Generative AI Data Services
Human-generated training data that makes your models genuinely capable
Solnix designs RLHF pipelines, preference ranking workflows, and expert-generated instruction datasets that align large language models to your specific task requirements, safety standards, and domain knowledge.
Overview
The human signal that shapes how your model behaves
Generic datasets make generic models. Solnix produces the domain-specific, expert-quality human signal, preference rankings, instruction pairs, adversarial examples, that shapes how your model reasons, responds, and refuses. The difference between a model that almost works and one your users trust.
What's included
RLHF & Preference Ranking
End-to-end RLHF pipelines, from pairwise ranking interface design to annotator training, quality scoring, and reward model training data delivery.
Instruction Tuning Datasets
Expert-authored instruction-response pairs across your specific domain, covering task types, difficulty gradients, edge cases, and format requirements that generic datasets miss.
Human-Generated Gold Standard Data
For tasks where synthetic data fails, creative reasoning, nuanced judgment, domain expertise, we source specialists who produce gold-standard outputs your model learns from.
Expert Data Generation
Specialist annotators in legal, medical, financial, scientific, and technical domains produce high-quality labeled data that requires genuine expertise, not crowd workers.
Adversarial & Safety Data
Red-team datasets, adversarial prompts, jailbreak attempts, and safety boundary examples that help your model refuse harmful requests and handle edge cases robustly.
Synthetic Data Augmentation
When real-world examples are scarce, we design synthetic data generation pipelines that produce diverse, validated examples, tested against real distributions before use.
Developer experience
Simple API. Powerful results.
Integrate in minutes with our SDK. Full TypeScript support, comprehensive documentation, and live examples for every feature.
How it works
From setup to production
Task Specification
We define the task taxonomy, annotation schema, difficulty distribution, and domain coverage requirements for your specific model alignment goals.
Expert Recruitment & Training
Domain experts are recruited, evaluated for qualification, and trained on your annotation guidelines before production begins.
Annotation & Quality Control
Preference pairs and instruction data are produced with multi-stage QA, inter-annotator agreement measurement, and calibration checks.
Dataset Delivery & Integration
Datasets are delivered in JSONL/HuggingFace format with train/validation/test splits and integrated with your fine-tuning pipeline.
FAQ
Common questions
Get started
Build the dataset that defines your model's capabilities
Talk to an expert and get a tailored implementation plan within 48 hours.