output review

Correct, rephrase, or adjust manually the responses generated by an AI model, in order to guarantee a high level of quality, clarity, accuracy or adaptation to the context. This step is often used to build reference datasets (gold data or gold standard) or refine a model via Supervised fine-tuning.

⚙️ Process steps:

Selection of generated outputs requiring revision

Correction or rewriting of the answer by a human expert

Annotating the types of changes made

Use of before-and-after pairs for supervised training, evaluation, or documentation

🧪 Practical applications:

Composition of sets of examples — Creation of “before-after” pairs to train models via direct supervision

Marketing — Stylistic correction of generated texts to respect the brand tone or target audience

Health — Review of AI responses to eliminate inaccurate formulations or formulations that do not comply with clinical recommendations

2d image of a bubble and text, to illustrate data generation with synthetic data generation tools

Data generation for RLHF

Produce Prompt and varied responses allowing models to be exposed to different formulations, levels of quality, or response styles. This data is then classified or evaluated by human annotators to guide reinforcement learning.

⚙️ Process steps:

Manually creating prompts representative of target users

Verification of the diversity of the outputs produced (style, relevance, errors)

Preparation of pairs or lists to be classified by human annotators

Organization of the dataset for training: prompts + responses + human preferences

🧪 Practical applications:

Optimizing conversational models — Creation of realistic scenarios to train models to respond better

Robustness of LLMs — Voluntary generation of tricky borderline or quick cases to detect faults and lead to safer behaviors

Customer supportT — Design of games of varied interactions to be classified to guide the tone and relevance of responses

2d image illustrating a pre-validated dataset, used as an illustration for Supervised Fine Tuning (SFT)

Supervised fine-tuning

Refine a language model using a dataset containing prompt and high quality responses, validated or reviewed by humans. Allows you to specialize a model on a specific field, to improve the quality of its answers, or to correct certain undesirable behaviors.

⚙️ Process steps:

Definition of the target domain or the behaviors to be adjusted

Creation or selection of a corpus of annotated examples (prompt + validated response)

Cleaning, normalizing and structuring the data set (JSONL format)

Verification by human reviewers to ensure the quality of the corpus

🧪 Practical applications:

Specialized health or pharmaceutical models — Training based on answers validated by professionals

Business chatbots — Fine-tuning with pre-written dialogues for a given sector (banking, HR, insurance...)

Multilingual fine-tuning — Adjustment of the model to languages that are not well covered thanks to supervised bilingual corpora

Image illustrating LLM benchmark, with logos of ChatGPT, Mistral, DeepSeek or Gemini and other Foundational models

Comparing generative models

Test several models (or variants of the same model) on identical prompts, then to assess their responses according to qualitative and quantitative criteria. To identify which model is the most suitable for a given use case, or to measure the gains of fine-tuning.

⚙️ Process steps:

Selection of a panel of prompts covering several use cases or typical scenarios

Generation of responses from different models (e.g.: base vs fine-tuned, GPT vs Mistral)

Human annotation of responses according to defined criteria

Rating or scoring of answers (pairwise, best-of, rating scale)

🧪 Practical applications:

Post-fine-tuning assessment — Check if a model refined on specific data outperforms its basic version

Multi-model benchmark — Compare several open source LLMs (LLama, Mistral, DeepSeek,...) on target tasks

Assessment for audit or compliance — Document the behaviors of a model to meet regulatory requirements

Compliance ethics

We moderate the content generated by your AIs to reinforce its quality, security and relevance, thanks to human and technological expertise adapted to each sector. In this way, you increase the impact of your models while controlling risks.

Image with a magnifying glass on a dataset, to illustrate audit of datasets or audit of training data

Training dataset audit

Analyze in depth a data set intended to train an AI model, in order to assess the quality, representativeness, structure, potential biases, and legal or ethical risks. To ensure that the foundations of the model are sound, reliable, and aligned with business and regulatory goals.

⚙️ Process steps:

Analysis of the overall structure of the dataset

Detection of biases or imbalances

Identification of sensitive or risky content

Assessment of diversity and thematic coverage

🧪 Practical applications:

Regulatory compliance (AI Act, RGPD...) — Verification that the dataset complies with transparency and ethical obligations

Algorithmic bias prevention — Identification of sources of injustice or unbalanced representations in the data

Assessing the robustness of the data — Analyze whether the dataset covers critical or sensitive cases

Image of a text with a small purple warning sign... to illustrate biases in AI-generated content

Detecting biases in content

Spot the imbalances, stereotypes or problematic representations present in the data used to train or test AI models. For prevent discrimination, ensure a ethical use of models and meet compliance requirements.

