High-quality training data for your AI language models
Native-language experts annotate NLP, ASR and NER datasets in 225+ languages with measured IAA quality (kappa of 0.8 or higher) — directly loadable into your ML framework.
Native-language experts in 225+ languages annotate your NLP, ASR and NER datasets against detailed guidelines — with measured inter-annotator agreement and direct delivery in JSON, JSONL or CSV.
AI models are only as strong as their training data. Weak annotations produce weak models — regardless of architecture or scale. We provide the human expertise and linguistic depth that automatic or crowdsourced annotation cannot match, particularly for low-resource languages and specialist domains such as medical, legal and technical content.
From core languages for LLM fine-tuning to low-resource markets where native annotators are irreplaceable.
We discuss your annotation task, quality requirements and labelling schema. From this we draft detailed annotation guidelines — the foundation for consistency across annotators.
We select native-language experts with the right domain knowledge and train them on your specific task. A pilot batch with IAA measurement validates the guidelines before full-scale production starts.
Our annotators carry out the task: text classification, Named Entity Recognition, sentiment labelling, parallel corpus building, ASR transcription or other language-specific annotations.
Inter-annotator agreement (IAA, Cohen or Fleiss kappa) is measured and reported. Segments with low agreement go through an additional review round to maximise data quality.
You receive the annotated dataset in JSON, JSONL, CSV or your own format — directly loadable into any ML framework. For iterative training cycles we deliver continuous batches.
LLM leaderboards are not won on architecture alone. The difference sits in the annotation quality of your fine-tuning data. Native experts bring the nuance and cultural context that crowdsourced platforms miss — especially for domain-specific and low-resource languages. That difference is measurable in benchmark scores.
From RLHF feedback to NER and sentiment analysis — native experts who understand exactly what you want the model to learn.
Native-language experts only — no crowdsourced or machine-labelled data. High-quality human annotations that genuinely strengthen your model, including for low-resource languages.
We measure and report inter-annotator agreement per task and target a kappa score of 0.8 or higher — calibrated to the complexity of the annotation schema.
Structured annotation processes scale from thousands to millions of segments or utterances — with the same quality standard at every volume tier.
Delivery in JSON, JSONL, CSV or your own format — directly loadable into PyTorch, TensorFlow, Hugging Face or your custom training pipeline.
From IAA measurement to GDPR-aligned processing — the foundation for training data you can build on.
From LLM fine-tuning to chatbot intents and ASR training — annotation at the scale your model needs.
AI · Fine-tuning An AI startup had 120,000 NL-EN translation pairs annotated for domain-specific fine-tuning. Native Dutch annotators, IAA kappa 0.89. Measurable improvement on the team benchmark.
Chatbot · Enterprise An enterprise chatbot team annotated 8,000 user intents across 18 languages for retraining. Native annotators per language with a consistent labelling tree. Measurable lift in intent classification accuracy after retraining.
Telecom · ASR A telecom provider annotated 600 hours of customer calls for ASR fine-tuning: verbatim transcription, diarisation and tone labels. Low-resource dialects received additional weighting.
From NLP model training to ASR data and sentiment datasets — annotation for every language-specific AI use case.
What clients say about working with Ecrivus — from AI startups to enterprise ML teams.
Certified translations for our international cases are delivered quickly and carefully. Our project manager knows our account inside out.
No-obligation — response within one hour on business days
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Last updated: May 2026
