AI Training for Generative Models
Feed your generative models with training data designed to perform. We create tailor-made datasets for the fine-tuning of your LLMs, to improve the quality of the responses generated and to reinforce the relevance of your AI-based systems
Our AI Trainers select, generate, structure your data, and annotate your assets precisely in order to optimize their quality for the fine-tuning of your generative models
Datasets & annotation
Fine tuning & optimization
Content creation
Classification & prioritization
Datasets and annotation
We transform your linguistic data into strategic resources for generative models, thanks to human and technological expertise adapted to each field.
Creating datasets
Collect and structure optimized data to effectively train your generative models. For example, these custom datasets make it possible to refine open source models such as Mistral, LLama, or Gemma.
Definition of business goals and use cases
Selection or generation of relevant data (texts, images, videos, etc.)
Structuring in a format compatible with AI frameworks
Human validation and performance evaluation on test games
Health - Constitution of medical corpora for automated diagnosis
Software development - Preparation of technical corpora for programming assistant (LLM)
Customer support - Training of multilingual chatbots specialized by sector of activity.
Data augmentation
Generate new variants of your existing data in order to expand, diversify, and strengthen the training sets for your generative models. This approach makes it possible to improve the robustness, generalization, and performance of the models, even with a limited initial volume of data.
Analysis of original data and identification of gaps
Selection of appropriate augmentation techniques (paraphrases, permutations, synthesis, multimodal mix...)
Manual or semi-automatic validation to ensure quality and consistency
Integration into the global dataset for fine-tuning
Health — Generation of variants of doctor-patient dialogues to train conversational diagnostic LLMs
Object detection — Image transformation (angles, contexts, noise) to refine VLMs in complex environments
Education — Creation of alternative exercises or educational content for generative models of academic support
Synthetic data
Artificially generate realistic data (texts, images, dialogues, documents, etc.) in order to enrich a dataset, fill gaps or simulate rare cases, while perfectly controlling the quality and diversity of the content produced.
Identification of specific needs or areas of scarcity in real data
Controlled generation of synthetic data via LLM, VLM or specific generative models
Human review and content adjustment to avoid biases, inconsistencies or hallucinations
Integration into the global dataset with annotation and quality validation
Software development — Creation of tickets, logs or code snippets to simulate rare use cases in programming assistance
Finance — Production of synthetic transaction scenarios to train an anomaly detection model
Customer support — Creation of realistic dialogues in different business contexts to strengthen the performance of AI chatbots
Text annotation
To enrich raw data with metadata (categories, entities, entities, relationships, intentions...) to make training games usable by generative AI models.
Selection of suitable tools: Prodigy, UbiaI, Label Studio, etc.
Manual or AI-assisted annotation of text data
Proofreading, cross-validation and harmonization
Export in compatible formats (JSON, CSV, XML, etc.) for integration into the training pipeline
Real Estate — Annotation of key characteristics in ads to improve natural language search or generate automatic summaries.
Call Center — Annotation of intentions and feelings in call transcripts to train customer support or conversation synthesis LLMs.
E-commerce — Annotation of product attributes in description sheets to improve AI-assisted search or automatic content generation
Visual annotation
Identify, frame or segment elements present in images or videos in order to make the data usable for training computer vision or multimodal models.
Definition of the annotation schema in relation to the AI objectives (bounding boxes, segmentation, keypoints, classification,...)
Getting started with tools and calibrating instructions between annotators
Manual or assisted annotation, with cross-checking
Quality control, harmonization, export of ready-to-use data (COCO, YOLO, Pascal VOC...)
Urban mobility — Annotation of pedestrians, vehicles and signs in embedded videos for autonomous driving models
Agriculture — Detection of diseases or growth stages on crop images for automated monitoring
Health — Annotation of anatomical structures on MRIs or X-rays to train diagnostic aid models
Audio annotation & transcription
Transform audio data into structured text, while identifying the speakers, intentions, or entities mentioned.
Manual or AI-assisted transcription of audio files (human voice, calls, dialogues...)
Annotating entities, emotions, intentions, or interruptions (depending on AI goals)
Human review to ensure fidelity to the original audio and compliance with the expected format
Structuring and exporting data for training or evaluating models
Customer service — Annotation of intentions and tones in telephone conversations to improve voice assistants or chatbots
Media — Multilingual transcription of interviews or podcasts for automatic generation of summaries or translation
Education — Creation of audio-text datasets for training subtitling or speech analysis models
Datasets for LLM fine-tuning
We transform your linguistic data into strategic resources for generative models, thanks to human and technological expertise adapted to each field.
