Labelbox accelerated the launch of text to image models for a frontier AI lab
An AGI research company preparing to launch an AI product needed rapid content moderation at scale while avoiding lock-in to a single labeling vendor, which had historically caused cost overruns, delays, and inability to flexibly scale. Their existing internal labeling tool was slow and resource-intensive to develop and struggled to keep up with expanding use cases.
The company's previous internal labeling tool was slow and engineering-intensive to build and maintain, and prior experiences with external vendors involved significant delays when starting labeling projects.
Using Labelbox, the company managed over 10 labeling vendors simultaneously and generated hundreds of thousands of annotations in three months, enabling a hugely successful AI product launch with widespread industry excitement and user adoption.
Frequently asked questions
What did this team achieve with this AI workflow?
Using Labelbox, the company managed over 10 labeling vendors simultaneously and generated hundreds of thousands of annotations in three months, enabling a hugely successful AI product launch with widespread industry e…
What tools did this team use?
Labelbox, webhooks.
What results were reported?
Labeling vendors managed: over 10; Annotations generated in three months: hundreds of thousands of annotated assets; Project configuration time: matter of minutes; Product launch outcome: hugely successful product launch (source-reported, not independently verified).
What failed first in this deployment?
The company's previous internal labeling tool was slow and engineering-intensive to build and maintain, and prior experiences with external vendors involved significant delays when starting labeling projects.
How is this quality assurance AI workflow structured?
Product launch triggers labeling need → Multi-vendor labeling platform setup → Project configuration and data import → Human safety content annotation → Real-time flagging and model refresh.