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Karmyug

Data for conversational AI

Assistants fail on the way people really talk: interrupted, mixed-language, half-finished. Train and test them on that.

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What Conversational AI needs

A conversational system needs more than clean question and answer pairs. It needs multi-turn dialogue that keeps context, speech with real accents and background noise, and an honest read of where the assistant gets lost.

We build the dialogue, speech and evaluation data around your assistant: the intents and domains it must handle, the languages and dialects of your users, and the failure cases from your logs.

Data types

  • Speech
  • Text
  • Dialogue

Industries

  • Banking and finance
  • Telecom
  • Retail and e-commerce
  • Healthcare
  • Automotive
  • BPO and call centres

The data it takes

  • Multi-turn dialogue

    Written or recorded conversations that hold context, persona and goals across many turns.

  • Spoken utterances

    Commands and questions recorded by real speakers on real devices, in real rooms.

  • Intent and slot labels

    Utterances labelled by intent, entities and sentiment to your taxonomy.

  • Response ratings

    Human scores for helpfulness, tone and correctness of the assistant's replies.

  • Code-switched language

    Conversation that moves between languages mid-sentence, as users actually do.

How we collect it

  1. 01

    Scripted and spontaneous

    Contributors act out scenarios from your intent list, and also talk freely about a topic, so you get both coverage and naturalness.

  2. 02

    Recorded on target devices

    Phones, smart speakers and in-car units, at the distances and noise levels your product meets.

  3. 03

    Labelled by native speakers

    Intent, entity and sentiment labels applied by speakers of each language, with a second review.

  4. 04

    Rated against a rubric

    Assistant replies scored by trained raters, with a written reason for low scores.

Services that deliver it

Related datasets

Full catalogue
speech

General conversation speech

Spontaneous telephonic conversation from native speakers, recruited against region quotas.

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speech

Call centre speech

Real support calls with agent and customer channels separated and code-switching preserved.

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speech

BFSI call centre speech

Banking support calls covering balance, dispute, collections and onboarding flows.

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Tell us about your Conversational AI project.

Describe the model and what it gets wrong today. You get volumes, languages, a quality target and a price, usually inside two working days.

  • Reply within one working day
  • A pilot batch you accept before we scale

Request Conversational AI data

The more specific the failure you are trying to fix, the more useful our first reply will be.

We use this only to reply to you. No sequences, no list. See our privacy policy.

Questions we get asked

Can you cover code-switched Indian languages?

Yes. Mixed-language speech and text is collected and transcribed as spoken, with language tags on each span, rather than normalised into one language.

Do you provide synthetic dialogue?

We write dialogue with human writers and can use model drafts only where you allow it, always reviewed by a person.

Other use cases

Speech AI

Speech data for ASR, voice interfaces and language understanding.

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Generative AI

Human-generated and evaluated data for modern generative AI systems.

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LLM / NLP

Language data for training, evaluation and language understanding.

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