Build Better Generative AI
With Expert Training Data
RLHF datasets, instruction tuning corpora, and safety annotation from domain specialists. Human feedback that helps your model learn what actually matters.
Training Data for Every Stage of LLM Development
From initial fine-tuning to safety alignment and evaluation. We build the data pipelines that move your model from prototype to production.
RLHF & Preference Data
Human feedback datasets that teach your model to follow instructions, rank outputs, and avoid harmful content. Expert annotators with domain knowledge across technical fields.
- Preference ranking
- Comparative labeling
- Harm detection
Instruction Tuning Data
High-quality instruction-response pairs across dozens of task types — coding, reasoning, summarization, translation, and domain-specific tasks.
- Task diversity
- Quality validation
- Style consistency
Multimodal Annotation
Image-text pairs, video captions, and audio transcripts for vision-language models. Structured annotation pipelines for large-scale multimodal datasets.
- Image captioning
- Visual QA
- Video description
Red Teaming & Safety Data
Adversarial prompts and safety evaluation data to identify model weaknesses. Systematic coverage of failure modes, jailbreaks, and alignment gaps.
- Adversarial prompts
- Safety labeling
- Policy violation detection
Domain-Specific Corpora
Custom datasets for medical, legal, financial, and technical domains. Subject-matter expert annotators who understand field-specific language and standards.
- Medical & clinical text
- Legal document annotation
- Technical terminology
Evaluation & Benchmarking
Build evaluation sets that measure what matters in your use case. Human-graded outputs for accuracy, coherence, helpfulness, and domain accuracy.
- Custom eval sets
- Human grading pipelines
- Benchmark construction
Training Data Quality Determines Model Quality
Model architecture matters. But the quality, diversity, and accuracy of your training data is what separates a useful LLM from an unreliable one.
Expert Human Annotators
Not crowd-workers — domain specialists in coding, science, medicine, and law who provide nuanced judgments your model can learn from.
Structured Quality Control
Multi-stage review, inter-annotator agreement measurement, and automated consistency checks at every step of the pipeline.
Scalable Data Pipelines
From 1,000 to 1,000,000+ examples. Flexible batch delivery with real-time progress tracking and format compatibility with major training frameworks.
Data-First AI for Generative Models
We build training pipelines around your model's specific requirements — not generic templates. Every dataset is purpose-built for your use case.
Preference Ranking Data
Side-by-side comparison datasets where annotators rank model outputs by quality, helpfulness, and accuracy — the foundation of RLHF pipelines.
Instruction-Response Pairs
Diverse, high-quality Q&A and task-completion examples that teach models to follow complex instructions across a wide range of topics.
Safety & Alignment Labels
Systematic labeling of harmful, misleading, or policy-violating outputs. Built to the annotation guidelines used by leading AI safety teams.
Code Annotation
Technical annotation by software engineers — code review, bug detection, documentation quality assessment, and correctness labeling.
Multilingual Datasets
Native-speaker annotation in 40+ languages for translation quality, cross-lingual understanding, and multilingual instruction following.
Evaluation Benchmarks
Custom evaluation sets designed around your specific capabilities and failure modes — more informative than standard academic benchmarks.
Nordic Precision Meets AI Expertise
Scandinavian attention to quality, combined with deep expertise in what makes generative AI actually work in production. We treat your training data as the strategic asset it is.
- Domain experts, not generic crowd workers
- GDPR-compliant data handling by default
- Transparent annotation guidelines per project
- Inter-annotator agreement reporting
- Compatible with HuggingFace, OpenAI, Anthropic formats
- EU data residency available
Discuss Your Training Data Requirements
Requirements Review
1 business day
Sample Annotation
3-day pilot set
Quality Validation
IAA measurement
Production Scale
Ongoing delivery
Request a Sample Dataset
Free pilot evaluation with your use case