Named entity recognition, intent classification, sentiment analysis, and text categorization — labeled by trained annotators and checked through three layers of review before it reaches your training set.
We match the annotation schema to what your language model actually has to learn — not a generic tag set.
People, places, organizations, products, and custom domain entities tagged at the span level.
Single- and multi-label intent tagging for chatbots, voice assistants, and support automation.
Document, sentence, or aspect-level sentiment and emotion labeling for reviews, feedback, and social text.
Topic, category, and taxonomy labeling for search, moderation, and content-routing models.
Multi-turn intent, entity, and sentiment labeling across full conversations, not isolated utterances.
Entity relationships and coreference chains for knowledge-graph and information-extraction models.
Intent and entity datasets for chatbots, voice assistants, and support automation.
Ticket categorization, sentiment tracking, and escalation-trigger labeling.
Moderation labeling, topic tagging, and abusive-language detection datasets.
English, Bengali, and several other South and Southeast Asian languages, alongside major global languages — see our Data Collection page for the full list.
Yes. We work from your existing schema or help design one from a sample of your raw text, then brief annotators against a documented guideline and edge-case library.
Yes — intent, entity, and sentiment labeling across full multi-turn dialogues, not just single utterances, for chatbot and voice-assistant training sets.
Share your requirements to discuss a suitable pilot and proposal scope.