The preference data
that actually moves the needle.
Pairwise and N-wise human-preference ranking with calibrated raters, per-rubric scoring and reward-hacking audits — the input layer your reward model needs.
Built for production, not just demos.
- Pairwise and N-wise preference ranking (up to N=8)
- Multi-rubric scoring (helpful, accurate, safe, concise, style)
- Domain-expert raters (medical, legal, financial, technical)
- Multilingual preferences across 60+ languages
- Adversarial probe-set generation for reward debugging
- Reward-hacking audits and feature ablation
- Side-by-side + cross-model comparisons
- Calibration against your house style guide
How a typical engagement runs.
Rubric
Define what 'better' means across helpful, accurate, safe, style — with examples and counter-examples.
Calibrate
Raters take 100 gold tasks; we tune the rubric and remove ambiguous edges.
Pilot
1k preference pairs with per-rater IAA + rubric-level confusion matrix.
Production
Scale to weekly batches with live IAA + reward-model drift dashboards.
Probe + Debug
Adversarial probe sets surface reward-hacking risk before deployment.
What you get in your bucket.
Questions, answered.
Pair-wise or N-wise — which should I use?
Pair-wise is simpler and fits TRL/DPO. N-wise (3-8 ranked) gives richer signal per record but needs more rater time. We typically start pair-wise and graduate to N-wise on hard slices.
How do you keep raters calibrated?
Continuous gold-task injection (every 20 records), per-rater accuracy dashboards, and weekly calibration syncs with our lead reviewers.
What about long-form generations?
We support multi-paragraph and code preferences. For very long context (40k+ tokens), we split into sections with rubric-tagged spans.
Can you generate the model completions for us?
Yes — we can run inference on your model or a baseline (open or hosted) to produce A/B candidates. Or you provide pre-generated pairs.
Ready to build
AI you can trust?
Talk to a solutions architect — get a pilot scoped in 48 hours.