Dataset for LLM
Collecting, structuring, and enriching large quantities of textual data in order to train or adjust language models. These data sets must be representative of the targeted uses, clean, diverse and contextualized, with rigorous quality and bias control.
Definition of AI goals (task, domain, languages, tone, etc.)
Research or production of relevant textual data (documents, dialogues, technical corpus, etc.)
Cleaning, normalizing, and structuring data into instruction/response, documents, strings, or tokenizable formats
Semantic annotation or enrichment with metadata (intent, entities, style, etc.)
Software development — Training of programming assistants on documented technical bases
Education — Generation of structured educational datasets for tutorials, quizzes, summaries, etc.
Health — Corpus of doctor-patient dialogues for specialized LLMs
Dataset for RAG
Structuring documentary databases usable by an AI search engine, combined with an LLM. These datasets should be reliable, well-segmented, rich in metadata and designed to promote accurate, traceable and contextualized responses.
Collection and selection of source documents (PDF, internal databases, FAQ, reports, manuals...)
Logical segmentation into passages (chunking), according to the context and the desired granularity
Cleaning and structuring of textual content to avoid duplicates or semantic noise
Addition of key metadata (title, source, category, category, category, language, date, etc.) to facilitate research scoring
In-house support — Indexing HR, IT, finance documents for business AI assistants
Juridical — Structuring case law or legal texts for an intelligent search engine
Technical support — Constitution of article + log databases for technical conversational agents
Prompt Engineering
Create Datasets Structured “prompt/response” to train, adjust, or evaluate language models (LLM). These datasets make it possible to simulate precise interactions, to transmit business knowledge or to improve the consistency and quality of AI responses.
Manual or assisted writing of realistic prompts, representative of the target domain
Generation or human writing of answers, according to quality standards (length, structure, tone, accuracy)
Proofreading, semantic validation and detection of biases or inconsistencies
Structuring and exporting to JSONL format or other format compatible with fine-tuning or evaluation
Test & evaluation — Generation of prompt “traps” to validate robustness or detect hallucinations
Multilingual/tone — Data sets with variations in style, register, or language to make the model more adaptable
Supervised learning — Annotated prompt datasets to assess or guide the behavior of a model
Quality control
To ensure that the data used for training or evaluating an LLM is accurate, consistent, diversified and without major bias.
Definition of quality criteria (accuracy, clarity, tone, format, compliance with instructions)
Human pair review Prompt/response to detect errors, inconsistencies, or duplications
Checking the lexical, stylistic and semantic diversity of prompts
Detecting and removing sensitive biases, inappropriate content, or outdated information
Fine-tuning LLM — Make tuning instruction data reliable to avoid unwanted effects
Model evaluation — Guarantee the neutrality and robustness of test sets for benchmark
Business compliance — Verify that the responses generated respect sectoral constraints (legal, health, HR...)
Bias assessment
Identify and document linguistic, cultural, social, or cognitive biases present in the datasets used to train an LLM. This step allows you to limit excesses, to improve the fairness of the model and to ensure better ethical and regulatory compliance.
Definition of the types of biases to monitor (gender, origin, origin, opinion, representation, register, etc.)
Identification of thematic imbalances or discriminating formulations
Annotating or reporting sensitive occurrences by trained human reviewers
Generating bias reports and recommendations to adjust or rebalance data
AI ethics — Detection of systemic biases before fine-tuning or production
AI dialogue — Prevention of stereotyped or inappropriate responses in voice assistants or chatbots
Linguistic diversity — Assessment of cultural or linguistic biases in multilingual datasets
AI fact-checking
Verify the veracity and reliability of the responses generated by an LLM, by comparing them to reference sources. To detect hallucinations in model development, or to add a layer of human supervision to moderate the data generated.
Manual or assisted verification (LLM, external tool) of the factual nature of the generated content
Crossing with reliable sources (business databases, internal documents, encyclopedias, up-to-date articles...)
Annotation of the level of truth (exact, partially accurate, false, invented...)