⚙️ Process steps:

Define the types of biases to monitor

Human annotation of problematic or ambiguous cases

Statistical evaluation of imbalances between categories or classes

Corrective recommendations (cleaning, balancing, exclusion, reformulation)

🧪 Practical applications:

Educational evaluation of generative models — Verification of the fairness of the answers in educational or academic cases

Preparing equity test games — Construction of scenarios to test the robustness of models in the face of biases

Blocking or reformulating risky content — Filtering generated outputs with implicit biases

Image of a text with an all-seeing-eye on top of it, to illustrate the monitoring of AI generated data

Monitoring AI-generated data

Set up a process of human or semi-automated control content produced by generative models (text, image, audio,...), in order to detect Inappropriate slip-ups, errors, biases, or content. For prevent reputational, legal or ethical risks.

⚙️ Process steps:

Definition of surveillance rules and criteria (thematic, linguistic, ethical, etc.)

Extraction of representative samples or real-time monitoring of generated outputs

Human or automated analysis of AI responses (via scoring tools, alerts, reports)

Annotation of problem cases (hallucinations, toxic remarks, inaccuracies, stereotypes...)

🧪 Practical applications:

Supervision of consumer chatbots — Continuous monitoring of responses to avoid inappropriate or offensive remarks

Monitoring of models in production — Verification that the answers remain consistent over time despite changes in use

Detection of sensitive or viral contents — Identification of potentially polarizing or problematic responses

Image of a form with a small icon with different people... to illustrate diversity within data and datasets

Data diversity check

Analyze a training or test data set to ensure it covers a sufficient variety of themes, styles, languages, profiles, or viewpoints. To guarantee the robustness, inclusiveness and generalizable performance AI models.

⚙️ Process steps:

Definition of the expected diversity criteria

Statistical and qualitative analysis of the dataset according to these criteria

Detection of imbalances or gaps (e.g. gender bias, lack of cultural variations, homogenous tone)

Enrichment recommendations (data addition, rebalancing, larger sampling)

🧪 Practical applications:

Preparation of multilingual or multicultural datasets — Ensure that each language or culture is fairly represented

Training specialized models (health, education, etc.) — Verification that the profiles of patients, students or users are varied

Regulatory compliance (AI Act, diversity & inclusion) — Provide proof of verification work on the representativeness of the data

Image of a form with a huge checkbox in a circle, to illustrate manual validation of data

Manual data validation

Involve human annotators or reviewers to check, correct, or confirm the quality of textual, audio, visual, or tabular data, before or after their use by an AI model. For make the training games, the benchmarks or the outputs generated more reliable.

⚙️ Process steps:

Selection of data to be validated (random, critical, from an automatic pipeline, etc.)

Definition of validation criteria (accuracy, format, format, clarity, completeness, alignment,...)

Human review or verification via annotation interface or control panel

Correction of identified errors or inconsistencies (faults, entities, formats, AI responses...)

🧪 Practical applications:

Correction of OCR or automatically transcribed datasets — Human review to make the extracted data reliable

Validating multilingual audio transcripts — Verification by native speaker or linguistic expert

Quality control on test games — Elimination of biases or errors in evaluation benchmarks

Image of a form with an "Information" ("i") sign at the bottom right. This is to illustrate documentation of datasets

Dataset documentation

Provide a clear, complete and structured description the origin, content, characteristics, and limitations of a dataset intended for training or evaluating an AI model. This step is essential to guarantee the transparency, reusability, and compliance data used in a project.

⚙️ Process steps:

Collection of information on the origin of the data (source, collection method, licenses, consent...)

Description of the characteristics of the dataset: data type, size, size, formats, formats, languages, classes, balance, anonymization...

Identification of usage goals (training, testing, fine-tuning, etc.) and reporting potential biases or limitations

Structured writing of documentation (e.g. datasheet, model card, AI inventory sheet)

🧪 Practical applications:

Regulatory compliance (AI Act, RGPD, DSA) — Provide formal and traceable documentation of the data used

Transparency in public or sensitive AI projects — Explain what a dataset contains and why it was chosen

Facilitating the reuse of internal datasets — Clear transmission of proprietary (company-specific) or Open Source datasets to data or AI teams

Moderation contextual

We moderate the content generated by your AIs to reinforce its quality, security and relevance, thanks to human and technological expertise adapted to each sector. In this way, you increase the impact of your models while controlling risks.

Image of a form with various domains on it (medicine, finance, law) - to illustrate domain annotation by experts

Moderation in health, finance, law

Annotate, filter, and validate AI-generated data or responses to avoid factual error, misinterpretation, or risky recommendation. In these regulated contexts, the presence of human supervision is essential to guarantee the compliance, reliability, and security AI systems.

⚙️ Process steps:

Definition of business rules and acceptability thresholds (tone, terminology, accuracy, etc.)

