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NLP and Large-Scale Text Analysis

Enterprise NLP pipelines to analyze, classify, and generate text in any language. From surveys to social monitoring, from contracts to support emails: we transform unstructured text into operational insights.

Linguistic analysis, machine translation, generation and classification of textual content on a large scale.

Use cases

  • E-commerce review and e-reputation analysis
  • Automated support ticket triage
  • Summarization of long legal documents
  • SEO product description generation
  • Brand monitoring on social media and news

Measurable benefits

  • Read millions of texts in minutes
  • Structured insights from unstructured content
  • Native multilingualism
  • Decreasing marginal costs with volume

Technical details

NLP Models

  • Multilingual BERT, RoBERTa, DeBERTa
  • Generative LLM (GPT-4, Claude, Mistral)
  • spaCy for NER and linguistics
  • Fine-tuning on vertical domains

Supported Tasks

  • Sentiment & emotion analysis
  • Named Entity Recognition (NER)
  • Topic modeling and clustering
  • Extractive and abstractive summarization
  • Neural translation (50+ languages)

Pipelines

  • Real-time streaming via Kafka
  • Batch processing via Spark for large datasets
  • Vector DB (Pinecone, Qdrant, Weaviate)
  • Semantic embeddings for search

Outputs

  • REST/GraphQL API
  • Event-driven webhooks
  • Dedicated analytics dashboard
  • CSV/JSON/Parquet export

How we work on text and documents

  1. Corpus and taxonomy — We define which documents are in scope and the category structure: without a shared taxonomy classification cannot be evaluated.
  2. Extraction — We normalise heterogeneous formats — PDFs, scans, emails, attachments — and recover text with recognition quality checks.
  3. Analysis models — Classification, entity extraction, summarisation or comparison: each task gets its own metric and acceptance threshold.
  4. Evaluation set — We build a sample annotated by domain experts and use it as a stable reference for every change.
  5. Human review — We set the confidence threshold below which a document is queued for review rather than processed automatically.
  6. Operations — In production we monitor rejects, new categories and anomalous documents — the clearest signal of a changing corpus.

We deliver the taxonomy, annotated evaluation set, extraction pipeline and per-category metric reports.

Working with us

The team that analyses the process is the team that builds and maintains it: product, engineering, integration with your existing systems, governance of automated decisions and post-release support.

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FAQ

Does it work in languages other than Italian?

Yes. We use multilingual models that natively cover 50+ languages, with quality comparable to English.

Can I analyze confidential documents?

Yes, pipelines can run on-premise or on a private cloud without data leaving your perimeter.

How accurate is the sentiment analysis?

Accuracy depends on the domain and the quality of labelled data: we measure the baseline on your corpus and, where needed, improve it with fine-tuning.