01
Context-aware sourcing
Our Automated Talent Engine matched US-based subject-matter experts to each prompt domain — graduate-level math, code, and law — so reviewers actually understood what they were grading.
Speed and quality for model teams
Replacing a billion-dollar vendor on RLHF and evals
A leading lab switched from a large multibillion-dollar vendor to RDL and unlocked faster turnaround and higher agreement scores across their preference and evaluation pipelines.
The outcome
8x
faster turnaround vs. prior vendor
The challenge
The team was shipping model updates weekly but their incumbent vendor needed two weeks to staff, calibrate and return a single preference batch. Inter-annotator agreement drifted below 80% on reasoning-heavy prompts, and every regression required a costly re-label. A model is only as strong as the data behind it — and that data had become the critical constraint.
Our approach
02
Calibrated rubrics
We co-authored graded rubrics with the lab and ran continuous gold-set checks, lifting agreement before a single production batch shipped.
03
Automation-scaled QA
Platform automation routed disagreements to senior reviewers and flagged low-confidence items in real time, keeping quality high as volume scaled 10x.
Results
8x
faster batch turnaround
98%
inter-annotator agreement
40%
lower cost per accepted label
“We switched from a large multibillion-dollar vendor and found RDL to outperform them on speed, quality, and most importantly, collaborative partnership.”VP of Research / Frontier foundation-model lab
Built around your model
Your model deserves better data.
Tell us what you’re training and we’ll show you what high-fidelity human data can do for it.