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Multilingual Data Collection
Services for AI Training

LanguageMark provides multilingual data collection services for AI and machine learning teams — gathering speech recordings, text corpora, dialogue data, and multimodal datasets across 197+ languages, including all 22 official Indian languages. Every dataset is collected by native-speaking contributors, verified by professional linguists, and delivered in your required format — ready for ASR, NLP, LLM, and conversational AI training.

Based in New Delhi and Bhopal, we have been building language datasets for AI teams across India and globally since 2016. We are the only Indian data collection provider explicitly covering all 22 Indian languages with professional linguistic oversight — not crowd-sourced annotation.
  • 197+ pairs
  • 10 Years Experience
  • Pilot Projects Available
  • All 22 Indian Languages
Discuss Your Data Requirements
Request a pilot dataset →
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  • Quality over quantity

1,000 well-collected utterances from native speakers outperform 10,000 crowd-sourced recordings of inconsistent quality.

  • Domain specificity

A healthcare voice dataset needs medical terminology. A customer service dataset needs colloquial registers. Generic data produces generic models.

  • Demographic diversity

Age, gender, regional accent, and dialect diversity must be built into the collection design, not added as an afterthought.

  • Consent and compliance

All data collected by LanguageMark is gathered with full informed consent, ownership documentation, and GDPR-compatible data handling protocols.

What Is AI Training
Data Collection — and Why Quality Matters More Than Volume

AI training data collection is the process of gathering raw data — speech recordings, written text, conversational dialogues, images, or video — from human contributors for use in training machine learning models. Unlike web-scraped data, collected data is purpose-built for a specific model requirement — with defined speaker demographics, linguistic variety, domain vocabulary, and recording conditions.

The most common failure in multilingual AI development is not a model architecture problem. It is a data problem. Models trained on insufficient or low-quality multilingual data produce unreliable outputs in non-English languages — regardless of how much compute was used in training.

For Indian AI development specifically, the challenge is acute. Most global data providers offer limited Indian language coverage, rely on non-native contributors for Indic languages, and do not understand the code-switching patterns, dialectal variety, and script complexity that characterise real Indian language use. LanguageMark addresses this directly — with professional linguists, not crowd workers, and all 22 Indian languages covered as standard.

Our Multilingual
Data Collection Services

From a pilot dataset to a production-scale data programme — here is what we collect and how we build it.
 

Speech & Voice Data Collection

Custom speech dataset creation for ASR, TTS, voice assistant, and spoken dialogue AI training. We recruit native-speaking contributors across defined demographic profiles, manage recording sessions in controlled and natural environments, and deliver verified, transcribed, and formatted audio datasets. Coverage: All 22 Indian languages + regional dialects + code-mixed Hindi-English + 170+ global languages.

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

Text Corpus Collection

Domain-specific text data collection for LLM pre-training, fine-tuning, and instruction-following datasets. We gather text from human contributors in defined domains — legal, medical, financial, conversational, technical — in your target languages, ensuring the corpus reflects actual language use rather than formal written style.

  • Best for: LLM pre-training corpora, domain-specific fine-tuning datasets, instruction-following data, chatbot training, search relevance datasets.

Conversational & Dialogue Data Collection

Multi-turn dialogue datasets for chatbot and virtual assistant training — collected from real human interactions in natural conversational registers. We design conversation scenarios, recruit contributors matching your target user demographics, and deliver structured dialogue data with intent labels and turn annotations.

  • Best for: Conversational AI, customer service chatbots, virtual assistants, helpdesk automation, multi-turn dialogue models.

Indic Language Specialised Data Collection

Purpose-built data collection for all 22 official Indian languages — including regional dialect variants, script-specific collection, code-switching patterns, and rural/urban register diversity. We work with native-speaking contributors who have been verified for linguistic accuracy by our in-house language team.

This is not generic crowd-sourced data. It is professionally managed, linguistically verified, and built to the quality standards Indian AI models actually need.

  • Best for: Sovereign AI initiatives, Indic LLM development, government AI programmes, voice AI for Bharat, Indian language NLP research.

