Make AI safe in the languages it actually fails in.
Native-speaker red-team, harm-taxonomy and evaluation datasets for low-resource and code-mixed languages — surfacing failures English benchmarks hide.
A model can pass English safety benchmarks and still produce culturally-specific harms in Indic and low-resource languages. We author red-team, harm-taxonomy and evaluation datasets from scratch — native-speaker, code-mixed, and IAA-measured — so you can measure and fix what translated benchmarks never test.
- Modalities
- Text, Audio, Multimodal
- Languages
- 22 Indic + low-resource & code-mixed (Hinglish, Tanglish, +global)
- Quality bar
- IAA ≥ 0.85 · two-pass QA
- Onboarding
- NDA → SOW in 14 days
Safety and evaluation are where multilingual AI quietly breaks. A model that aces a machine-translated benchmark can still emit caste, communal, gendered and regionally-specific harms in code-mixed and low-resource languages — and you won't see it, because no evaluation set tested for it.
The harder truth: for most of the world's languages there is no benchmark to translate, and LLM-as-judge shortcuts don't agree with human judgement on culturally-specific harms. You need native-grounded evaluation data authored by people who speak the language and know the culture.
Red-team prompts, a culturally-grounded harm taxonomy, and gold evaluation splits — authored (never scraped) by native-speaker experts, mapped to a shared taxonomy, with two-pass QA and reported inter-annotator agreement. You get evaluation data that surfaces the failures English benchmarks hide, in languages that have no benchmark to begin with.
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
- Native-speaker-authored red-team and gold references (never scraped)
- Culturally-grounded harm taxonomy (caste, communal, gendered, code-mix)
- Inter-annotator agreement (IAA) ≥ 0.85 with two-pass adjudication
- Coverage across low-resource & code-mixed languages with no prior benchmark
- Reproducible scoring; documented rubric per delivery
At a glance
What does Safety & Evaluation Datasets cover?
The technical spec for this service — modalities, language coverage, deliverable formats and the QA stages every batch passes through.
| Specification | Detail |
|---|---|
| Modalities | Text, Audio, Multimodal |
| Languages / coverage | 22 Indic + low-resource & code-mixed (Hinglish, Tanglish, +global) |
| Deliverable formats | Red-team prompt sets, harm taxonomy, gold eval splits (JSONL) + scoring rubric & coverage report |
| QA stages | Taxonomy design → native-speaker authoring → two-pass QA with adjudication → IAA reporting → reproducible scoring |
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
Safety & Evaluation Datasets — 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.
Ready to scope a pilot?
A senior Language PM will scope your project and respond within one business day.
Talk to a Language PM