Structuring results to enrich data sets or feed robust test sets
Networks & Media — Detection of hallucinations or erroneous content in sensitive cases
Assessment datasets — Composition of tested and rated games for the benchmark of generative models
Fine tuning — Improvement of responses generated through supervised truth games
Creation of contents
We transform your linguistic data into strategic resources for generative models, thanks to human and technological expertise adapted to each field.
Multilingual writing
Produce training or test data in multiple languages to improve abilities Polyglots language models. Datasets to train or evaluate an LLM in international or multi-regional use cases, while guaranteeing the semantic and stylistic coherence between languages.
Definition of target languages and contexts of use (formal, technical, conversational...)
Manual writing or translation of prompts and responses by native or specialized annotators
Linguistic quality control (grammar, tone, cultural adaptation, terminology)
Export in structured multilingual format (JSONL, TSV, CSV with columns by language...)
Multilingual chatbots — Training of models able to understand and respond in several languages
Product documentation — Creation of multilingual instruction or customer support databases
Cross-language semantic analysis — Robustness tests on maintaining meaning across multiple languages
Specialized content
Create datasets aligned with specific sector (health, law, finance, energy, etc.) to train or adjust language models on vocabulary, structures and specific business contexts. The objective is to guarantee relevant, credible responses that are adapted to concrete use cases.
Identification of the business domain and target use cases (Q/A, generation, summary, etc.)
Writing prompts and responses by experts or writers trained in business terminology
Integration of reference documents (reports, notes, documentation, internal guides...)
Content annotation or enrichment (entities, themes, intentions, etc.)
Juridical — Generation or reformulation of clauses, responses to simulated legal cases
Finance — Training in the generation of analysis summaries, regulatory responses
Health — Creation of doctor-patient dialogues, synthesis of medical reports
Technical content
To train or adjust an LLM on complex subjects with high information density (computer science, engineering, cybersecurity, cloud, etc.). These datasets are structured to reflect the editorial standards and business vocabulary used in real technical environments.
Definition of the technical scope
Writing prompts and responses based on technical documentation
Content structuring
Technical accuracy check by qualified reviewers or experts in the field
Development assistants — Creation of prompts/responses to help with code, debug, explanation
Cybersecurity — Datasets for analyzing vulnerabilities or best practices in computer security
Modeling & engineering — Generation of content linked to technical or industrial systems
Instructions & prompts
Writing clear, structured and contextualized instructions for training or evaluating language models (LLM, conversational agents, AI assistants).
👉Useful for tuning instruction datasets
Definition of the types of instructions (e.g.: explanatory, task to be carried out, direct question...)
Manual writing of various prompts (domains, styles, levels of complexity)
Generation or human writing of the expected answers (informative, synthetic, guided...)
Structuring data in format instruction + output (e.g.: JSONL, TSV) for tuning instruction
Supervised training — Composition of pairs for fine-tuning or RLHF
Business specialization — Formulation of instructions aligned with specific tasks (HR, IT, legal...)
Prompt base — Creation of a library of typed and reusable prompts
Simulated dialog
To train models to interact naturally in multi-turn conversations. Each exchange is structured to reflect a realistic scenario (customer, patient, user...), with well-defined roles and consistent responses over time.
👉Great for chatbots, voice assistants, or AI agents
Definition of dialogue scenarios (assistance, simulation, advice, support...)
Writing multi-turn conversations between two or more roles (user/AI, expert/customer, etc.)
Verifying transitions, clarity of responses, and intent of requests
Structured export in format posts (e.g.: JSONL, OpenAI chat format, Markdown...)
Business chatbots — Training dialogues adapted to specific sectors (health, insurance, tech...)
Behavioral tests — Creation of evaluation games to check the maintenance of the context over time
Transcription & reformulation — Reconstitution of dialogues inspired by calls or tickets
Paraphrases & reformulations
Generation of content variants to enrich linguistic diversity and improve the robustness of models
👉 Useful for classification, intent detection, or controlled generation
Selection or creation of phrases/sources to be reformulated (questions, answers, instructions, texts...)
Manual or assisted writing of alternatives (similar paraphrases, stylistic or structural reformulations)
Classification by type of reformulation (simple, enriched, condensed, tone/formality, etc.)
Structuring data in format input/reformulation (JSONL, CSV, aligned pairs...)
Semantic search — Increase in user requests with varied formulations
Varied generation — Enrichment of the output of a model with several formulations
Education & languages — Paraphrase for vocabulary learning or academic reformulation
Grading and prioritization
We transform your linguistic data into strategic resources for generative models, thanks to human and technological expertise adapted to each field.
