Data entry ops · Production
Labelbox delivers high-throughput data labeling with curated expert teams and foundation-model pre-labeling
The problem
Data labeling at scale is slow and expensive, and sourcing labelers with the right domain expertise or language proficiency across diverse industries is difficult.
Workflow diagram · grounded in source
1
On-demand expert access
trigger
“A world-class network of highly-skilled subject matter experts are available from around the world to increase the throughput of your project”
2
Expert team matching
routing
“we carefully curate labeling teams and match your project with labelers who are already well-versed. Expertise includes advanced topics like agriculture, medical, law, physics, and more, as well as experts proficient in 20+ languages”
3
Foundation model pre-labeling
ai_action
“Automation techniques including bulk classification and model-based pre-labeling powered by foundation models decrease labeling costs and time”
4
Human review of edge cases
human_review
“teams have more time to focus on critical edge cases or areas where the model might not be performing as well”
Reported outcome
Labelbox claims to set new standards in quality and throughput at half the cost, with foundation-model pre-labeling decreasing labeling costs and time so teams can complete larger scale projects.
Reported metrics
Labeling costhalf the cost
Labeling costs and timedecrease labeling costs and time
Reported stack
foundation models
Frequently asked questions
What did this team achieve with this AI workflow?
Labelbox claims to set new standards in quality and throughput at half the cost, with foundation-model pre-labeling decreasing labeling costs and time so teams can complete larger scale projects.
What tools did this team use?
foundation models.
What results were reported?
Labeling cost: half the cost; Labeling costs and time: decrease labeling costs and time (source-reported, not independently verified).
How is this data entry ops AI workflow structured?
On-demand expert access → Expert team matching → Foundation model pre-labeling → Human review of edge cases.