Natural Language Processing
Optimize your NLP models by transforming your documents into usable data. Thanks to rigorous processing and tailor-made annotation, we structure, extract and enrich your textual content to reveal their full potential for AI
Our team transforms your text content thanks to fine linguistic annotation and advanced NLP tools. For reliable data ready to train your artificial intelligence models
Text annotation
Audio annotation
Multilingual translation
Complex language processing
Annotation of text
We transform your textual data into strategic resources thanks to human and technological expertise adapted to each sector.
Semantic labeling and NER
THEsemantic labeling (Semantic Tagging) and the recognition of named entities (NER, Named Entity Recognition) allow you to automatically or manually annotate elements such as names of people, places, organizations, dates, quantities, products, organizations, dates, quantities... in raw texts.
Define the types of entities to be extracted according to business or AI objectives
Upload documents into a suitable annotation tool (e.g.: Prodigy, Doccano, Label Studio)
Manually annotate entities with precision and semantic consistency
Export data for training, fine-tuning or information research
Scientific publications — Extract the names of molecules, pathologies, researchers or methods
Legal files — Identify clauses, stakeholders, dates and locations in contracts
Real Estate — Identify information about real estate in ads published online
Text classification
Assign to each document, paragraph, or sentence one or more thematic, functional, or emotional labels, in order to structure a corpus or to train a prediction model. It makes it possible to organize unstructured content on a large scale for various use cases: automatic filtering, moderation, customer support, sector monitoring, etc.
Define a taxonomy of classes (e.g. themes, intents, priority levels, tones...)
Manually annotate each item with one or more classes
Structure data for supervised training (format: CSV, JSON, TSV...)
Export a balanced and ready-to-use NLP dataset
Content moderation — Detect risky texts (spam, hate, uncharted) in social platforms
Competitive intelligence — Categorize articles or user feedback by subject or tone
Customer support — Automatically classify tickets according to their nature (billing, technical, information request...)
Grammatical and syntactic analysis
Annotate texts with information on the nature of words (POS tagging), relationships between terms (syntactic dependencies), and sometimes the more complex sentence structures (verbal nuclei, subordinate, etc.). These annotations are fundamental for development of models for translation, grammatical correction or advanced linguistic analysis.
Define the linguistic conventions to follow (tagsets, dependency types, annotation formats)
Annotate each word with its grammatical category (noun, verb, adjective...)
Validate the accuracy of the annotations through cross-proofreading
Export data in a usable format (Conll-u, JSON, XML)
Machine translation templates — Train systems capable of maintaining the correct syntactic structure
Writing assistants — Propose syntactic reformulations according to the desired level or register
AI grammatical correction — Detect style or sentence construction errors
Annotation of intentions and feelings
Identify the attitude, goal, or emotion conveyed by a text (or a sentence) in order to train models of contextual understanding, moderation, automated response, or personalized recommendation. It makes it possible to distinguish content positive, negative, neutral, but also the underlying intentions (request, complaint, thanks, suggestion...).
Define the categories of feelings (positive, negative, neutral...) or intentions (question, order, complaint...)
Manually annotate each segment with the corresponding label
Add metadata if necessary (tone, target of the emotion, degree of intensity...)
Export training-ready data in a structured format
Chatbots — Annotate the intentions in the messages to adapt the responses generated
Social network analysis — Detect opinion trends and weak signals on a large scale
Customer reviews — Identify the dominant emotions in user feedback
Multilingual annotation
Apply semantic, syntactic, or emotional annotations to contents in multiple languages, while respecting the linguistic, cultural and contextual specificities of each. It is essential for training robust multilingual models, used in applications such as machine translation, international voice assistants, or cross-language search engines.
Adapt annotation instructions according to each language (terminology, grammatical rules, typology of entities)
Assign tasks to native or specialized annotators by language
Validate the consistency of annotations between languages (alignment, coverage, interlinguistic coherence)
Export data in a format compatible with multilingual models (JSON, CSV, XML, CoNLL)
International chatbots — Create multilingual intent datasets for voice assistants
Supervised machine translation — Align semantic annotations to pairs of translated sentences
Multilingual corpus for LLM — Annotate entities and feelings in multiple languages for fine-tuning
LLM training data
Produce prompt and response pairs assembled into data sets in order to guide the learning or finetuning of generative models. This data plays a key role in behavior, accuracy, and safety of LLMs.
Write or collect prompts adapted to target use cases
Manually produce or validate consistent, relevant, and unbiased responses
Annotate additional information if necessary (quality, level, style, tone, context...)
