Quality assurance · Production
Labelbox delivers RL training data, custom evaluations, and robotics data for leading AI labs
The problem
Leading AI labs need high-quality, expert-annotated training data for post-training at scale, custom model evaluation benchmarks, and specialized robotics data to advance frontier AI capabilities.
Workflow diagram · grounded in source
1
Expert rubric development
trigger
“Expert-crafted scoring criteria for coding, science, finance, and more”
2
AI-powered data diversity
ai_action
“AI-powered diversity
Data engine that ensures and steers broad task and environment coverage”
3
Expert human annotation
human_review
“Alignerr expert network
Elite human intelligence, on demand.
1.5M+ knowledge workers
Professionals across 40+ countries and 200+ domains”
4
Custom model evaluations
validation
“Private AGI benchmarks
Custom assessments for frontier capabilities before public release
Arena evals
Head-to-head model comparisons with human preference judgments
Rubric-based multimodal
Structured scoring across text, vision, and reas…”
5
Training data delivery
output
“From reward signals to preference pairs, we deliver the data your models need to learn”
Reported outcome
Labelbox partners with over 80% of leading AI labs in the US, supported by a network of 1.5M+ knowledge workers including 50K+ PhDs across 40+ countries and 200+ domains.
Reported metrics
AI lab partnerships (US)over 80%
Knowledge workers in network1.5M+
Countries represented40+
Domains covered200+
Show all 7 reported metrics
AI lab partnerships (US)over 80%
knowledge workers in network1.5M+
countries represented40+
domains covered200+
PhDs in network50K+
Master's degrees in network200K+
licensed professionals in network85K+
Reported stack
AlignerrLabelbox Leaderboards
Frequently asked questions
What did this team achieve with this AI workflow?
Labelbox partners with over 80% of leading AI labs in the US, supported by a network of 1.5M+ knowledge workers including 50K+ PhDs across 40+ countries and 200+ domains.
What tools did this team use?
Alignerr, Labelbox Leaderboards.
What results were reported?
AI lab partnerships (US): over 80%; Knowledge workers in network: 1.5M+; Countries represented: 40+; Domains covered: 200+ (source-reported, not independently verified).
How is this quality assurance AI workflow structured?
Expert rubric development → AI-powered data diversity → Expert human annotation → Custom model evaluations → Training data delivery.