Service
NLP & LLMUnstructured text contains more information than any relational database. Contracts, reviews, reports, emails. You just need to know how to listen to them.
Language intelligence applies natural language processing (NLP) techniques and large language models (LLM) to extract structured knowledge from unstructured text. It turns documents, comments and communications into actionable signal for the business.
Opinion & review analysis
Automatic sentiment classification and extraction of recurring themes from customer feedback, surveys and online reviews.
Contract data extraction
Automated reading of contracts, invoices and legal documents to extract key clauses, dates, amounts and conditions.
Q&A on internal documentation
Question-answering systems over manuals, knowledge bases and technical documentation, without reading thousands of pages.
Automatic classification & routing
Automatic categorisation of tickets, emails and requests to direct them to the right department or person without manual intervention.
Use case definition
We identify the specific problem: what text, what information we want to extract and what decision it must improve. An NLP project without a business objective is an experiment.
Corpus preparation
We collect, clean and structure available texts. The quality of the corpus determines the quality of the result — more than the model itself.
Model and pipeline
We build the appropriate pipeline: from classical classifiers to production LLMs, depending on the use case, volume and privacy requirements.
Integration into existing systems
We connect the model to the tools your team already uses: CRM, ERP, customer service platforms, intranets. The result reaches where the decision is made.
Companies with large text volumes
Organisations that generate or receive massive customer feedback, contracts, internal reports or communications that aren't analysed systematically.
Customer service teams
Companies that want to understand the real reasons for contact, automate frequent responses or prioritise the most urgent tickets.
Law firms and document-heavy companies
Organisations working with contracts or complex documents that need to extract key information without exhaustive manual review.
E-commerce and retail businesses
Businesses wanting to extract signal from product reviews and post-purchase feedback to improve product, logistics and service.
It depends on the use case. For sentiment classification or thematic analysis, pre-trained models work well with few labelled examples — sometimes fewer than two hundred. For information extraction from highly sector-specific documents, the more examples the better. We assess available volume in the initial diagnosis.
Yes. We can design pipelines that run entirely on private infrastructure or in cloud with strict privacy configurations, without documents leaving your environment. We don't depend on external APIs if the case requires maximum privacy.
Classical NLP models are faster, cheaper and more predictable — good for well-defined tasks like classification or entity extraction. LLMs are more flexible and handle more open-ended tasks, but require more care in production. We choose the approach based on the problem, not on trends.
A first functional prototype can be ready in two to four weeks. A production system with full integration, validation and monitoring typically requires two to three months, depending on pipeline complexity and data availability.
Tell us what documents or texts you have and what you need to know from them. We assess feasibility at no commitment.
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