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Multilingual Data Services
For AI & NLP Training

LanguageMark provides multilingual data services for AI and machine learning teams — covering data annotation, speech and text data collection, dataset creation, and linguistic quality assurance across 197+ languages, including all 22 official Indian languages. We combine the linguistic expertise of professional language specialists with the data requirements of modern AI development — delivering training data that is accurate, culturally appropriate, and bias-aware from the ground up.
Based in New Delhi and Bhopal, we have been building language datasets and delivering annotation services to AI teams and enterprise organisations across India and globally since 2014.
  • 22 Indian Languages
  • 197+ pairs
  • 10 Years Experience
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  • Mistranslated labels

Training data annotated by non-native speakers with limited domain knowledge

  • Cultural misclassification

Sentiment and intent labels that reflect the annotator’s culture, not the target market’s

  • Dialectal bias

Speech models trained on standard language variants that fail on regional accents and dialects

  • Domain terminology errors

Generic annotators who cannot reliably label medical, legal, or technical content in non-English languages

Why Most AI Training Data Fails in Non-English Languages

Most AI training data workflows are built for English-first development and adapted for other languages as an afterthought. The result is predictable — models that perform well in English and degrade significantly in other languages, particularly in low-resource and Indian languages.

The problem is not the model. It is the data. Poor quality multilingual training data produces four specific failure modes: mistranslated labels, culturally inappropriate sentiment classifications, dialectal bias in speech recognition, and terminology errors in domain-specific applications.

LanguageMark addresses all four — by treating data quality as a linguistic problem, not a volume problem. Our annotation and data collection workflows are built around language expertise first, and scaled through process second.

Our Multilingual Data Services

From raw data collection to fully annotated, LLM-ready datasets — here is what we deliver.
 

Text Annotation & NLP Labeling

Sentiment analysis, named entity recognition (NER), intent classification, relation extraction, coreference resolution, and semantic similarity labeling — in 197+ languages. Every annotation is performed by native-speaking linguists with domain knowledge in the target language and subject area.

  • Best for: NLP model training, LLM fine-tuning, chatbot development, search relevance systems, document classification.

Speech & Audio Data Collection

Custom speech dataset creation for ASR, TTS, voice assistant, and conversational AI training — in Indian languages, regional dialects, and global languages. We recruit native speakers, manage recording sessions, and deliver verified, transcribed, and annotated audio datasets formatted for your training pipeline.

  • Best for: ASR model training, voice assistant development, IVR systems, speech-to-text for Indian languages, wake word detection.

Indic Language Data — All 22 Official Indian Languages

Training data collection, annotation, and validation in all 22 official Indian languages — including Hindi, Bengali, Tamil, Telugu, Marathi, Gujarati, Kannada, Malayalam, Punjabi, Urdu, Odia, and more. We include regional dialects, script variants, and code-switching patterns that standard annotation vendors cannot reliably provide.

  • Best for: Indian AI startups, LLM developers building for Bharat, voice AI for Indian markets, government AI initiatives, multilingual NLP research.

Image & Video Annotation

Object detection, image segmentation, bounding boxes, keypoint annotation, video frame labeling, and action recognition — with multilingual metadata and culturally appropriate labeling for non-Western visual contexts. Our annotators are briefed on cultural conventions in the target market before work begins.

  • Best for: Computer vision models, autonomous systems, retail AI, surveillance and security applications, medical imaging AI.

LLM Evaluation & RLHF Data

Human preference data, response ranking, quality evaluation, and Reinforcement Learning from Human Feedback (RLHF) datasets for large language model fine-tuning and alignment. Evaluators are domain-trained and language-native — producing feedback data that reflects real user expectations in the target language and market.

  • Best for: LLM developers, AI labs fine-tuning foundation models, companies building domain-specific AI applications, model safety and alignment teams.

Dataset QA & Validation

Independent quality review of existing annotated datasets — checking for labeling errors, cultural bias, inter-annotator disagreement, and consistency issues before the data enters your training pipeline. We also validate datasets produced by AI annotation tools, crowd-sourcing platforms, or internal teams.

  • Best for: AI teams with existing datasets that have shown unexpected model performance issues, companies inheriting third-party annotation data, teams using automated annotation tools that need human validation.

How We Build
Multilingual Training Data

Data quality is not a final check. It is built into every stage of our workflow. We apply the same HATF™ quality principles to data services as we do to all our language work — defined stages, named reviewers, and measurable quality benchmarks at each step.
 

Step 1 —

Requirements scoping

Language pairs, annotation schema, domain vocabulary, format requirements, quality targets, and delivery timeline confirmed before work begins. We do not start annotation until the schema is agreed and documented.

Step 2 —

Annotator selection and briefing

Domain-specialist, native-speaking annotators selected per language and subject area. Every annotator is briefed on the specific schema, terminology, edge cases, and quality standards for the project before the first label is applied.