Structure the dataset in a training format compatible with LLM frameworks (JSONL, YAML, CSV...)
Tuning instruction — Provide specific examples to train a model to follow instructions
Multilingual models — Build instruction sets and answers in multiple languages for fine-tuning
Personalized AI assistant — Create a body of business dialogue to adapt an LLM to a specific sector
Audio annotation
We transform your audio data into strategic resources thanks to human and technological expertise adapted to each sector.
Audio segmentation
Identify and delineate relevant portions of an audio recording, such as sentences, turns of speech, or silences. To facilitate the transcription, audio-text alignment, speech analysis, or speech recognition model training (ASR).
Load audio files into a suitable segmentation tool
Manually or automatically create segments by defining precise timstamps (start/end)
Annotate segments if necessary (type of content, speaker, quality,...)
Export segments or metadata in a compatible format (e.g., TextGrid, JSON, CSV)
Preparing for transcription — Facilitate the distribution of work into coherent blocks
Audio indexing — Delimit speeches for audio or video search engines
Voice recognition — Produce clean, aligned audio units for ASR training
Multilingual transcription
Listen to recordings in different languages (or dialects) and to transcribe them accurately into text, respecting the linguistic and cultural specificities of each language. To constitute reliable audio-text corpora, useful for training or evaluating models of Multilingual speech recognition (ASR) Or of natural language processing.
Segmenting the audio (silences, speaker changes, thematic division...)
Transcribe word for word, paying attention to punctuation, hesitation, and possible foreign words
Apply appropriate linguistic conventions (orthographic standards, dialects, phonetic transcription if required)
Export transcripts in a standardized format (TXT, CSV, JSON, XML...)
Multilingual corpora for ASR — Create audio-text games in several languages for model training
Conversational analysis — Transcribe multilingual calls for international customer services
Automatic voice translation — Produce quality transcripts before AI translation
Speech annotation
Add structured information to an audio recording, such as speaker changes, emotions, intentions, pauses, overlaps, or accentuations. It allows contextualize voice content for the analysis or training of AI models in speech recognition, NLP or emotion detection.
Segment audio into speech turns or thematic units
Identify speakers (anonymous or named) and tag them
Structure annotations with accurate timstamps and standardized categories
Export in standard voice annotation formats (TextGrid, ELAN XML, JSON)
Multi-speaker systems — Create voice recognition datasets per speaker
Voice assistants — Annotate emotions or intentions to refine the responses generated
Sociolinguistic studies — Identify the characteristics of speaking (intonation, breaks)
Audio classification
Assign one or more categories to audio files based on their content, whether it's musical genres, emotions expressed, types of noise or other specific criteria. It allowsorganize and use large amounts of audio data, in order to train recognition or filtering models.
Define relevant classes or categories (emotions, genres, events, background noise...)
Manually scan each file to assign the appropriate category (s)
Structure data in the form of tagged files (JSON, CSV, XML)
Export results in a compatible format for AI training or analysis
Customer call analysis — Detect the tone of exchanges to analyze satisfaction
Sound monitoring — Identify the types of noise in industrial or urban environments
Music recommendation systems — Sort songs by genre or ambiance for personalized suggestions
ASR data preparation
La ASR data preparation (Automatic Speech Recognition) consists in shaping audio recordings and their aligned transcripts so that they can be directly exploited by speech recognition models. It ensures that the data is clean, consistent, time-aligned and adapted to the expected format by ASR engines.
Segment audio into short, coherent units (sentences, turns of speech)
Clean and standardize associated transcripts (punctuation, spelling, standardization of entities)
Label useful metadata (language, audio quality, type of speaker...)
Export data in a standard format for ASR (ex.: JSONL, TSV, WAV + TXT, Kaldi, Whisper)
Adaptation to a specific field — Prepare specialized audio/text data (health, finance...)
ASR engine evaluation — Provide a structured test game with ground truth for performance calculation
Training speech recognition models — Create clean and complete corpora for AI training
Customized vocal corpora
Collect, structure, and annotate custom audio recordings, according to the specific needs of an artificial intelligence project: target language, accent, accent, business context, tone, background noise, etc. These datasets are designed to train or test speech recognition, transcription, or oral comprehension models, with total control over their quality and diversity.
Define the specifications of the corpus (languages, dialects, domains, scenarios, formats...)
Organize or supervise audio collection (studio, telephone, field recording...)
Annotate associated metadata (speaker, quality, context, noise...)
