RLHF and evaluation data that reflects real judgment
Preference ranking, prompt-response evaluation, and NLP labeling from an English-fluent managed workforce — calibrated to your rubrics, not generic clickwork.
Model quality fails when raters guess the rubric
LLM products need humans who can apply nuanced preferences: helpfulness, honesty, safety, style, and instruction following — consistently, at volume.
- Noisy pairwise rankings that disagree across shifts
- Shallow critiques that do not teach the model
- Policy edge cases labeled inconsistently
- Expensive Western rater pools that cannot scale experiments
Preference discipline
Pairwise and ranked choices with calibration rounds before production scale.
Evaluation coverage
Prompt-response scoring, safety judgments, and instruction-following checks.
English fluency advantage
Workforce educated in English-medium systems — better for nuanced reading and critique.
Human signal for language models
From classic NLP labels to modern RLHF loops — delivered with training, consensus checks, and reporting your research or product team can trust.
RLHF & preference ranking
Pairwise comparisons, multi-way ranking, and rubric-based absolute scores for reward-model or DPO-style pipelines.
Prompt-response evaluation
Rate answers for accuracy, helpfulness, tone, citation quality, and instruction adherence.
Safety & refusal review
Policy judgments, red-team assist, and consistent handling of boundary cases under your safety guidelines.
Critique & rewrite assist
Structured critiques and improved responses where your workflow needs richer supervised signal.
Classic NLP labels
Classification, NER, sentiment, relevance, and document structure for search and traditional NLP systems.
African language pathways
Transcription, translation, and evaluation support for underrepresented languages where coverage exists.
Rater calibration before throughput
We treat agreement and rubric literacy as first-class metrics — then scale.
Rubric bootcamps
Gold examples, edge cases, and written decision rules before raters touch production volume.
Agreement monitoring
Overlap samples and disagreement review so preference noise stays visible.
Your stack or ours
BYOP into Label Studio, Prodigy, proprietary eval UIs, or export-friendly workflows.
Cost-efficient iteration
Run more eval and preference experiments without Western rater sticker shock.
From rubric to ranked outputs
Share rubric
Policy, scoring dimensions, and example judgments
Calibrate raters
Gold set agreement before production opens
Pilot rankings
Deliver prefs/evals with disagreement notes
Scale loops
Expand volume for RLHF, eval, or NLP label pipelines
Questions from LLM teams
Need cleaner preference and eval data?
Send your rubric and a small sample set. We will propose a calibrated ranking or evaluation pilot.
Request a Demo