RLHF services: human feedback your model can learn from
Preference rankings, rubric-based scoring, and safety review from trained human reviewers — calibrated on your guidelines, QA-sampled in production, and reported with inter-rater agreement, so the signal you train on is one you can trust.
Start with a pilot batch
Send us your task type, rough volume and guidelines if you have them. We calibrate on a shared gold set, run a pilot batch through the full pipeline — usually within a week — and report quality and inter-rater agreement before you commit to volume. Priced per item or per hour; your NDA, your tooling, your data never reused.
Who this is for
Teams whose models need human judgment, not just automated metrics.
AI labs fine-tuning models
Preference data and rubric grading at the quality bar RLHF and DPO pipelines need, with calibration before any volume.
Product teams aligning an AI feature
Your model works in the demo and misbehaves in the wild. We rank and score real outputs against your guidelines, so tuning targets the failures that actually matter.
Teams replacing ad-hoc labeling
Crowd or in-house labeling without calibration drifts. We bring a gold set, second-reviewer sampling, and agreement reporting, so quality is measured instead of assumed.
Safety and policy teams
Red-team style probing and policy review of model outputs, handled under NDA and inside your tooling.
What RLHF work includes
The full loop from your guidelines to training-ready data.
Preference ranking
pairwise and ranked comparisons of model responses
Rubric-based scoring
graded judgments against criteria you define
Calibration & gold sets
shared reference items that anchor every reviewer
Safety & policy review
red-team probing and guideline-violation checks
Technical RLHF
code and reasoning outputs judged by working engineers
Agreement reporting
inter-rater agreement tracked and reported every cycle
How an RLHF engagement runs
The same discipline as our evaluation work, aimed at training data.
Guidelines & calibration
We grade a shared calibration set against your rubric and resolve disagreements until reviewers converge on your standard.
Pilot batch
A bounded batch through the full pipeline — usually within a week — so you can inspect the data before committing to volume.
Production with QA sampling
Trained reviewers matched to your task type. A second reviewer samples completed work, and agreement is tracked throughout.
Reporting & rubric refinement
You get training-ready data in your format, plus notes on guideline gaps, edge cases, and recurring failure modes in the outputs.
Frequently asked questions
Straight answers to what founders and teams usually ask before starting.
How is RLHF different from model evaluation?
Evaluation measures how a model behaves; RLHF produces the human preference signal used to change that behavior in training. We do both — many clients start with evaluation to find the failure modes, then run RLHF to generate the data that fixes them.
What data formats do you deliver?
Yours. Ranked pairs, scored items, and annotations in the schema your pipeline expects — and we work inside your labeling tooling when required.
How do you keep raters consistent?
Calibration against a shared gold set before production, second-reviewer sampling during it, and inter-rater agreement tracked and reported to you. Reviewers who drift are recalibrated or rotated off the task.
Can you handle code and technical tasks?
Yes. Technical reviewers are working software engineers who run the code and check the reasoning — not raters guessing from the surface of an answer.
How do we start, and is there a minimum?
Send the task type, rough volume, and any guidelines you have. We propose a pilot batch — usually completed within a week — and quote per item or per hour depending on the task. There is no long-term commitment to start.
Tell us what you're building
Whether it's a product to ship or a model that needs human eyes on it, tell us where you are and we'll reply with next steps.
Spanish Fork, UT 84660-1319
United States