Quality assurance · Production
Labelbox AI data platform: RL training data, custom evals, robotics data, and expert annotation network
The problem
(not stated)
Workflow diagram · grounded in source
1
RL post-training data delivery
output
“From reward signals to preference pairs, we deliver the data your models need to learn.”
2
Custom model evaluation
validation
“Private AGI benchmarks
Custom assessments for frontier capabilities before public releaseArena evals
Head-to-head model comparisons with human preference judgments”
3
Expert human annotation
human_review
“Alignerr expert network
Elite human intelligence, on demand.”
Reported outcome
(not stated)
Reported metrics
leading US AI labs partneredover 80%
knowledge workers in Alignerr network1.5M+
Countries represented in network40+
Domains covered in network200+
Show all 7 reported metrics
leading US AI labs partneredover 80%
knowledge workers in Alignerr network1.5M+
countries represented in network40+
domains covered in network200+
PhDs in network50K+
Master's degrees in network200K+
licensed professionals in network85K+
Reported stack
Alignerr
Frequently asked questions
What did this team achieve with this AI workflow?
(not stated)
What tools did this team use?
Alignerr.
What results were reported?
leading US AI labs partnered: over 80%; knowledge workers in Alignerr network: 1.5M+; Countries represented in network: 40+; Domains covered in network: 200+ (source-reported, not independently verified).
How is this quality assurance AI workflow structured?
RL post-training data delivery → Custom model evaluation → Expert human annotation.