Train
Train provides governed, multilingual training data for modern AI systems — including large language models, agentic systems, and multimodal models. LILT combines expert human intelligence, rigorous quality controls, and platform-level auditability to deliver high-fidelity training signals that improve model performance and eliminates hidden risk.
Research-designed frameworks that prioritize precision and reproducibility for production AI systems.
Proven methods to normalize judgment across diverse cultural contexts and low-resource language variants.
Identity/compliance controls plus ongoing QA, calibration, and agreement tracking to keep SFT data stable over time.
Produce high-quality labeled data to improve instruction following, task accuracy, and domain adaptation across languages and regions.
Expert-authored and expert-reviewed dataMultilingual and domain-specific coverageConsistent guideline enforcement at scaleGenerate preference data and feedback signals to align model behavior with human expectations and policy constraints.
Structured preference rankingCalibration across raters and localesBias and drift monitoring over timeSupport reward-based training where outputs can be programmatically or logically validated.
Task decomposition and reward definitionHuman verification for edge casesSignal validation before training ingestionCreate training datasets across text, audio, and multimodal inputs to support global and multimodal AI systems.
Multilingual prompt and response generationSpeech and audio annotationCross-modal consistency checks
Not commodity labeling — AI research expert-led by design
Multilingual by default, not retrofitted
Aligned to evaluation and governance workflows
Production-ready, not research-only
Train is built for teams that need reliable training signals, not just volume.
Tasks are designed by linguists and domain specialists to ensure clarity, cultural accuracy, and relevance to the target model behavior.
Work is performed by vetted, language-native and domain-qualified contributors — not generalist crowd labor.
All outputs are evaluated using LILT’s quality and signal engine, including agreement analysis, rater calibration, and drift detection.
Training data is delivered with full metadata, lineage, and documentation suitable for regulated or high-risk deployments.
Build datasets that hold up across every market.
Instruction tuning for multilingual LLMs
Alignment and preference modeling
Agent behavior training
Domain-specific model adaptation
Safety-sensitive or regulated AI systems