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Anyone can label data.
Few can write the answer.

Expert-generated SFT, preference, critique, and reasoning data — built to your specification and delivered in the schema your pipeline expects.

From specification to schema-ready delivery.

Every item passes the same route — and the ones that don't hold up go back rather than through.

  1. SpecificationYour rubric, schema, and gold standards
  2. QualificationContributors assessed against the domain
  3. AuthoringAn expert writes the demonstration
  4. ReviewIndependently checked against your rubric
  5. RejectedWork that doesn't hold up goes back
  6. DeliveryIn the schema your pipeline expects

What we assess

Data types, generated to your specification.

Every dataset is produced by contributors qualified in the domain, then verified against your rubric and gold standards.

SFT datasets

Expert-written demonstrations that model the behaviour you want to teach.

Preference pairs

RLHF and RLAIF comparisons with the rationale behind each judgment attached.

Critique and rewrite

Structured critique of model output, paired with a corrected version.

Reasoning traces

Step-by-step solutions that expose the working, not just the answer.

Domain answer generation

Subject-matter responses in fields where correctness requires real expertise.

Output verification

Expert verification of model-generated candidates before they enter training.

How it runs

Built to your rubric, not a generic guideline.

We work from your specification and your gold standards, and we measure contributors against them continuously rather than once at onboarding.

  1. Specification

    We work from your rubric, schema, and gold standards — not a generic annotation guideline.

  2. Qualified contributors

    Domain experts are assessed against project-specific standards before any production work.

  3. Multi-stage review

    Independent review against your rubric and gold standards at every step.

  4. Format delivery

    Delivered in the schema and structure your training pipeline expects.

Who does the work

Written by people who know the subject.

Post-training data is only as good as the person who wrote it. We staff each dataset with contributors qualified in the relevant domain and measure them continuously against your standard.

  • Software engineering
  • Mathematics and formal reasoning
  • Computer science and systems
  • Finance and accounting
  • Law and policy
  • Medicine and science
  • Languages and localisation

Build the dataset your model is missing.

Tell us the capability you're trying to teach and the schema you need. We'll design the contributor pool and quality gates around it.