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Text data for AI teams

Text data written, labeled and translated by people

Text data for AI is written language your model learns to read or write: prompts, questions, messages and reviews, paired with labels. People from our 30,000+ network write it in the languages and domain you need, and our annotators and translators label and translate it.

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30,000+
people in our network, speaking languages worldwide
150+
projects delivered directly for end clients since 2021
8
in-house tools behind these services, built by us
1
named contact from your brief to the last file
The short answer

What is text data?

Text data for AI is written language paired with labels, like the intent behind a message or the names and dates in it, that a language model learns to read or write from. The text shows how people really write, and the labels tell the model what it means.

Good text data sounds like your users. The same request written by many people, in their own words, teaches a model far more than one tidy version repeated.

It usually needs a label set agreed up front, and in multilingual products it needs the same care in every language, scripts and spelling habits included.

Labels are only as useful as they are consistent, and our guide to data annotation quality shows what to look for before you trust a labeled set.

Text data at IndecalAI

Writing
Prompts, questions, answers and everyday messages, written by people in our 30,000+ network.
Labels
Classification, entity tagging and sentiment, with a multi-level review on every batch.
Translation
Human-verified translation in our own Translation tool.
Languages
Languages worldwide, with deep coverage of Indian languages, native scripts and Latin letters.
Domains
The domain you need, like banking, healthcare or retail support.
Quality
Reviewed before delivery.
Your data
NDA first, used only for the agreed scope, returned or deleted when you ask.
Contact
One named contact who replies within one business day.
First step
A small free sample before you commit.
Company
Based in Ahmedabad, India, since 2021, with 150+ projects delivered.
AI showcase

Follow one message through

One customer request, from how people wrote it to how it reads in Hindi. Switch layers to see each stage. It's an example, and your layers are agreed with you.

Pick a part of the sample

The writing

The same request written by many people in their own words, so your model learns how users really phrase things.

  • Writers People from our 30,000+ network.
  • Task Prompts, questions, answers or messages.
  • Domain Banking, healthcare, retail or yours.

Labels

The intent behind each message and the entities inside it, tagged by trained annotators and checked. See data annotation.

  • Intent One class per message, from a set you agree on.
  • Entities Products, dates, times, places and more.
  • Review Multi-level review on every batch.

Sentiment

Reviews and messages labeled positive, negative or neutral by people who catch sarcasm and local slang.

  • Scale Three grades, or finer if you need them.
  • Sarcasm Caught by people who read the language.
  • Use Feedback analysis and support analytics.

Translation

The text carried into other languages with its labels intact, reviewed by people who know the language. See translation and localization.

  • Review Linguistic and cultural review on top.
  • Labels Carried across and checked.
  • Tool Our own Translation tool.
What we deliver

The text work we do

Text written by people in your domain, labels for classes, entities and sentiment, and translation into the languages your users read, Indian languages included.

Written by people

Prompts, questions, answers and everyday messages, written by people in the languages and the domain you need.

For chat assistants, search and language models

Free sample

Classification

Each message gets one label from a set you agree on, with a clear rule for what counts as "other".

For routing, intent detection and content filters

Free sample

Entity tagging

Names, products, places, dates and times marked in the text, so your model can pull them out on its own.

For assistants, search and document reading

Free sample

Sentiment

Reviews, chats and messages labeled positive, negative or neutral, sarcasm included.

For feedback analysis and brand tracking

Free sample

Translation

Text translated into the languages your model has to handle, with a linguistic and cultural review on top of any machine draft.

For multilingual assistants and datasets

Our tool: Translation Free sample

Indian language text

Text in Indian languages well past Hindi, in native scripts and in the Latin letters people often type on phones.

For apps and assistants built for India

Free sample

Where it's used

What teams train on it

Common uses for text data. They're examples of the work, not a list of clients. If your project goes beyond text, see speech data, image data and video data. Two example categories: English e-commerce support text and paired chat and voice support data.

Chat assistants

The same request written many ways, so an assistant understands users who don't phrase things like the docs.

Written by people

Routing and intent

Messages labeled by topic and urgency, so a model sends each one to the right place.

Classification

Search and extraction

Products, dates and places tagged, so a model can pull out what the user actually asked for.

Entity tagging

Feedback analysis

Reviews labeled for sentiment, sarcasm included, so a model reads the mood correctly.