Deliver a corpus ready for training in a structured and documented format
Autonomous driving: Detection and tracking of vehicles, pedestrians and cyclists
E-commerce: Localization of products for inventory automation
Oversight: Tracking movements in public environments
Translation multilingual
We transform your linguistic data into strategic resources thanks to human and technological expertise adapted to each sector.
Multilingual annotation
Enrich translated or native texts in several languages with linguistic, semantic or functional tags, while respecting the cultural and grammatical specificities of each language. To train models for translation, multilingual generation, or interlingual comprehension.
Define the types of annotation required (entities, emotions, intentions, grammatical structure...)
Annotate text segments according to linguistic guidelines specific to each language
Check the interlanguage consistency, alignment, and quality of annotations
Export annotated datasets in a structured format (JSON, XML, ConLL...)
International dialogue systems — Prepare multilingual annotated dialogues for voice assistants
Multilingual corpora for LLM — Enrich texts with named entities or thematic categories in multiple languages
Supervised machine translation — Annotate segments to improve aligned learning
Validation of AI translations
Review, correct, and evaluate texts translated automatically (by AI engine) in order to guarantee their coherence, fidelity to the original meaning, fluidity and terminological conformity. To constitute high quality multilingual corpus, specialize translation models, or control automatic generation pipelines.
Compare source and target texts produced by AI (sentence to sentence or segment to segment)
Identify errors in meaning, style, grammar, or context
Mark borderline or ambiguous cases for future iterations
Export validated or corrected translations for production or retraining
Test corpus for NMT — Create a high quality ground truth to evaluate a translation engine
Regulatory or technical translations — Verify terminological compliance in sensitive areas
Multilingual AI services — Control automatically generated responses in different linguistic contexts
Cleaning and standardization
Filter, correct, and harmonize translated or aligned content in order to guarantee their linguistic quality, compatibility and consistency. To avoid biases, duplicates, format errors, or inconsistencies that can affect the performance of machine translation or multilingual generation models.
Detect and remove duplicates, empty lines, or corrupt segments
Correct typographical or format errors in source and target texts
Standardize punctuation, capitalization, abbreviations, and segmentation
Export cleaned corpora in a format ready for training (e.g.: TMX, JSONL, TSV)
Preparation of multilingual test games — Ensuring the clarity and consistency of assessment data
Standardization of multilingual content — Standardize translations from multiple sources
Machine translation engine training — Clean and structure parallel corpora
Specialized translation
Translating documents by mobilizing a business or sector expertise, in order to guarantee the terminological accuracy, regulatory compliance and stylistic consistency. To constitute quality corpus in complex fields, intended for the training or validation of AI models in demanding professional contexts.
Identify the field concerned (legal, medical, technical, financial...) and associated terminology
Select translators or annotators trained in the sector concerned
Annotate or tag technical terms, legal notices, or critical sections if needed
Export translated content in a structured format ready for use IA (e.g. JSON, XML, TMX)
Regulatory translation — Adapting contracts, policies or legal documents to different legal frameworks
Technical support systems — Translate FAQ's or specialized guides for virtual assistants
Corpus for medical AI — Translate and structure multilingual clinical reports or studies
Annotation: AI translation errors
Reread automatically generated translations and to Mark errors according to predefined categories (error in meaning, grammar, omission, tone, etc.). To constitute evaluation or fine-tuning data sets, and to provide targeted feedback to improve neural translation models (NMT).
Define an error annotation schema (types, severity, position...)
Mark the errors encountered and classify them according to their nature
Add comments or suggestions for critical cases
Export results in a structured format for analysis or retraining (JSON, CSV, XML)
NMT engine improvement — Identify the recurring weaknesses of an AI translation model
Annotated test corpora — Create evaluation datasets to benchmark multilingual systems
Supervised training — Provide faulty/corrected pairs to correct AI behaviors
Complex multilingual annotation
THEcomplex multilingual annotation goes beyond simple labelling, by integrating links between languages, levels of meaning, stylistic variations or phrase-by-sentence alignments, for applications of neural machine translation, multilingual generation, and semantic alignment. It requires specialized annotators who can work with several languages simultaneously, while respecting linguistic and contextual coherence.
Define annotation objectives (alignment, reformulation, semantic enrichment...)
Prepare multilingual pairs to annotate, with or without reference source text
Add metadata (type of variation, tone, register, fidelity to the message)
Export annotations in an interoperable format (JSONL, rich TMX, aligned TSV)
Multilingual LLM training — Provide complex translation examples with nuances and variants
Corpus for multilingual generation systems — Annotate style, order, or tone choices in translations
Alignment of interlanguage paraphrases — Link different formulations and idioms in multiple languages
Treatment complex linguistics
We transform your linguistic data into strategic resources thanks to human and technological expertise adapted to each sector.
