Service
Human Data for LLM Projects in India
Human-generated text and speech for LLM training and evaluation in Indian languages: prompts, preference rankings, instruction-response pairs, red-teaming and cultural-fit review.
- Turnaround
- 2-6 weeks depending on task complexity and contributor screening depth.
- Languages
- 14 Indian languages + Indian English
- Delivery
- Task data in your schema
What you get
- Task data in your schema
- Rubric and calibration results
- Contributor metadata (anonymised)
- Agreement statistics
Technical specification
Every parameter below is written into the statement of work before recording begins. If your pipeline needs different values, they replace ours rather than being converted after delivery.
| Parameter | Standard |
|---|---|
| Task types | Prompt writing, response ranking, instruction-response pairs, adversarial testing |
| Languages | Any language in the network, including code-mixed Hinglish |
| Contributors | Screened by domain, education band and language proficiency |
| Agreement | Overlapping assignments with adjudication |
| Provenance | Per-item contributor and time records |
How the work runs
- Task specification and rubric design
- Contributor screening against the rubric
- Calibration round with feedback
- Production with overlap and gold items
- Adjudication and delivery
Quality control
Every item is traceable to a screened contributor, which matters when a model vendor audits your data provenance.
QA failures are remedied by re-collection, not by editing the delivered files. Repaired audio introduces artefacts that survive into your model.
Speaker and contributor sourcing
Contributor pools are built per domain; the same pool is not reused across conflicting tasks.
Consent is captured per participant and mapped to file IDs, so provenance survives an external audit of your training data.
Timeline
2-6 weeks depending on task complexity and contributor screening depth.
Staged delivery is available: first batches ship while later batches are still recording, so training can start early.
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.
Commonly used for
- LLM fine-tuning
- RLHF-style preference data
- Multilingual evaluation
- Red-teaming
Frequently asked
What is the minimum volume for llm human data?
Programmes typically start around 50 hours or equivalent units per language. Smaller pilots are accepted when they lead into a larger build, because most of the setup cost is in specification and recruitment rather than recording time.
Can you work to our schema instead of yours?
Yes. Manifest fields, file naming, directory structure and label schema are set by you. Working to your schema from the start avoids a conversion pass that usually loses metadata.
Who owns the delivered data?
You do. Deliverables come with a perpetual, transferable licence and participant consent that covers model training and distribution of the resulting model.
How is pricing structured?
Per delivered hour or per unit, quoted against a written specification. Quotas, recording conditions and QA thresholds all move the price, which is why we quote from a spec rather than from a price list.
Get a quote for human data for llm projects
Send the specification you already have, or the rough shape of it, and you get a scoped quote with a timeline.