RLHF & Human Preference Data Collection

Human feedback data for Reinforcement Learning from Human Feedback (RLHF) and direct preference optimisation — including response ranking, quality evaluation, and preference labeling by domain-expert contributors in your target language. We manage contributor recruitment, task design, quality calibration, and delivery.

  • Best for: LLM fine-tuning and alignment teams, AI safety researchers, companies building domain-specific AI applications that need culturally grounded human feedback.

Multimodal Data Collection

Combined speech, text, and image data collection for multimodal AI training — including image-caption pairs, video-transcript datasets, and audio-visual dialogue data. Collected with multilingual metadata and culturally appropriate content for Indian and international markets.

  • Best for: Multimodal LLM training, video AI, image-text models, audio-visual scene understanding, document AI.
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  • Scheduled Indian Languages

Hindi · Bengali · Telugu · Marathi · Tamil · Urdu · Gujarati · Kannada · Odia · Malayalam · Punjabi · Assamese · Maithili · Santali · Kashmiri · Nepali · Sindhi · Dogri · Konkani · Manipuri · Bodo · Sanskrit

  • Regional Dialects (on request)

Awadhi · Bhojpuri · Rajasthani · Bundeli · Chhattisgarhi · Kumaoni · Garhwali · Tulu · Kodava · Konkani variants

  • Code-Mixed Variants
Hindi-English (Hinglish) · Tamil-English (Tanglish) · Telugu-English · Bengali-English · and others
 

Building AI for India?
Your Data Needs to Reflect India.

India is not one language market. It is 22 official languages, hundreds of dialects, multiple scripts, and the most complex code-switching patterns of any country on earth. A model trained on Hindi alone will fail in Tamil Nadu. A model trained on standard written Hindi will fail in Bhojpuri-speaking districts. A model trained on formal register will fail in casual mobile voice interactions.

LanguageMark has been working in Indian languages professionally since 2014. We understand the linguistic reality of India — not just its official language list. Our data collection programme for Indic languages includes:

  • — All 22 constitutionally recognised Indian languages
  • — Regional dialect variants including Awadhi, Bhojpuri, Maithili, and others
  • — Both formal and colloquial registers per language
  • — Script variants where applicable (Devanagari, Tamil script, Telugu script, etc.)
  • — Code-mixed Hindi-English in urban and semi-urban registers
  • — Age and gender diversity built into every collection design
For AI teams building products that genuinely work for Indian users — not just urban English-speaking users — this is the data foundation that makes the difference.

How We Build Your Dataset

Every data collection project starts with a scoping conversation — not a standard order form. Data requirements are too variable and too consequential for a one-size workflow. Here is how a standard project moves from brief to delivery.
 

Step 1 —

Requirements scoping

Language pairs, domain, demographic profile, collection environment, format requirements, volume, and quality targets documented before recruitment begins.

Step 2 —

Contributor recruitment and vetting

Native-speaking contributors recruited and screened for language proficiency, domain familiarity, and recording environment quality. Contributors sign consent and data ownership agreements before participation.

Step 3 —

Pilot collection

A small pilot batch collected, reviewed by our linguistic QA team, and validated against your schema. Feedback incorporated before full-scale production begins. No large-scale collection starts without a passed pilot.

Step 4 —

Production collection with QA sampling

Full-scale collection with continuous quality sampling throughout. Audio quality checks, transcription accuracy verification, and linguistic correctness reviewed at regular intervals — not only at final delivery.

Step 5 —

Linguistic review and annotation

Collected data reviewed by in-house linguists for accuracy, naturalness, and schema compliance. Transcriptions verified, annotations applied, and metadata formatted to specification.

Step 6 —

Delivery and documentation

Dataset delivered in your required format with full metadata, speaker demographics, collection environment notes, consent documentation, and a quality summary report.
 
 
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AI Applications
We Build Data For

Different AI applications require fundamentally different data — in structure, volume, linguistic register, and collection environment. We design collection programmes for each application type.
 
