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.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Training data need identified
The company sought a single platform to consolidate and unify data labeling efforts for generative AI product development.
Tools used
Labelbox CatalogLabelbox AnnotateLabelbox Boost
Outcome
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.
What failed first
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.