Make your frontier model speak the world, not just English.
Multilingual evaluation, RLHF/DPO preference data, safety & red-team sets and custom collection — so your frontier model works beyond English.
AI labs and frontier model builders ship models that ace English benchmarks and quietly fail in Hindi, Tamil, Swahili or Bahasa. We are the native-speaker data partner behind multilingual evaluation, RLHF/DPO preference data, safety and red-team sets, and bespoke field collection — across 22 Indic languages and a 400-language footprint.
What teams in AI Labs & Frontier Model Builders are up against
Frontier capability is now table stakes; multilingual, culturally-grounded behaviour is the moat. Labs hit the same walls:
- English-centric evals hide the gap. A model can post strong MMLU scores and still mangle honorifics, code-mix and pragmatics in real Indic usage — and you won't see it until users do.
- Preference data is shallow outside English. RLHF/DPO pairs scraped or machine-translated don't capture how native speakers actually rank tone, formality and cultural fit.
- Safety guardrails miss local harms. Caste, communal, regional and code-mixed harms slip past English-only red-teaming.
- Long-tail languages have no data to buy. For Bodo, Santali or Maithili there is no corpus — it has to be collected in the field, with consent.
Compliance & residency
Frontier-scale data brings provenance and rights scrutiny from enterprise buyers and regulators:
- Provenance you can defend — every record tagged with source, consent basis and license tier for clean downstream training.
- DPDP Act, 2023 alignment — consent, de-identification and processing records for personal data captured in the field.
- India-residency option — collection, annotation and storage in-country for sovereign and regulated programs.
How we solve it
The multilingual data layer behind better frontier models
From evaluation that exposes the real gap, to preference data and field collection that closes it — one native-speaker partner.
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Multilingual evaluation & benchmarks
Gold eval sets that measure honorifics, code-mix, pragmatics and cultural reasoning — the failures English benchmarks never surface.
Cultural & cross-lingual evaluation -
RLHF & DPO preference data
Native-speaker preference pairs calibrated for tone, formality and cultural fit, with inter-rater agreement reported per batch.
RLHF & evaluation -
Safety & red-team data
Native-speaker red-teaming for caste, communal, regional and code-mixed harms that English-only guardrails miss.
Content moderation & safety -
Custom field collection
Consented, rights-cleared speech and text in low-resource languages with no corpus to buy — collected in the field to your spec.
Multilingual data collection
Proof
Why teams trust us with this vertical
- languages in our collection footprint
- 400
- inter-annotator agreement on preference & eval data
- ≥ 0.85
- native-speaker, in-region annotation — no machine-only labels
- 100%
languages in our collection footprint
22 Indic at production depth
inter-annotator agreement on preference & eval data
native-speaker, in-region annotation — no machine-only labels
every item human-reviewed
Go deeper
Datasets and services for this vertical
Jump straight into the catalog filtered for this domain, or scope a custom program.
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Browse the dataset catalog
See rights-cleared, documented datasets filtered to this vertical — or commission a custom set.
View datasets -
Explore our services
End-to-end collection, annotation, RLHF/DPO, evaluation and safety — applied to your use case.
All services -
Keep it sovereign
India-resident collection, annotation and storage for regulated and government-grade programs.
Sovereign Data
FAQ
AI Labs & Frontier Model Builders — common questions
- Can you build evaluation sets for languages we don't have benchmarks for?
Yes. We author gold evaluation sets with native speakers for low-resource and Indic languages, covering honorifics, code-mix, pragmatics and cultural reasoning — the dimensions standard English benchmarks ignore. See Cultural & Cross-Lingual Evaluation.
- How do you ensure RLHF/DPO data reflects real native-speaker preference?
Calibrated native-speaker raters, written rubrics and reported inter-rater agreement (IAA ≥ 0.85) — not machine translation or crowd guesswork. See RLHF & Evaluation.
- Can sensitive data stay in India?
Yes. We offer an India-residency option across collection, annotation and storage. See Sovereign Data.
- Do you collect languages that have no existing dataset?
Yes — field collection with consent is core to what we do. See Multilingual Data Collection and browse safety & eval datasets.
Build an AI data program for AI Labs & Frontier Model Builders.
Tell us your languages, modalities and use case — we'll scope a rights-cleared, documented data program and a delivery schedule.
Where our data services apply
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Multilingual Data Collection
Native-speaker audio, video, image and text collection across Indic, African and low-resource languages — field-grade, consented, documented.
Explore the service -
RLHF & Evaluation
Preference data, red-teaming, DPO and culturally-calibrated evaluation — so your model learns the judgement your users expect.
Explore the service -
Content Moderation & Safety
PII filtering, harmful-content taxonomies and culturally-aware safety pipelines — calibrated for Indian context.
Explore the service -
Cultural & Cross-Lingual Evaluation
Evaluation for honorifics, code-mix, idioms, caste-safety and pragmatic correctness — beyond translated MMLU.
Explore the service