We curate, annotate and quality-check the data large language models learn from, and we work with world-leading LLM providers to do it.
We source, filter, deduplicate and structure large text and code corpora so models learn from signal, not noise.
Human-written prompts and high-quality responses for supervised fine-tuning, across tasks and difficulty levels.
Careful comparisons and ratings of model outputs that teach models what "better" means (RLHF and related methods).
Hard, held-out test sets that measure real capability and catch weaknesses before a model ships.
Training data quality decides model quality. Our process makes sure every item is accurate, consistent and traceable.
We agree on the data type, volume, quality bar and delivery format.
We deliver a small batch so you can check quality and tune the guidelines.
We ramp up the team with quality checks built into every batch.
We deliver regular, versioned batches with quality reports attached.
Tell us what data you need. We'll propose a pilot so you can judge the quality yourself.