We work in languages from all over the world, and we go deep on Indian language data: collection, transcription, conversations and translation, done by native speakers from a network of 30,000+ people, in the regions your users live.
call 0212 · Hindi and English
Kal hi meeting en hai hi
spokenwritten three ways
checked and labeled
Hindi and English Delhi accent intent: reminder native speaker review
checkedIndian language data is speech, text and translated material in the languages people in India actually use, recorded or written by native speakers so an AI model can understand them. That means the scripts, the regional accents and the everyday habit of switching between languages mid-sentence.
India's constitution lists 22 scheduled languages, and plenty more are spoken every day. A model that handles Hindi well tells you very little about how it will cope with Tamil.
Public datasets exist for some of these languages and are thin or missing for others. Where they do exist, they often come from read-aloud or formal text, which sounds nothing like a customer on a noisy phone line.
Scoping speech in an Indian language? The guide to what to specify before you collect speech data applies here as much as anywhere, and it pairs well with the language notes on this page.
A single spoken request from a phone call, and the four things that make Indian language data harder than it looks. Tap through them. It's a made-up example.
Hindi, Marathi and Nepali share Devanagari. Bengali, Gujarati, Gurmukhi for Punjabi, Odia, Tamil, Telugu, Kannada and Malayalam each have their own. So the people writing your data have to read and type the right one, not just speak the language.
People move between languages inside one sentence, Hindi and English being the usual pair. Someone has to decide how that gets written, and then everyone has to write it the same way.
Hindi spoken in Delhi, Bhopal and Patna doesn't sound the same, and some of the words differ too. A speech model trained on one city can stumble in the next.
Places and people have several valid spellings, and a number might be spoken in Hindi and written in digits. Leave that to chance and the same thing ends up written five different ways.
The same services as anywhere else, done by people who speak the language and know the region. It all runs on our own tools.
Read prompts and natural talk recorded by native speakers in the regions you choose. See data collection for how it runs.
For speech recognition and voice assistants
Our tools: Prompt Audio and Conversation Audio Free sample of Indian language speech collection
Two-sided calls for banking, support and voice bots, the way people really talk on the phone. More on conversational AI data.
For voice bots, IVR and call analytics
Our tool: Conversation Audio Free sample of Indian language call data
Audio written down by people who know the language and the accent, then checked. See transcription.
For speech-to-text training and call analytics
Our tool: Transcription Free sample of Indian language transcription
Prompts, interface text and training data translated and checked by native speakers. See translation and localization.
For multilingual chatbots and apps
Our tool: Translation Free sample of Indian language translation
Entities, intents and sentiment tagged in the language your users actually write in. See data annotation.
For intent detection and sentiment models
Written text in the scripts and spellings real users type, including Hindi in Latin letters on a phone keyboard.
For chatbots, search and text classification
A few of the jobs Indian language data ends up doing. These are typical projects, not a client list. There are also pages for speech data and text data in general.
Commands said in Hindi, Tamil or a mix with English, by people from the regions you sell in.
Real call patterns, so a bot follows a customer who switches language halfway through.
Calls written down and labeled by speaker, so a model can follow who said what.
Prompts and screens that read naturally to someone in Chennai, not like a word-for-word copy.
Messages typed in mixed scripts, tagged for intent and mood.
Held-back data from each language and region, to check your model works for all of them.
None of these is exotic. They show up on nearly every project, and each one is cheap to settle early and painful to fix later.
Someone has to decide how a Hindi-English sentence gets written: each word in its own script, everything in one script, or Latin letters. Pick one and stick to it.
The same language sounds different city to city, and some words change too. Plan the speakers by region, not just by language.
On phones, lots of people type Indian languages in Latin letters, and the spelling changes from person to person. Decide whether to keep it as typed or tidy it up.
Places and people have many valid spellings, and a number might be spoken in one language and written in another. A shared spelling list saves a lot of cleanup.
The more precise the brief, the closer the match. You don't need all of this on day one, though. If you're unsure about some of it, say so. Working it out is part of the scoping conversation.
Two things matter more than the rest: the region, because a language isn't one accent, and the script rule, because it decides how every line gets written.
Open datasets are a sensible place to start where they fit. AI4Bharat, a research lab at IIT Madras, publishes open speech and text resources for Indian languages, which you can find on the AI4Bharat website.
When your product needs a language, an accent, a domain or a recording condition that open data doesn't cover, that's where custom collection comes in. The NDA comes first, before any data moves, and we use your data only for the scope we agree.
Same order every time. The difference is who does each step: people who speak the language and know the region.
Language, region, script rule, domain and a rough amount.
Signed before any data moves.
Native speakers record or write in our own tools.
Written down by the agreed script rule, then reviewed.
Intents, entities and speakers tagged and checked.
Held-back data checks your model region by region.
Each language has its own page: the data we can produce in it, what to know before you brief it, and how a project runs. All 100 languages are on the languages directory.
Indian languages are where we go deepest, but we work in languages from all over the world. The translation page shows how the two meet.
Short definitions, so a brief and a reply mean the same thing to both sides.
Every one of these works in Indian languages. You can also compare all eight.
Speech and text from native speakers.
Audio written down by the agreed script rule.
Calls and dialogue for voice bots.
Checked by native speakers.
Text and audio labeled in the language.
What people ask us most about Indian languages. The services FAQ covers how the services fit together.
Speech, text and translated data in the languages people in India actually use, recorded or written by native speakers, so AI models can understand and respond to them. That covers the scripts, the accents and the everyday habit of mixing languages.
We go deep on Indian languages, well past Hindi and English, though we work in languages from all over the world. Tell us the language and the dialect you need.
That depends on how your product will use it. Agree the convention first: each language in its own script, one script for everything, or Latin letters. Tell us your target and the transcribers follow it consistently.
Yes. Tell us the region along with the language, because Hindi in Delhi, Bhopal and Patna doesn't sound the same. We plan the speakers around the places your users live.
Sometimes, and it's worth checking first. AI4Bharat publishes open speech and text resources. When you need a language, accent, domain or recording condition that open data doesn't cover, custom collection fills the gap.
No. We work in languages from all over the world. Indian languages are where we have particular depth.
Yes. Send a short brief with the language and dialect, and we'll put together a small sample free before you commit.
Say which language and dialect, whether it's speech or text, and roughly how much. We'll put together a small sample free before you commit.
This opens your email app with the brief filled in. Nothing is sent until you press send. You can also use the contact page.