 

Large Language Models (LLMs)

Domain-specific text corpora, instruction-following datasets, RLHF preference data, and evaluation sets for LLM training and fine-tuning — in Indian languages and global languages.

Automatic Speech Recognition (ASR)

Read-aloud utterances, spontaneous speech, command-and-control phrases, and conversational recordings in multiple acoustic environments. Designed to train models that work across accents, ages, and noise conditions — not just studio-quality recordings.

 

Text-to-Speech (TTS)

Expressive, natural-sounding speech recordings from professional and semi-professional voice contributors. Covers multiple speaking styles — neutral, expressive, conversational — with script design included in the workflow.

 

Conversational AI & Chatbots

Multi-turn dialogue datasets in natural conversational registers — collected from real human interactions in your target domain, language, and demographic profile.

 

Voice Assistants & Wake Words

Wake word recordings, command utterances, and short-form voice interaction data — with sufficient speaker diversity and acoustic variety to build robust detection models.

 

Document & Multimodal AI

Image-text pairs, document scan datasets, handwriting samples, and audio-visual data for multimodal and document AI training — with multilingual metadata and culturally appropriate content.
 
 

Frequently Asked Questions (FAQ)

Q: What is AI training data collection?
AI training data collection is the process of gathering raw data — speech recordings, written text, conversational dialogues, or images — from human contributors specifically for use in training machine learning models. Unlike web-scraped datasets, collected data is purpose-designed for a specific model requirement, with defined linguistic variety, demographic diversity, domain vocabulary, and collection conditions built into the design from the start.
 
India has 22 officially recognised languages, hundreds of dialects, multiple scripts, and widespread code-switching between Indian languages and English. Generic global datasets — even those labelled as multilingual — typically cover standard written forms of a few Indian languages and miss the dialectal variety, colloquial registers, and code-switching patterns that real Indian users produce. Building AI that works reliably for Indian users requires data collected from real Indian speakers across the linguistic diversity of the country — not data adapted from another market.
 
 
 
LanguageMark collects data in all 22 officially recognised Indian languages — including Hindi, Bengali, Tamil, Telugu, Marathi, Gujarati, Kannada, Malayalam, Punjabi, Urdu, Odia, Assamese, and all others. We also cover regional dialect variants, code-mixed Hindi-English (Hinglish) and other code-mixed varieties, and both formal and colloquial registers per language. Based in New Delhi and Bhopal — collecting across India.
 
 
Volume requirements depend heavily on the model type, target language, and task complexity. As a general guide: basic ASR for a single language in a constrained domain may require 100–500 hours of speech. A robust conversational ASR system across multiple accents typically requires 1,000+ hours. For LLM fine-tuning, 10,000–100,000 instruction-following examples are commonly used. We recommend starting with a scoping conversation to define your requirements — volume alone is rarely the limiting factor. Data quality and diversity usually are.
 
 
 
All data collected by LanguageMark is gathered with full informed consent from contributors. Contributors sign agreements that transfer data ownership rights to the client for the agreed use cases. We provide full consent documentation with every dataset delivery. Our collection workflows are designed to be GDPR-compatible and follow data minimisation principles. For projects with specific regulatory requirements — healthcare AI, government programmes — we adapt our consent and data handling protocols accordingly.
 
 
 
 
Yes — and we recommend it. A pilot of 500–2,000 utterances or 5,000–10,000 text examples allows you to evaluate data quality, verify that the collection design matches your model requirements, and confirm the working relationship before scaling. Pilots are quoted separately with no obligation to proceed to production. Most of our ongoing clients started with a pilot.
 
 
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You May Also Need

Data Annotation Services

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

Multilingual Data Services Hub

Overview of all our AI data services — annotation, collection, dataset QA, and RLHF data — in one place.

 

Transcription Services

Professional audio transcription in 197+ languages including all Indian languages. AI transcription review and verification also available.

Tell Us What You're Building.
We'll Tell You What Data You Need.

Share your model type, target languages, and use case. We will respond within one business day with a collection design proposal, quality methodology, and pilot project scope. No standard order forms. Every data programme starts with a conversation.
 
Data Collection Requirements
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