Proofreading and human validation by annotators trained in sectoral issues

Reporting or rephrasing non-compliant or ambiguous content

Traceability of human interventions for audit, compliance and continuous improvement

🧪 Practical applications:

Finance — Verification of content related to taxation, investments or banking regulations

Law — Control of responses generated by legal models (clause, case law, advice) to avoid confusion or misinformation

Health — Human validation of AI responses in medical chatbots or clinical assistants

Image of a text / content, with a big "No entry" sign. This is to illustrate content filtering by human annotators

Content filtering

Identify, isolate, or delete the contents (training data or generated outputs) that are inappropriate, irrelevant, sensitive, illegal, or harmful to the quality or compliance of an AI project. It can be textual, audio, visual, or multimodal data. Filtering can be automated, manual, or hybrid.

⚙️ Process steps:

Definition of filtering rules: forbidden topics, sensitive comments, language level, noise, duplicates, etc.

Human intervention for validation

Labeling rejected content

Updating rules and thresholds according to regulatory or business developments

🧪 Practical applications:

Preparing training datasets — Elimination of toxic, fuzzy, redundant or irrelevant examples

Targeting sectoral corpora — Removal of data that is not relevant for training a specialized model (health, finance, etc.)

Blocking NSFW or sensitive content — Exclusion of content that does not conform to the final use of the model

An image with a person icon and workflows. To illustrate business workflows in the context of AI data preparation

Business workflows

Contextualizing human interventions in content production or decision workflows, in order to ensure business relevance, data quality, and compliance integrated AI systems.

⚙️ Process steps:

Definition of human roles in the loop: validation, filtering, enrichment, reformulation,...

Creation of customized workflows with escalations, trade-offs or trust thresholds

Integration into internal tools

Performance monitoring and continuous adaptation of the human role in the AI-augmented process

🧪 Practical applications:

Legaltech — Workflow for double validation on legal clauses or recommendations proposed by IA

Documentary processing — Inclusion of reviewers in the OCR pipeline + data extraction for audit or contract

E-commerce & marketing — Enrichment or manual adaptation of AI descriptions according to ranges or brands

Image with an icon from a person speaking, and a thumb down.. to illustrate human review of content AI or non-AI generated

Human review of AI conversations

Manually review dialogues generated by virtual assistants, chatbots, or LLM models in order to correct errors, identify inconsistencies or detect risks of slip-ups.

⚙️ Process steps:

Analysis of the conversational logic, the relevance of the answers and the respect of the instructions

Annotation of identified errors: hallucination, inadequate tone, confusion, broken thread...

Correction or reformulation suggestion (if post-treatment or active supervision)

Feedback to AI teams or integration of corrections into learning games

🧪 Practical applications:

Customer support — Reviewing AI dialogues with users to ensure clarity, politeness and efficiency

Education and e-learning — Review of AI exchanges to ensure pedagogical accuracy and language level

Deploying new AI agents — Systematic human QA phases before production

Image of a text form with a star (illustrating ranking / qualification) and a checkbox

Content qualification

Assign metadata, labels, or ratings to content collected or generated (texts, images, audio extracts, videos) in order to make usable in an AI pipeline: training, filtering, prioritizing, or moderating.

⚙️ Process steps:

Definition of qualification criteria (relevance, theme, language level, sound quality, etc.)

Human review of raw or generated content (visual, textual, audio...)

Assigning metadata or labels (e.g. trust level, theme, tone, intent, technical quality)

Reporting unusable or problematic content (e.g. noise, empty, off-topic, sensitive content)

🧪 Practical applications:

Preparing datasets for LLMs fine-tuning — Qualification of quick/response pairs according to their clarity or training value

Conversational analysis — Attribution of labels to AI dialogues: objective achieved, ambiguous response, consistent style...

Curation of data collected on the web or in business — Human sorting to keep only usable data

Image with a skull and a tickbox on a paperform. This is to illustrate data moderation or preparation in the context of red teaming or AI red teaming

Content annotation for AI Red Teaming

identify, classify and document potential flaws or undesirable behaviors of AI models, by structuring critical cases to assess and strengthen their robustness and security

⚙️ Process steps:

Definition of critical test scenarios (malicious prompts, ambiguities, workarounds, adversarial prompts)

Generation of content by the AI model in these targeted scenarios

Human review and annotation of risky behaviors (hallucinations, illegal responses, explicit or implicit biases, circumvention of instructions, etc.)

Qualification of the severity and type of vulnerability detected (toxicity, security, reputation, compliance)

🧪 Practical applications:

Assessment of the robustness of a model before production — Offensive tests simulated by specialized annotators

Ethical benchmark — Measuring the sensitivity of a model to certain types of prompts or sensitive contexts

Building Red Team test games — Creation of robust evaluation corpora from annotated outputs

Use cases

Our expertise covers a wide range of AI use cases, regardless of the domain or the complexity of the data. Here are a few examples:

1/3

🛡️ Content moderation on social networks

Automatic identification of hate speech, spam, or inappropriate content to ensure a safe online space.