Step 3 —

Pilot batch and calibration

A small pilot batch (typically 200–500 items) is annotated, reviewed, and calibrated before full-scale production begins. Inter-annotator agreement is measured and schema ambiguities are resolved at this stage — not after 50,000 items.

Step 4 —

Production annotation with sampling QA

Full-scale annotation with continuous sampling quality checks throughout. Items flagged for review are returned to annotators with specific feedback — not discarded or overridden.

Step 5 —

Independent review

A separate review team checks a defined percentage of completed annotations for accuracy, consistency, and schema compliance. Review is documented and traceable.

Step 6 —

Delivery and format validation

Final dataset delivered in your required format (JSON, CSV, CoNLL, JSONL, custom). Format validated before delivery. Annotation coverage report and quality summary included.
 
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AI Applications We Build Training Data For

We work with AI teams at every stage — from initial dataset creation to model iteration and ongoing data improvement.
 

Large Language Models (LLMs)

Pre-training corpora, fine-tuning datasets, instruction-following data, RLHF preference data, and safety evaluation sets — in English, Indian languages, and global languages.

Automatic Speech Recognition (ASR)

Spoken language datasets for ASR training — covering standard language, regional dialects, accented speech, spontaneous conversation, and noisy environment recordings in Indian and international languages.

 

Natural Language Processing (NLP)

Text datasets for sentiment analysis, intent detection, NER, relation extraction, question answering, summarisation, and machine translation — with linguist-verified labels across all target languages.

 

Conversational AI & Chatbots

Dialogue datasets, intent-utterance pairs, multi-turn conversation data, and response preference rankings for chatbot and virtual assistant development — including Indian language and code-mixed variants.

Computer Vision

Annotated image and video datasets for object detection, segmentation, classification, and action recognition — with multilingual metadata and culturally calibrated labels for non-Western visual environments.

 

Search & Recommendation

Relevance judgments, query-document pairs, click-through datasets, and preference rankings for search engine and recommendation system training — in multiple languages with market-specific relevance calibration.
 
 

Frequently Asked Questions (FAQ)

Q: What is multilingual data annotation and why does AI need it?
Multilingual data annotation is the process of labeling training data — text, audio, image, or video — in multiple languages for use in AI and machine learning model training. AI models learn from labeled examples. Without accurately annotated multilingual training data, a model trained on English-labeled data will perform poorly in other languages because it has no reliable signal to learn from. The quality of the annotation directly determines the quality of the model’s performance in each target language.
 
LanguageMark provides AI training data across all 22 official Indian languages — including Hindi, Bengali, Tamil, Telugu, Marathi, Gujarati, Kannada, Malayalam, Punjabi, Urdu, Odia, Assamese, and more. We also cover regional dialects, script variants, and code-mixed Hindi-English patterns. Based in New Delhi and Bhopal, we have been working in Indian languages professionally since 2014.
 
 
 
Data collection is the process of gathering raw data — recording speech samples, collecting text from human contributors, capturing images, or sourcing documents. Data annotation is the process of labeling that raw data so AI models can learn from it — adding tags, categories, bounding boxes, transcriptions, or sentiment labels. Both are required for AI training. LanguageMark provides both as part of an integrated multilingual data service.
 
We apply a five-stage quality process to every data project — requirements scoping, annotator selection and briefing, pilot batch calibration, production annotation with sampling QA, and independent review before delivery. Annotators are selected for native-language proficiency and domain expertise in the subject area. Inter-annotator agreement is measured and documented. Quality reports are included with every delivery.
 
 
 
We deliver datasets in all standard AI training formats including JSON, JSONL, CSV, CoNLL, BIO, IOB, XML, and custom schema formats. We work with your existing data pipeline and tooling — including Hugging Face datasets format, COCO format for image annotation, and custom formats for proprietary systems. Format requirements are confirmed before work begins and validated at delivery.
 
 
 
Yes. We recommend starting with a pilot — typically 1,000–5,000 annotated items — to validate annotation quality, confirm schema alignment, and establish a working relationship before scaling. Pilot projects are quoted separately with no obligation to proceed to full production. Most clients use the pilot to confirm quality and then move to a larger ongoing engagement.
 

Data Annotation Services

Text, audio, image, and video annotation — in 197+ languages. NER, sentiment, intent, image segmentation, and LLM evaluation data built by domain-specialist linguists.

Data Collection Services

Speech recordings, text corpus collection, multilingual dialogue data, and custom dataset creation. Built to your schema, verified for quality, delivered in your format.

Explore Our
Data Services in Detail

Building a Multilingual AI Product?
Let's Talk About Your Data.

Tell us about your model, target languages, annotation schema, and volume. We will respond within one business day with a proposed approach, quality methodology, and a sample-based quote. Pilot projects available with no minimum commitment.
 
 
Discuss Your Data Requirements
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