High-quality training data to push the frontier of AI models

We work with expert contractors—STEM PhDs, multilingual specialists, and more—to generate difficult prompts, ground-truth data, and RLHF evaluations, delivering high-quality training/eval data.

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Build Better AI Models

Supervised Fine-Tuning (SFT)

High-quality prompt–response pairs with full chain-of-thought reasoning examples form the backbone of these datasets. They span diverse domains, including STEM, legal, multilingual, and medical tasks.

By showing step-by-step reasoning, they teach models the correct paths rather than just the final answers. Each example is validated by expert contractors to ensure accuracy and minimize noise.

Reinforcement Learning Human Feedback (RLHF)

Structured human inputs ground model behavior in real-world preferences. Expert annotators evaluate outputs across task-specific metrics, not just correctness.

These datasets capture edge cases such as adversarial prompts and complex long-form reasoning. They also support both pairwise comparisons and scalar ratings, enabling flexible evaluation pipelines.

Structured human inputs ground model behavior in real-world preferences. Expert annotators evaluate outputs across task-specific metrics, not just correctness.

These datasets capture edge cases such as adversarial prompts and complex long-form reasoning. They also support both pairwise comparisons and scalar ratings, enabling flexible evaluation pipelines.

Evaluation Benchmarks

Datasets that measure valuable benchmarks provide stress tests for ambiguity, compositional reasoning, and domain transfer. They are designed to expose how models perform under challenging conditions rather than just on standard test sets.

They also capture rare failures such as hallucinations, bias, and factual drift that often surface post-deployment. Each benchmark is built with reproducible protocols, enabling longitudinal tracking of genuine improvements versus overfitting.

Domain-Specific Datasets

Vertical datasets co-designed with subject-matter experts push frontier capabilities in specialized domains. Examples include coding datasets with stepwise debugging traces, biomedical Q&A with citations, and robotics control sequences built for training and evaluation.

These datasets fill gaps where public resources are either too shallow, such as GitHub code snippets, or too noisy, like web-scraped medical advice. They also capture failure-prone edge cases unique to each domain, ensuring models are trained on the challenges that matter most.