Text & NLP Annotation Services

Large language models and classical NLP pipelines both depend on clean, consistent text labels. Our text annotation services cover named entity recognition, sentiment analysis, intent classification, document tagging, and conversational AI datasets — with multilingual pools and secure handling for regulated enterprises.

Labels that improve real NLP performance

Ambiguous entities, slang, and domain jargon break naive tagging workflows. We train annotators on your taxonomy, run inter-annotator agreement on edge cases, and refine guidelines as your product vocabulary evolves.

NLP annotation modalities

Span-based NER for finance and healthcare entities; sentiment and emotion tags for customer feedback; intent labels for chatbots; relation extraction for knowledge graphs; OCR-aligned text regions for document AI.

Enterprise text data challenges

Long documents, tables, mixed languages, and PII require careful workflows. We support de-identification review, locale-specific nuance, and consensus on low-confidence spans before data reaches fine-tuning jobs.

Multilingual and industry coverage

Annotators for English, European languages, and key APAC locales across legal, medical, retail, and customer-support corpora — with QA tuned to each regulatory context.

Integration with modern ML stacks

Deliver JSON, CSV, or platform-native exports compatible with Hugging Face, spaCy, and custom fine-tuning pipelines. Human-verified labels accelerate both classical NLP and LLM evaluation sets.

Get started

Build NLP datasets your models can rely on. Share sample corpora, entity types, and quality thresholds — we propose annotation design, timeline, and per-unit pricing for your text labeling program.