Sentiment

Multilingual products

Text in every language your product supports, native scripts and Latin letters included.

Indian language text

Testing a language model

Held-back text in each language and domain, to check your model where it matters.

Data and model evaluation

Text data categories

Text data by industry and language

Each page covers what that kind of text project usually needs, and each one is built to your brief. Every category, including speech, image and video, is on the datasets hub.

English e-commerce support text Healthcare patient messaging text Hindi BFSI chat support text Tamil BFSI complaint classification German support chatbot text Spanish chatbot intent classification Portuguese retail customer feedback French retail review sentiment Arabic social media sentiment Mandarin support ticket triage Enterprise email and ticket logs Enterprise forms and documents Enterprise spreadsheet and CRM data
Before writing starts

Four calls to make before anyone writes a word

Get these wrong and no amount of volume fixes it. We settle them with you at the start.

Where the text comes from

Written new by people from our network, or your own text to label, or both.

The label set

Every class and entity type, including what counts as "other", agreed before the first label.

Languages and scripts

Which languages, and whether text is written in native scripts, Latin letters or both.

Personal details

Whether writers use invented names and details, and what the text may be used for afterward.

How it works

From the writing brief to labeled text

One named person looks after the project. Each step links to the service that handles it.

  1. Brief

    You tell us the domain, the languages and what the text is for.

  2. NDA first

    The NDA is signed before any data moves.

  3. Writing

    People from our network write the text in their own words.

  4. Labeling

    Annotators label it, and every batch is reviewed.

  5. Translation

    Text carried into other languages, labels intact.

  6. Evaluation

    Held-back text checks your model language by language.

Languages

Text in the languages your users write in

We work in languages from all over the world and go deep on Indian ones, well past Hindi and English, in native scripts and in Latin letters. Our page on Indian language data has the details.

Glossary

Language data terms, plainly put

Short definitions for the words that come up on language projects.

Natural language processing (NLP)
The field of AI that reads, understands or writes human language.
Corpus
A collection of text gathered for a purpose, like training or testing a language model.
Intent
What the writer wants done, like moving a delivery or checking a bill.
Named entity
A specific thing mentioned in the text, like a person, product, place, date or amount.
Sentiment
Whether a piece of text reads as positive, negative or neutral.
Label set
Every class and entity type a text can get, agreed before labeling starts.
Transliteration
Writing a language in another script, like Hindi typed in Latin letters.
Code-mixing
Using two languages in one message, like Hindi and English in the same sentence.
Related work

Services behind a text project

Text work usually spans a few of these. You can also compare all eight.

Data collection

Text written by people in our network.

Data annotation and labeling

Classes, entities and sentiment labeled.

Translation and localization

Text carried into other languages.

Conversational AI data

Dialogue for chat and voice assistants.

Synthetic data

Generated text for rare cases, reviewed by people.

FAQ

Questions about text data

Common questions about text data. The services FAQ covers how the services fit together.

What is text data?

Written language paired with labels, like the intent behind a message or the names and dates in it, that a language model learns to read or write from.

Who writes the text?

People from our 30,000+ network, in the languages and the domain you need: prompts, questions, answers and everyday messages.

Can you label text we already have?

Yes. Trained annotators label your text for classes, entities and sentiment, and every batch gets a multi-level review. See data annotation.

Which languages and scripts do you cover?

Languages from all over the world, with deep coverage of Indian languages, including text in native scripts and in Latin letters. Our Indian language data page has more.

Can you translate the text as well?

Yes. Translation runs in our own Translation tool, with a linguistic and cultural review on top. See translation and localization.

How much text do we need?

It depends on the task, the number of labels and how varied your users are, so there's no standard number. We plan it with you, and the free sample shows quality before you commit to volume.

Can we try a small sample first?

Yes. Send a short brief and we'll write or label a small sample free before you commit.

Does our data stay private?

Yes. The NDA is signed before any data moves, we use it only for the scope we agreed, and only the people working on your project can get to it. It's returned or deleted when you ask.

Start with a free sample

Pick what you need, add the languages and roughly how much text, and send it. We'll write or label a small sample free before you commit.

  1. Your named contact replies within one business day.
  2. We sign an NDA before any data changes hands.
  3. We write or label your free sample, then agree the full scope.
What do you need?
Email this brief

This opens your email app with the brief filled in. Nothing is sent until you press send. You can also use the contact page.