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
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?
Typically 88-94% on general domains, 95%+ after fine-tuning on your labeled data.