Pairwise output ranking
Compare model responses side by side to identify which output better matches user intent, task requirements, and quality standards.
RLHF services
Human preferences that move models toward better behavior.
Collect structured rankings, scores and multilingual feedback for reward modeling, reinforcement learning and task-specific alignment.
What we collect
Human preference signals for model alignment.
Feedback workflows are designed around your model, task, user expectations, languages and target behavior.
Scaled preference scoring
Rate AI outputs using structured scoring scales to capture differences in helpfulness, accuracy, coherence, safety, and overall response quality.
Open-ended feedback collection
Collect detailed human feedback on model outputs to identify issues in tone, fluency, factuality, safety, and instruction following.
Task-specific alignment evaluation
Evaluate model behavior against custom criteria designed for each use case, such as response safety, refusal quality, or domain accuracy.
Multilingual feedback workflows
Gather human feedback across languages, regions, and cultural contexts to improve model alignment for global users.
Reward model training data output
Prepare structured preference and feedback datasets that can support reward modeling, reinforcement learning, and model alignment workflows.
Built for alignment
Feedback workflows matched to the behavior you value.
We turn task definitions into clear comparison rubrics and calibrated human judgments across languages and user contexts.
01Reward model data
02Assistant helpfulness
03Safety & refusal quality
04Multilingual alignment
How we deliver
From preference rubric to training-ready feedback.
A calibrated workflow keeps rankings consistent, disagreements measurable and output structured for downstream use.
Define preferences
Align tasks, response qualities, scoring scales, policies and thresholds.
Train & calibrate
Qualify raters through examples, comparisons and feedback.
Rank & review
Collect judgments with consensus, audits and disagreement analysis.
Structure & deliver
Provide preference datasets with quality findings and documentation.
Build human preference data for better model alignment.
Share your model outputs, target behavior and evaluation criteria. We’ll propose a focused RLHF pilot.
Discuss an RLHF pilot ↗