Teach your model the judgement your users actually expect.
Preference data, red-teaming, DPO and culturally-calibrated evaluation — so your model learns the judgement your users expect.
Preference pairs, DPO datasets, red-teaming and human evaluation calibrated to real Indic linguistic and cultural context — not translated Western preferences.
- Modalities
- Text, Multimodal
- Languages
- EN, HI, TA, TE, BN, MR, +12 Indic
- Quality bar
- IAA ≥ 0.85 · two-pass QA
- Onboarding
- NDA → SOW in 14 days
A model can be fluent and still be wrong about what people actually want. Off-the-shelf preference data encodes Western norms; ported to Indic users it gets honorifics, formality, safety and cultural nuance subtly — and sometimes dangerously — wrong.
Building preference and DPO data in-house needs calibrated raters, a defensible rubric, and red-teaming that surfaces harms your English-only guardrails never see.
Preference and DPO datasets, red-team sets and human-eval results that move the metrics that matter — alignment to your users' actual judgement, with documented rater calibration, rubric and inter-rater agreement.
Where teams put this to work
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Foundation Model Labs
Indic preference & DPO pairs
Native-speaker preference pairs calibrated for honorifics, formality and cultural nuance, with inter-rater agreement reported.
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Safety
Red-teaming for culturally-specific harms
Native-speaker red-team prompts surfacing caste, communal and regional harms that English-only guardrails miss.
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Enterprise GenAI
Human evaluation of model responses
Rubric-based human evaluation with calibrated raters to benchmark alignment before and after fine-tuning.
A pipeline built for SLAs
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1
Guidelines
Co-write annotation guidelines with the client team and pilot reviewers.
Calibrated rubric
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2
Pilot
Run a 500-unit pilot to validate guidelines and tooling.
IAA ≥ 0.80
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3
Production
Scale to full volume with daily QA sampling and weekly calibration.
IAA ≥ 0.85
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4
Delivery
Versioned drops with audit logs, kappa reports, and dataset cards.
Audit-ready
Quality gates
- Calibrated raters with a documented, piloted preference rubric
- Inter-rater agreement reported alongside every delivery
- Red-team coverage across culturally-specific harm categories
- Gold references and adjudication for contested pairs
At a glance
What does RLHF & Evaluation cover?
The technical spec for this service — modalities, language coverage, deliverable formats and the QA stages every batch passes through.
| Specification | Detail |
|---|---|
| Modalities | Text, Multimodal |
| Languages / coverage | EN, HI, TA, TE, BN, MR, +12 Indic |
| Deliverable formats | Preference pairs (JSONL), DPO datasets, red-team sets, human-eval scores |
| QA stages | Rater calibration → rubric scoring → adjudication of contested pairs → inter-rater agreement report |
Representative spec. Exact modalities, languages, formats and acceptance thresholds are scoped per project in the SOW.
Trust & compliance
Data you can defend in a procurement review
Compliance is a feature, not a footnote — every delivery is built to clear legal, security and regulatory scrutiny.
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Rights-cleared & consented
Written contributor agreements granting commercial reuse, with a consent reference and authorship log per record.
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India-residency capable
Collection, annotation, processing and storage available in-country, aligned with the DPDP Act, 2023.
Sovereign delivery -
Documented & measured
Published quality metrics, provenance and license terms on every delivery — transparency technical buyers can audit.
FAQ
RLHF & Evaluation — common questions
- Is the data rights-cleared and safe to use commercially?
Yes. Every record is created or sourced under written agreements granting commercial reuse, with a consent reference and authorship log attached. We don't ship scraped data you can't legally train on.
- How do you measure and report quality?
We publish quality metrics on every delivery — inter-annotator agreement, QA pass rate, and (for speech) word error rate — with two-pass QA and adjudication. Each drop ships with a data card documenting methodology and provenance.
- Can you guarantee India data residency?
Yes. We offer sovereign delivery: work performed and stored entirely within India, aligned with the DPDP Act, 2023. See our Sovereign Data page for the full residency and compliance posture.
- How do we get started?
Tell us your languages, modalities, volume and timeline. A senior PM scopes the work and replies within one business day; we move from NDA to a signed SOW in about 14 days.
Scope a pilot for your next data program.
Tell us what you need — we'll recommend the right approach, quality bar and a phased plan. Or browse off-the-shelf datasets in the catalog.
Where this applies
Ready to scope a pilot?
A senior Language PM will scope your project and respond within one business day.
Talk to a Language PM