Text as a source of business intelligence
Companies generate huge volumes of text every day: emails, contracts, support tickets, reports, clinical notes, delivery notes, meeting minutes. Most of it is never analyzed systematically because doing it by hand is impossible at scale. AI-powered natural language processing changes that equation.
NLP isn’t just translation or spell-checking. It’s the ability to make a machine understand the content, intent, and context of a text — enough to drive automated decisions or produce analysis that would otherwise stay buried in unread email.
NLP capabilities we implement
Automatic document classification
Text classification models assign categories to documents with no human intervention. A law firm can automatically sort contracts by type and urgency. A logistics company can route every issue that arrives by email to the right department without anyone reading and forwarding it by hand.
Information extraction and entity recognition
NER (named entity recognition) models identify and extract structured information from unstructured text: dates, amounts, party names, reference numbers, specific clauses. Applied to contracts, a process that used to take hours of manual review can run in seconds per document.
Automatic summarization
Abstractive summarization models condense long documents into actionable summaries that keep the key points. For leadership teams receiving large volumes of reports, that means staying informed without reading every document in full.
Intent classification in customer service
Support tickets and customer service chats hold valuable information about the problems users face and how urgent they are. Intent classification models prioritize automatically, assign the right agent, and spot emerging trends before they escalate.
Applications by sector
In legal and notary services, automatic extraction of contract clauses and detection of inconsistencies between documents cut review time. In healthcare and social care, processing unstructured clinical notes turns them into structured records and reveals diagnostic patterns. In manufacturing and logistics, classifying issues and delivery notes speeds up administrative workflows. In banking and insurance, automated review of applications and detection of anomalies in documentation accelerate operations.
Models and architectures
We work with transformers pretrained in the language of the documents and fine-tuned to each client’s specific terminology. BERT-family models (RoBERTa, mBERT, XLM-R) are still the benchmark for classification and extraction tasks with limited data. For summarization and text generation, we use state-of-the-art generative models with grounding techniques to prevent hallucinations in critical contexts.
The result is a robust pipeline that integrates with your existing document systems — SharePoint, Google Drive, ERP systems, or internal databases — and returns results in the format your team needs to act.
Frequently asked questions
Do NLP models work well with technical language or industry jargon?
Everyday Spanish, French, Italian, German, and the other major European languages are very well represented in modern base models. For highly specific terminology — legal, medical, industrial — fine-tuning on the client’s own corpus significantly improves accuracy. The volume needed is much smaller than people usually assume: a few hundred labeled documents are often enough.
What about documents in formats like PDF or Word?
Before NLP processing, a text extraction stage handles native PDFs, scanned PDFs (via OCR), Word, Excel, and other common business formats. OCR quality on scanned documents is the main source of variability in these cases.
How long does it take to put an NLP pipeline into production?
The timeline depends on the complexity of the document corpus and the validation rounds needed with the client’s team. We agree on the schedule before we start.