NLP Data Annotation Services for Language Models and Conversational AI

Modern NLP systems rely on carefully annotated language datasets to understand intent, extract entities, classify text, detect sentiment, and interact naturally with users. Whether you are training a conversational agent, a domain specific classifier, or a multilingual language model, the quality of your annotations directly determines model performance.DataVLab provides NLP data annotation services tailored to production grade AI systems. We support text classification, token level tagging, named entity recognition, sentiment scoring, intent detection, relation extraction, conversational turn annotation, and domain specific linguistic tagging. Our teams work across multiple languages and follow detailed taxonomies aligned with your model objectives.We build annotation workflows that ensure consistent linguistic judgments across large datasets. This includes multi pass quality control, inter annotator agreement scoring, taxonomy validation, and conflict resolution. We also support EU based annotation teams for projects requiring stricter data handling and confidentiality.With experience across healthcare NLP, financial language models, retail chatbots, safety and compliance AI, and domain specific assistants, we help teams create training datasets that are reliable, scalable, and ready for fine tuning or evaluation.