Safety signal that cleared a frontier text-to-image launch
Problem
The lab was launching a highly anticipated AI product and needed rapid content moderation signal. It didn't want to lock into a single labeling vendor — too slow to switch, too much risk concentration — and any platform had to pass strict security and compliance review.
Solution
Labelbox let the lab produce signal through multiple contributor networks at once, mitigating single-vendor risk, with enterprise-grade security.
Result
The lab ensured a successful product launch, reviewing and generating hundreds of thousands of annotated assets in just three months for its content moderation use case (tagging safe vs. unsafe content).
A frontier AI lab had to vet what its text-to-image product generated before launch. Labelbox produced hundreds of thousands of safe/unsafe judgments in three months, without single-vendor risk.
The challenge
A frontier artificial general intelligence (AGI) lab was testing and validating a large number of research projects, with heavy data needs. Its internal labeling tool took too much engineering to keep up with expanding use cases. As it prepared to launch a large-scale AI product, the lab needed to rapidly vet the content its application generated — and couldn't risk depending on a single labeling vendor, which meant cost overruns, delays, and no flexibility to scale or switch. Security and compliance were non-negotiable for a launch of this profile.
The approach
The lab used Labelbox to produce the signal and keep research velocity. The platform let it test and contract multiple contributor networks at once — managing over 10, from introduction to kickoff — so no single point of failure threatened the launch. Labelbox passed strict security and compliance review with enterprise-grade infrastructure. Setup took minimal engineering: the lab configured image and text projects in minutes, used webhooks and attachments to import data and give contributors context, and evolved ontologies and workflows as model performance and project direction changed.
The outcome
For its content moderation use case — tagging safe vs. unsafe content — the lab reviewed and generated hundreds of thousands of annotations in three months. It built a real-time pipeline to flag and report images and refresh its models, accelerating the removal of violent, toxic, and lewd content. The signal cleared the way for a successful launch that drove widespread industry excitement and adoption.
Where this goes
Safety is an eval problem. Grounding a generative model in human judgment about what's acceptable is what lets a frontier lab ship to the public with confidence.