How a Fortune 500 creative tools company shipped generative AI across its products using Labelbox
The company's R&D division spent excessive engineering cycles building custom training data infrastructure for generative AI products, resulting in project delays, siloed AI development across groups, and a fragmented data quality evaluation process that reduced model confidence and slowed ROI from AI/ML investments.
The team's homegrown training data infrastructure was siloed and fragmented with no central platform for AI teams to collaborate or evaluate data quality consistently, leading to lower model confidence.
The company achieved a 50% reduction in labeling operations time and a 5X increase in AI product deployment speed within 8 months, and released AI Assistant products providing comprehensive PDF understanding to production in 2023.
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Frequently asked questions
What did this team achieve with this AI workflow?
The company achieved a 50% reduction in labeling operations time and a 5X increase in AI product deployment speed within 8 months, and released AI Assistant products providing comprehensive PDF understanding to produc…
What tools did this team use?
Labelbox Catalog, Labelbox Annotate, Labelbox Boost.
What results were reported?
Labeling operations time: 50%; AI product deployment speed: 5X; Time to achieve results: within just 8 months; Daily time savings on data filtering: hours of time every day (source-reported, not independently verified).
What failed first in this deployment?
The team's homegrown training data infrastructure was siloed and fragmented with no central platform for AI teams to collaborate or evaluate data quality consistently, leading to lower model confidence.
How is this quality assurance AI workflow structured?
Training data need identified → Catalog filters unstructured data → Annotate curates training data → QA tools validate model quality → Teams define labeling instructions → Dynamic queue processes PDFs → AI Assistant products deployed.