📦 Dataset : Corpus of publications or comments extracted from social networks, annotated manually according to different categories (toxic, insulting, explicit, spam, etc.). Annotations often include labels of severity, context (humor, irony), and can combine multiple languages and language registers.

2/3

🧠 Human preference training (RLHF) for AI assistants

Fine-tuning an LLM according to human preferences on the quality of the responses generated, in order to align the model with user expectations.

📦 Dataset : Pairs or series of responses generated by the model on the same query, scored by humans according to criteria such as relevance, clarity, security or politeness. This data is used to train a reward model used in the RLHF pipeline.

3/3

👮 Content filtering in video games or forums

Detecting inappropriate behaviors in in-game chats or community forums to maintain a healthy environment.

📦 Dataset : Chat logs and snippets of conversations from games or community platforms, annotated to indicate rule violations (harassment, cheating, spoilers, etc.). Data may include contextual elements such as the name of the game, the phase of the game, or the role of the players.

Image of a dataset, in the context of the use case described

Why choose
Innovatiana?

Ask us for a quote

A team of experts dedicated to content moderation and optimization of AI models through reinforcement learning based on human feedback (RLHF). For your content moderation projects: data filtering, evaluating the quality of responses or aligning with human values

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)

Ask us for a quote
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🔍 We study your needs

We offer you tailor-made support taking into account your constraints and deadlines. We offer advice on your certification process and infrastructure, the number of professionals required according to your needs or the nature of the annotations to be preferred.

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🤝 We reach an agreement

Within 48 hours, we assess your needs and carry out a test if necessary, in order to offer you a contract adapted to your challenges. We do not lock down the service: no monthly subscription, no commitment. We charge per project!

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💻 Our Data Labelers process 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.

You are testifying

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.

Karen Smiley

AI Ethicist

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!

Henri Rion

Co-Founder, Renewind

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,...).

Tim Keynes

Chief Technology Officer, Fluximmo

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.

Dan D.

Data Scientist and Neurosurgeon, Children's National

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!

Louise Block

Accelerator Program Coordinator, Singa

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!

Sumit Singh

Product Manager, Labellerr

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.

Klein Blue Team

Klein Blue, platform for innovation and CSR strategies

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.

Kasper Lauridsen

AI & Data Consultant, Solteq Utility Consulting

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.

Bill Heffelfinger

CVAT, CEO (2023-2024)

Conceptual illustration showing a blindfolded figure holding scales of justice alongside an AI logo, symbolizing Innovatiana’s commitment to ethical and responsible artificial intelligence

🤝 Ethics is the cornerstone of our values

Many data labeling companies operate with questionable practices in low-income countries. We offer an ethical and impacting alternative.

Learn more

Stable and fair jobs, with total transparency on where the data comes from

A team of Data Labelers trained, fairly paid and supported in its evolution

Flexible pricing by task or project, with no hidden costs or commitments

Virtuous development in Madagascar (and elsewhere) through training and local investment

Maximum protection of your sensitive data according to the best standards

The acceleration of global ethical AI thanks to dedicated teams

🔍 AI starts with data

Before training your AI, the real workload is designing the right dataset. Find out below how to build a robust POC by aligning quality data, adapted model architecture, and optimized computing resources.

✨ Ideation of a use case

Have you identified a use case where AI can provide an innovative solution? We prepare your data. We work to:

🤝 Collaborate with your teams to understand data needs as well as the types of data (structured, unstructured, images, videos, texts, audio, multimodal,...) required.

🧩 Design custom annotation schemes (data and metadata) and select tooling.

👥 Evaluate the workload and staffing required to create a complete dataset.

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⚙️ Data processing

Data processing includes collecting, preparing, and annotating training data for artificial intelligence. We work to:

📡 Search and aggregate raw data from a variety of sources (images, videos, text, audio, etc.).

🏷️ Annotate data, applying advanced data labeling techniques to create datasets ready for training.

🧪 Generate artificial data to complete data sets in cases where real data is insufficient... or sensitive.

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🤖 AI model training and iteration

This step includes setting up and training the AI model, based on the prepared data. We work with your Data Scientists to adjust the data sets:

🔧 Rework datasets and metadata, labels or source data.

📈 Quickly integrate feedback by updating the “Ground Truth” datasets.

🎯 Prepare new targeted data to improve the robustness of the system.

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Feed your AI models with high-quality training data!

👉 Ask us for a quote
En cliquant sur "Accepter ", vous acceptez que des cookies soient stockés sur votre appareil afin d'améliorer la navigation sur le site, d'analyser son utilisation et de contribuer à nos efforts de marketing. Consultez notre politique de confidentialité pour plus d'informations.