AI output ranking
CoMapping several responses generated by a model (or several models) from the same prompt, in order to determine which one is the most relevant, clear, useful, or aligned with expectations. For the supervised fine-tuning (SFT), the ranking preferential or intermodel evaluation.
Definition of ranking criteria (relevance, accuracy, tone, conciseness...)
Human preference annotation (pairwise or full ranking)
Calculating metrics to identify the best behavior
Structuring the results to feed a dataset of Supervised ranking (e.g. for RLHF)
Preferential fine-tuning — Train a model to favor certain answers in a given context
Comparison of models — Identify the most efficient version based on real use cases
RLHF — Data creation for reinforcement training via human feedback
User preferences
To guide AI models to responses perceived as more useful, appropriate, or engaging by end users. This makes it possible to adapt a model to a specific context, a response style Or a Expectation of work, by going beyond simple factual information.
Definition of user profiles or scenarios (level of expertise, preferred tone, expected format...)
Collection or simulation of user feedback on responses generated (ratings, comments, rankings)
Annotation of preferences in relation to attributes (form, clarity, usability, nuance...)
Exploitation for training or readjusting models according to targeted expectations
Business areas — Alignment of responses with industry practices or standards
Conversational personalization — Adapt the tone or structure according to user profiles
AI education/tutoring — Generate explanations adapted to the learner's level
Contextual prioritization
To train or adjust an LLM to prioritize the information generated According to the context of use, the intention of the user or the criticality of the elements. The aim is to avoid generic responses and to ensure that the model highlights what matters most in each situation.
Definition of use cases with implicit priority rules (e.g.: security, urgency, clarity, summary,...)
Creation of contextualized prompts and outputs to be classified or annotated according to their priority relevance
Annotation of key elements to highlight in the response (tags, labels, segments)
Structuring data into prompts + prioritized or annotated responses for prioritization
Business agents — Models capable of adapting to the user objective in real time
Legal background — Prioritization of key clauses or restrictive conditions
Customer support — Responses oriented to rapid action or direct problem solving
Validation of generated data
To ensure that the answers or content produced by an LLM are consistent, compliant, comprehensive, and actionable according to the defined objectives.
Human or assisted proofreading (secondary AI) to evaluate each output generated
Annotating errors, inconsistencies, ambiguous or biased formulations
Output classification: valid/to be corrected/to be rejected
Creation of a validated or enriched dataset with statuses and comments that can be used for training
Content generation — Validate AI texts before publication or customer use
Reduction in hallucinations — Detect and filter erroneous or invented content
Business quality — Ensure that AI outputs respect the standards of a specific field
Optimizing results manually
Reformulate, correct, or enrich AI-generated responses to achieve a higher level of quality, clarity, or relevance. To constitute premium sample datasets, refine a model, and improve the end user experience.
Selection of generated responses to be optimized (from an AI model or pipeline)
Human revision to improve structure, precision, tone, or completeness
Application of specific instructions (shorten, clarify, structure, reformulate...)
Recording before-and-after pairs for supervised training or sample database
Educational corpora — Manual rewriting to create excellent instruction sets
Comparative trainingF — Use of corrected versions to improve the robustness of the model
Targeted quality improvement — Manually catch up with the limits of an LLM in certain cases
Continuous optimization
To improve the performance of a language model over time, by exploiting user feedback, errors observed and cases not covered. This agile approach makes it possible to maintain a high level of relevance and to adapt the model to changes in the business context or data.
Regular feedback collection (users, human evaluation, performance metrics)
Progressive enrichment of the dataset with new examples, counterexamples, reformulations, etc.
Production of targeted datasets for retraining
Quality monitoring
Increase in specialization — Progressive strengthening of the capacities of a model in a given field
Continued supervised learning — Recurring addition of annotated examples with high added value
Agile training loop — Continuous integration of new data into the AI pipeline
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:
Why choose
Innovatiana?
We put at your disposal a scalable team of experts, specialized in the creation and validation of data for generative AI. For your LLMs, VLMs, chatbots and RAG systems
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)
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.
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!
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.
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.
🤝 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.
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 to design the right dataset. Find out below how to build a robust POC by aligning quality data, adapted model architecture, and optimized computing resources.
Feed your AI models with high-quality, expertly crafted training data!
