Intent classification, named entity recognition, sentiment labelling, and dialogue annotation in 15+ languages. Power your chatbots, search engines, and conversational AI with precisely labelled text data from our expert linguistics team.
Natural Language Processing models are only as good as the text data they're trained on. Our linguistics-trained annotators label intent, entities, sentiment, relations, and dialogue structure with the nuance that automated tools miss — especially in low-resource African and Asian languages.
We support the full NLP annotation stack: from document classification and NER through coreference resolution, relation extraction, and multi-turn dialogue annotation for conversational AI systems.
Our annotators include native speakers of Swahili, Amharic, Hausa, Arabic, French, and other languages underrepresented in standard NLP datasets. We bridge the language gap for global AI products.
Intent classification, entity extraction, and slot filling labels for conversational AI training. We annotate single and multi-turn dialogues with intent hierarchies, slot-value pairs, and dialogue act labels for robust NLU models.
Query intent and entity annotation for search relevance models and recommendation engines. We label user queries with intent type, named entities, temporal expressions, and implicit preferences to improve retrieval accuracy.
Brand monitoring, customer feedback analysis, and social media AI training. We deliver fine-grained sentiment labels — aspect-level sentiment, emotion classification, and subjectivity scores — across product reviews, tweets, and support tickets.
Medical entity extraction and clinical note classification for healthcare AI. Our trained annotators identify medications, dosages, diagnoses, procedures, and clinical findings in unstructured EHR text with HIPAA-compliant data handling.
Contract clause annotation and regulatory document tagging for legal AI systems. We identify obligations, rights, definitions, and risk clauses in complex legal text — supporting contract review automation and compliance monitoring tools.
Low-resource language annotation for global product expansion. Our native speaker network covers Swahili, Amharic, Hausa, Yoruba, Zulu, Arabic, Hindi, and more — enabling AI products to serve markets that standard annotation providers cannot reach.
We define the intent taxonomy, entity types, sentiment scales, relation schemas, and edge-case rules in collaboration with your NLP team. Annotator guides with worked examples are produced for every label type before annotation begins.
Sample texts are annotated by two or more independent annotators. Inter-annotator agreement is measured using Cohen's kappa or Fleiss' kappa, and guidelines are refined to resolve systematic disagreements before full production starts.
Annotation is carried out by native speaker teams for each target language, with continuous calibration sessions to maintain label consistency. Language leads review edge cases and flag cultural nuances that affect label interpretation.
Inter-annotator agreement scoring, expert adjudication of disagreements, and a final QA lead review are applied before every delivery. Output is provided in CSV, JSON, CoNLL, IOB2, or any NLP framework-native format your pipeline requires.
Annotators who understand linguistic nuance, idiom, and cultural context — not just translated instructions. For African, Middle Eastern, and South Asian languages, our native speaker network is unmatched in the industry.
Inter-annotator agreement is measured and maintained above 90% throughout production using kappa scoring. Regular calibration sessions address label drift and ensure consistency is sustained across long-running projects.
Ramp from pilot to millions of labelled utterances without quality degradation. Our parallel team structure and language-specific QA leads ensure throughput scales while annotation standards remain constant.
NDA-backed workflows and ISO 27001 security for sensitive text datasets — including clinical notes, legal documents, and proprietary customer conversations. All data is handled in secure, access-controlled annotation environments.
CoNLL, IOB2, JSONL, CSV, Hugging Face Dataset, or any NLP framework-native format are all supported. We include schema documentation and sample validation files with every delivery to streamline your training pipeline integration.
Premium linguistics expertise at African talent rates. Access the same annotation quality as specialist NLP providers in the US and Europe at significantly lower cost — making large-scale multilingual dataset creation viable for any budget.
Get a custom quote within 24 hours. Our team is ready to discuss your dataset requirements and timeline.