General Presentation
Natural language processing (NLP) refers to the set of techniques that allow a machine to read, analyze, interpret, generate, and interact in human language, whether written or spoken. It constitutes one of the most complex and strategic branches of modern artificial intelligence, due to the richness, ambiguity, and contextual dimension of language.
At NeuriaLabs, we mobilize advanced architectures to enable systems to finely understand expressed intentions, extract useful information from massive volumes of text, respond appropriately to complex queries, and produce fluent, precise, and contextually adapted natural language.
Intervention Areas
Our expertise in NLP extends across the entire linguistic processing chain, from the preprocessing of raw textual data to the generation of natural language or speech synthesis. We particularly integrate the following components:
1. Syntactic and morphological analysis
Identification of the grammatical structure of sentences, dependencies between words, lemmas, tenses, genders, and cases. This step allows for a formal representation of language for algorithmic exploitation.
2. Semantic modeling
Representation of the meanings of words, expressions, or phrases in vector spaces, using approaches such as Word2Vec, GloVe, FastText, or contextual embeddings (BERT, RoBERTa, GPT, etc.), allowing for the capture of semantic proximity, synonymous or antonymous relationships, and nuanced meanings depending on the context.
3. Text understanding
Automatic extraction of intentions, facts, named entities (people, places, organizations), dates, causal or logical relationships, as well as the resolution of co-references (e.g., "she" refers to which person?). These functions are essential in conversational assistants, document analysis tools, or compliance platforms.
4. Automatic summarization and language generation
Ability to automatically summarize long texts, rephrase content, generate synthetic responses, or produce original text based on instructions or input corpora. These functions are used notably for regulatory monitoring, automated reports, editorial content generation, or writing assistance.
5. Voice interaction and speech recognition
Conversion of speech to text and vice versa (speech-to-text and text-to-speech), with integration of models tailored to the target language, expected register (formal, technical, conversational), and specific business needs. These functions apply to intelligent call centers, accessibility tools, or voice control environments.
Sector Applications
The NLP technologies deployed by NeuriaLabs find direct application in numerous sectors:
• Legal: contract analysis, extraction of sensitive clauses, automated legal classification, synthesis of case law.
• Health: interpretation of medical notes, structuring of reports, extraction of clinical data from unstructured files.
• Banking and insurance: reading and analysis of contractual documents, scoring of requests, detection of risks or non-compliance in written exchanges.
• Customer service: implementation of multi-domain conversational assistants, with fine understanding of intentions and contextual management of interactions.
• Monitoring and intelligence: automatic processing of massive informational flows (articles, reports, publications), extraction of strategic insights.
Technologies and Architectures Used
We master all next-generation linguistic models, based on transformer-type architectures, which have significantly redefined performance standards in NLP:
• Static contextual models (BERT, DistilBERT, RoBERTa)
• Generative models (GPT, T5, BLOOM, Falcon)
• Multilingual models (mBERT, XLM-R, NLLB)
• Compact models for constrained terminals (TinyBERT, DistilGPT, quantized LLaMA)
• Customized processing chains (RAG, retrieval-augmented generation) combining NLP and advanced document search
We also develop our own preprocessing, training, and fine-tuning pipelines, integrating the linguistic, sectoral, and ethical constraints specific to each area of intervention.
NeuriaLabs Approach
Our positioning is based on:
• A fine understanding of the business and legal stakes related to the exploitation of language;
• A capacity to customize linguistic models on specialized corpora (law, finance, medicine, technical, administrative);
• A constant focus on the transparency of models, the mastery of linguistic biases, the verifiability of sources, and semantic security;
• Total interoperability with document management systems, CRMs, monitoring tools, or business applications.
