Who we work with
Indian AI Training Data for LLM Companies
Foundation and applied LLM teams that need Indian-language human data with provable provenance, covering languages their web crawl barely touched.
- Typical engagement
- Instruction-response pairs
- Languages
- 14 + Indian English
- Model
- Direct or white-label
The problems that bring teams here
- Web-scraped Indian-language text is thin, noisy and heavily transliterated
- Code-mixed Hinglish is nearly absent from any structured training source
- Provenance and consent for human-generated data must survive external audit
What you are actually buying
Need 500 hours of Marathi speech from 1,000 speakers? Need 2,000 Hindi speakers? Need natural Hinglish conversations? Need Indian English accents across substrate groups?
Those are specifications, not projects. Send the spec and you get a quote against it. If the spec is not written yet, a 20-minute scoping call produces one.
How teams like yours evaluate a data partner
- Is every item traceable to a screened, consenting contributor?
- Can contributors be screened by domain expertise, not just language?
- Is there an adjudication process for disagreement on subjective tasks?
Typical scope
Instruction-response pairs, preference rankings and evaluation sets across 6-12 Indian languages.
Contract and licensing points you will raise
- Auditable provenance records
- Contributor consent for model training and distribution
- No third-party or scraped content in deliverables
How the engagement runs
- You send requirements, or we scope them with you
- We return a written specification, timeline and fixed quote
- You approve; recruitment and prompt design begin
- Sessions run across the studio network with progress reporting
- QA, packaging and staged delivery against the manifest schema you specified
The numbers we hold ourselves to
- 100% of delivered files pass automated technical QA for SNR, clipping, duration and silence
- 5-25% of files pass a second native-speaker content review, stratified by city, dialect and transcriber, and escalating to 100% on any batch that fails the agreed threshold
- Accepted yield runs 85-90% for scripted speech, 60-70% for spontaneous, 55-65% for conversational and 50-60% for telephony
- Default cohort quotas: 50/50 gender, with age bands at 30% (18-25), 40% (26-40) and 30% (41-60)
- 48 kHz / 24-bit capture, delivered as 16-bit PCM WAV, with studio sessions held below a -50 dBFS noise floor
- First response within one working day; a scoped, fixed quote within two to three
These are the figures a delivery is measured against, not aspirations. A batch that misses them is re-recorded at our cost rather than repaired.
Frequently asked
How quickly can you start?
Specification and recruitment usually take one to two weeks; recording starts immediately after. Low-resource languages take longer to field and should be started first in a multi-language programme.
Can you work under our brand?
Yes. White-label delivery is standard for data vendors and platforms who hold the end-client relationship.
Do you handle consent and provenance?
Every participant signs consent covering AI training and downstream model distribution, and consent records map to file and item IDs in the delivered manifest.
What if a batch fails QA?
It is re-collected. The commercial terms cover re-collection rather than partial credit, because a partially usable dataset costs you more than a late one.
Send us your requirement
Language, hours, speakers, demographics, format, deadline. That is enough for a quote.