LLM Training Data For The Global Majority.
We build SFT, RLHF, and evaluation data for foundation models. Not just in English—across 480+ languages and 3,420 dialects using domain-certified human evaluators.
Who This Is For
Foundation Model Teams
Organizations training base models requiring massive, clean multilingual corpora and instruction-following sets.
Fine-Tuning Teams
Engineers adapting models for specific domains (legal, medical, coding) in non-English markets.
RLHF & Alignment
Safety and red-teaming units needing culturally aware human evaluators to rank preferences and flag harm.
Evaluation Programs
Teams benchmarking model performance against localized, dialect-specific QA datasets.
Human-in-the-Loop LLM Data Streams.
Most LLM training data exists for English. We build it for the other 479 languages, utilizing verified native speakers with domain expertise.
Supervised Fine-Tuning (SFT)
- High-quality instruction-following pairs
- Creative writing and reasoning generation
- Domain-specific factual QA creation
- Summarization and extraction datasets
Preference & Alignment (RLHF)
- Culturally nuanced response ranking
- Adversarial red-teaming in local dialects
- Safety, toxicity, and bias evaluation
- Model rewriting and response correction
How It Works
A structured, auditable process designed for enterprise scale.
Guideline Calibration
Guideline Calibration
Ingest prompt taxonomies, safety rubrics, and formatting rules for target language context.
Evaluator Qualification
Evaluator Qualification
Native speakers assessed on your specific reasoning or ranking tasks before assignment.
Generation & Ranking
Generation & Ranking
Contributors execute SFT writing or RLHF preference ranking within tracked annotation environments.
L2 QA Escalation
L2 QA Escalation
Senior SMEs review ambiguous edge cases, focusing on cultural context and reasoning validity.
Delivery & Sync
Delivery & Sync
Batched data payloads delivered in standard formats to your engineering pipelines.
Rare-Language LLM Execution
Scaling data collection in French or Spanish is a procurement problem. Scaling it in Tigrinya, Hausa, or Quechua is an infrastructure problem.
Governance & Quality Automation
Subjective LLM evaluations require more than spot checks. We implement rigorous multi-layer validation to ensure logical depth and factual correctness.
- Inter-Annotator Agreement (IAA): Continuous statistical tracking of reviewer divergence on preference ranking tasks.
- Blind Golden Sets: Hidden test questions integrated into standard workflows to verify ongoing annotator calibration.
- Domain Validation: Scientific, coding, or legal reasoning tasks are automatically restricted to reviewers with verified domain credentials.
Governed LLM Data vs. Generic Crowd Output
The structural gap between calibrated, domain-expert evaluation data and unqualified annotation.
- Unvetted annotators without domain expertise
- No linguistic or cultural calibration
- Inconsistent formatting and schema adherence
- No blind golden set verification
- High inter-annotator disagreement
- Domain-certified native speakers with blind testing
- In-market cultural and dialectal calibration
- Schema-enforced structured output delivery
- Continuous blind golden set verification
- Statistical IAA monitoring and drift detection
- Unvetted annotators without domain expertise
- No linguistic or cultural calibration
- Inconsistent formatting and schema adherence
- No blind golden set verification
- High inter-annotator disagreement
Supported Deliverables
Related Programs
Explore how we deliver llm training data at global scale.
45,000 instruction pairs written by financial professionals, not scraped
Recruiting financial professionals across 8 sub-domains to author 45,000+ verified instruction-response pairs with <5% post-review revision rate.
What 'helpful' means in 25 different cultures
Deploying native-speaker evaluator teams across 25 languages to produce 120,000+ culturally calibrated preference judgments for model alignment.
Service FAQ
Common operational and scoping questions regarding this specific pipeline.
We bypass standard crowd platforms entirely. For domains like coding or legal reasoning, we recruit directly from professional networks and universities, subjecting candidates to blind tests before admitting them to the active reviewer pool.
Yes. Our SFT writing teams are trained dynamically on your specific structural requirements, including proper markdown usage, chain-of-thought XML tags, and verifiable code logic.
In-market native speakers perform the evaluation. A prompt translated to Arabic and evaluated by a diaspora speaker in London will yield different safety flags than one evaluated by a resident of Riyadh. We map reviewers to the target cultural geography.
Once initial calibration is locked (typically 1-2 weeks), steady-state execution speed depends on reviewer allocation volume. We routinely process multiple thousands of high-complexity preference rankings per week per language.
Request an LLM Data Workflow Review
Share your prompt taxonomy and language targets. We'll outline our execution capacity and QA model.