M21AI

Multilingual Data Annotation for Artificial Intelligence

Training data annotation, RLHF evaluation, intent and entity labelling, and multilingual dataset quality control. Native annotators in over 40 languages, IAA metrics and ISO 17100 certified processes.

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The quality of training data directly determines the performance of AI models in international markets. Inconsistent annotation, biased labelling or datasets with linguistic gaps degrade the precision of NLP and NLU models in languages other than English. For companies training or fine-tuning multilingual models, annotation quality in each language is as critical as the model architecture itself.

M21AI provides multilingual annotation and data classification services with native annotator teams in over 40 languages. Our processes include inter-annotator agreement (IAA) metrics using Cohen's Kappa, sampling review and cross-validation. Over 20 years of experience in ISO 17100 certified linguistic processes, applied to the quality demands that machine learning pipelines require.

28M+

Words translated with AI assistance

800+

AI/tech projects completed

150+

Clients with AI-enhanced services

95+

Language pairs available

Areas of Expertise

NLP and NLU

Named entities, sentiment, intents
Native annotators with technical training
Calibration and documented edge cases

Multilingual RLHF

Response evaluation by native speakers
Safety and cultural appropriateness criteria

Quality Control

Cohen's Kappa, sampling review
Quality reports per delivery

Integration

JSONL, CoNLL, IOB, CSV
Webhooks and incremental delivery

NLP and NLU Annotation

Data annotation for natural language processing models requires annotators who understand the linguistic subtleties of each language. Named entity annotation, sentiment classification, intent labelling and co-reference resolution are tasks where native linguistic competence makes the difference between a dataset that improves the model and one that introduces bias.

M21AI uses native annotator teams trained in project-specific technical guidelines. Before starting annotation, we conduct calibration sessions to ensure alignment between annotators, define edge cases and establish tie-breaking criteria for ambiguous categories. This process reduces inter-annotator variability and produces consistent datasets from the first iteration.

Evaluation and RLHF

Reinforcement Learning from Human Feedback (RLHF) depends on human evaluators who understand the nuances of quality, relevance and safety of a model's responses in each language. Evaluation conducted by non-native speakers or without adequate cultural context can train the model to prefer responses that sound artificial or culturally inappropriate in the target language.

M21AI provides native evaluator teams for multilingual RLHF processes, trained in client-specific evaluation criteria. Evaluators classify responses for factual accuracy, natural fluency, cultural appropriateness and compliance with safety guidelines. We monitor evaluation consistency with IAA metrics and conduct recalibration sessions when agreement indices fall below defined thresholds.

Dataset Quality Control

An annotated dataset without rigorous quality control can compromise months of training work. M21AI implements multi-layered QA processes: automatic format and completeness validation, inter-annotator agreement (IAA) with Cohen's Kappa metrics, stratified sampling review and cross-validation between independent annotators. We identify systematic error patterns before they contaminate the complete dataset.

Each delivery includes a detailed quality report with consistency metrics by category, identification of problematic categories, label distribution and recommendations for subsequent iterations. For ongoing projects, we monitor the evolution of quality metrics over time, ensuring that annotation precision is maintained or improved as volume grows.

Formats and Pipeline Integration

Datasets annotated by M21AI are delivered in the formats required by each training pipeline, ready for direct ingestion. We support standard formats such as JSONL, CoNLL, IOB, CSV and client-defined proprietary formats. The file structure, including annotation schemas, metadata and provenance information, is agreed at the start of the project and maintained consistently throughout all deliveries.

We integrate with data management and annotation platforms such as Label Studio and Prodigy, and support delivery via webhooks for automated pipelines. For large-scale projects, we configure incremental delivery workflows that feed the training pipeline as annotation batches are completed and validated, reducing the total time between data collection and the start of training.

Our Commitments

Native Annotators

Native annotator teams in over 40 languages, trained in project-specific technical guidelines.

IAA Metrics

Inter-annotator agreement measured with Cohen's Kappa. Calibration and recalibration sessions for consistency.

ISO 17100 Processes

Quality processes audited by Bureau Veritas, applied to data annotation and classification.

Flexible Formats

Delivery in JSONL, CoNLL, IOB, CSV and proprietary formats. Integration with Label Studio and Prodigy.

What Our Clients Say

We are extremely pleased with the service provided. You demonstrate speed and adherence to desired deadlines

Bruno Martins, DEFT Training & Manpower Services

We thank you for your professionalism in executing it, quality and compliance with the established deadline

Pedro Pires, ENVAC South Europe & Americas

I was validating the translation with the designers who did the original version (PT) and I confirm everything is fine!

Madalena Caetano, HR Consultant

Frequently Asked Questions

We use inter-annotator agreement (IAA) measured with Cohen's Kappa as the primary consistency metric. We complement this with stratified sampling review (typically 10-20% of the dataset), cross-validation between independent annotators and label distribution analysis. Each delivery includes a quality report with these metrics, identification of problematic categories and improvement recommendations.

We have native annotator teams in over 40 languages, with particularly strong coverage in European languages, Portuguese (PT and BR), Spanish, French, German, Italian, and Asian languages such as Chinese, Japanese and Korean. For less common languages, we assess availability on a case-by-case basis. All annotators are native speakers trained in project-specific technical guidelines.

Our multilingual RLHF process uses native evaluators who classify model responses for factual accuracy, natural fluency, cultural appropriateness and compliance with safety guidelines. We conduct calibration sessions before starting, define evaluation criteria with the client and monitor consistency with IAA metrics. Recalibration sessions are held when agreement indices fall below agreed thresholds.

Speak with a specialist

A brief call to understand the annotation and multilingual data needs of your AI project. No commitment.