Turn text into intelligence.

Yes. For multilingual classification, NER, and semantic search, we use multilingual transformer models -- XLM-RoBERTa and mBERT handle 100+ languages with strong performance on European and major Asian languages. Arabic, Hindi, and other morphologically complex languages typically need domain-specific fine-tuning on top of the multilingual base.

For text classification with a fine-tuned transformer, 500-2,000 labeled examples per class is usually enough for production quality. Named entity recognition needs 1,000-5,000 annotated sentences. If your labeled data is thin, we combine semi-supervised learning with active learning loops so you label efficiently rather than randomly.

Traditional NLP with fine-tuned transformers is faster, cheaper, and more deterministic -- ideal for high-volume classification, entity extraction, and structured output. LLMs handle ambiguous, open-ended tasks better but cost 10-50x more per token. Many of our production systems combine both: a fine-tuned classifier routes text, with an LLM handling only the cases that benefit from reasoning depth.

A classification or NER pipeline integrated into an existing API takes 4-8 weeks. Semantic search built on a document corpus takes 2-4 weeks. Document extraction from structured layouts runs 4-6 weeks. Conversational AI on a domain knowledge base takes 6-10 weeks. The biggest variable is labeled data availability at project start.

For binary classification on clean text, well-tuned models reach 90-97% F1. Multi-label classification over 20+ categories runs 80-88% micro-F1. NER on domain-specific entities reaches 85-93% F1 after fine-tuning. We report precision and recall separately because the right tradeoff depends on your use case.