data_entry_ops · saas · workflow
Fortune 500 creative software company achieves 50% reduction in labeling operations time and 5X AI deployment speed with Labelbox
The company's R&D division spent significant engineering cycles building its own training data infrastructure, causing project delays and siloed AI development across teams that struggled to discover available data for ML use. Data quality evaluation was also highly fragmented, leading to lower model confidence and delayed ROI on AI/ML initiatives.
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 · Unstructured data filtering
Labelbox Catalog leverages metadata and custom embeddings to filter unstructured data across text, PDF, images, and videos.
Tools used
LabelboxLabelbox CatalogLabelbox AnnotateLabelbox Boost
Outcome
Using Labelbox's full product suite, the company achieved a 50% reduction in labeling operations time and a 5X increase in AI product deployment speed within 8 months, with AI Assistant products released to production in 2023 and the ability to process tens of thousands of PDFs using a dynamic queueing system.
Results
Time saved50%
Volume5X
Running since2023
Grounding & classification
Source type: vendor customer story
30 fields verified against source quotes, 1 dropped as unverifiable.
computer visiondata extractiondocument aiknowledge basehuman review describedmetric backedproduction runtime claimedtools describedvendor confirmedworkflow describedsoftwarecycle time reductionemployee productivitythroughput increasetime savedvendor customer storydata entry opsquality assurancedocument to recordhuman review queue