Reward-model quality is bounded by rater agreement. We run pods against a versioned rubric with weekly calibration, blind gold seeding and a documented escalation path for every ambiguity.
1.3×
Calibrated bench
Held against deployed demand
100%
KYC verified
Before task access is granted
What you receive
Training data with the agreement statistics attached, so you know what you are training on.
Preference data, critique writing and demonstration data from trained rater pods.
Rubric-anchored reward-model data produced by named pods under weekly calibration, with per-dimension agreement reported per batch.
Deliverables
Adversarial prompt design and evaluation-prompt curation.
Instruction datasets and red-team prompt sets built against an explicit coverage matrix, so gaps are visible rather than assumed.
Deliverables
Model-assisted generation with human seed curation and verification.
Generation pipelines with human-curated seeds, deduplication and a verification layer. Delivered with a quality-audit sample rather than a raw dump.
Deliverables
Expert text and code annotation, not commodity labelling.
Entity labelling, classification, reasoning-step tagging and error-taxonomy application performed by domain-matched specialists.
Deliverables