Sentiment & emotion analysis
Annotate or extract emotional attitudes, judgments, or states expressed in text, audio, or video. This task goes beyond the simple positive/negative, and may include emotional nuances (joy, anger, frustration, irony, sarcasm,...)
Define the categories of feeling (positive, negative, neutral...) and emotions (anger, fear, joy, surprise...)
Manually annotate or validate the feelings and emotions expressed
Add levels of intensity or certainty as needed
Export in a compatible format (JSON, CSV, XML) for training or testing
Conversational models — Allow voice assistants to react to a user's emotional tone
Watch out for social networks — Follow the emotional dynamics related to a subject or a brand
Analysis of customer reviews — Detect the dominant emotions in product or service returns
Conversational models
Structuring, annotating, and enriching human dialogues, in order to train chatbots, virtual assistants, or LLMs to better understand contexts, sequences and intentions. This includes annotations specific to exchange dynamics : role of the speaker, type of intention, context break, reformulation, etc.
Collect or segment dialogues into speech turns or interactions
Annotate each message with the intention expressed (request, statement, question, refusal...)
Identify roles (user, agent, specific contact person)
Export structured data for training conversational models (JSON, YAML, CSV)
Chatbot training — Annotate dialogue scenarios to assist users in concrete cases
AI response models — Learn to manage the context of a long or multi-stakeholder exchange
Analysis of customer exchanges — Understand the reasons for dissatisfaction or recurring intentions
Multimodal annotation
Annotate links between several data modalities — text, audio, image or video — in order to train models capable of understanding and generating language in a enriched context. To link transcripts to visual elements, mark objects referenced in text, or contextualize sentences using a vocal tone or displayed image.
Align the different modalities (text + image, text + image, text, video,...)
Annotate entities or semantic elements in each modality
Verify the temporal or semantic alignment between modalities
Export data in a structured and intermodal format (JSON, XML, VQA, AVA...)
Vision-language AI — Link detected objects to descriptive phrases for VLM models
Analysis of filmed conversations — Link speech to facial expression or tone of voice
Annotating complex scenes — Enrich scripts or dialogues with contextual visual or audio elements
Information extraction
Identify and structure important elements contained in texts: named entities, dates, places, places, relationships, events, numbers, etc. To transform free text into database usable by AI systems, for research, analysis or decision making.
Define the types of information to be extracted
Segment texts and identify relevant expressions (pattern matching or patterns)
Link the extracted elements together (subject/action/object relationships, attributes, temporality)
Structure results in a format that can be used for AI training
Automated financial analysis — Extract companies, amounts, key dates from reports or contracts
Enrichment of databases — Automatically feed a CRM or an entity database from textual sources
Extracting events — Identify highlights in press articles or legal documents
Advanced context classification
Assign categories to texts based on their global context (position in a dialogue, underlying intention, register, tone...), and not simply according to their raw content. For train finer, context-sensitive models, particularly useful for conversational assistants, recommendation systems, or automatic moderators.
Define complex categories taking into account the intent, register, or function of the text
Annotate each segment in relation to its context (e.g.: implicit request, irony, digression)
Mark ambivalences or borderline cases to refine the taxonomy
Export annotations with built-in context
Moderation of forums or social networks — Use AI to detect problem messages based on their tone or context
Smart chatbots — Classify intentions in a conversation with context memory
Analysis of long documents — Use AI to categorize paragraphs according to their role in argumentation or narration
Annotation for semantic search
Prepare textual corpora by identifying concepts, intentions, reformulations and semantic relationships, in order to allow search engines or generative AI to Understand the real meaning of a request.
Select representative corpora (FAQ, business documents, user dialogue...)
Annotate key concepts, intentions, and semantic targets in texts
Link the contents together through semantic links (e.g.: question ↔ answer, theme ↔ variation)
Export the structured corpus for training or evaluating semantic search models (RAG, dense retrievers, etc.)
RAG (Retrieval-Augmented Generation) — Annotate document/question pairs to improve the relevance of the results
AI search engines — Feed models capable of understanding complex research intentions
Automated customer support — Associate the varied requests of a user with a base of semantic answers
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 service a team of flexible and rigorous experts, dedicated to annotation and structuring of textual data. For your NLP projects: classification, entity extraction, sentiment analysis, or semantic modeling
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 training data!
