Data entry ops · Production

Fortune 500 creative software company achieves 50% reduction in labeling operations time and 5X AI deployment speed with Labelbox

The problem

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.

Workflow diagram · grounded in source
1
Unstructured data filtering
ai_action
“Labelbox Catalog provided the ability to quickly leverage metadata and custom embeddings for filtering all of this unstructured data, saving the team hours of time every day for a process that would have taken months of custom engineerin…”
2
Consistent annotation process
human_review
“leverage Labelbox Annotate to develop a consistent process for curating high-quality AI data”
3
Model quality assurance
validation
“Using built-in quality assurance tools, the team quickly saw increases in model performance, specifically for their NLP and computer vision projects which focused on understanding the complex structure of PDF documents”
4
Cross-team labeling collaboration
human_review
“multiple data science teams and product teams are now able to more effectively work with internal stakeholders to define what data they are interested in, and to translate requests into specific labeling instructions”
5
PDF processing at scale
integration
“the company can also now process tens of thousands of PDFs using a dynamic queueing system, prioritizing labeled data to enhance generative AI outputs”
6
AI products released to production
output
“Their AI Assistant products, which provide a comprehensive understanding of PDF structure and content, have been released to production in 2023, enhancing quality and reliability”
Reported 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.

Reported metrics
Labeling operations time50%
AI product deployment speed5X
Time to achieve results8 months
Daily time saved on data filteringhours of time every day
Show all 6 reported metrics
labeling operations time50%
AI product deployment speed5X
time to achieve results8 months
daily time saved on data filteringhours of time every day
efficiency and speed improvementssignificant improvements in both efficiency and speed
PDFs processedtens of thousands of PDFs
Reported stack
LabelboxLabelbox CatalogLabelbox AnnotateLabelbox Boost
Source
https://labelbox.com/customers/creative-software-customer-story
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

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…

What tools did this team use?

Labelbox, Labelbox Catalog, Labelbox Annotate, Labelbox Boost.

What results were reported?

Labeling operations time: 50%; AI product deployment speed: 5X; Time to achieve results: 8 months; Daily time saved on data filtering: hours of time every day (source-reported, not independently verified).

How is this data entry ops AI workflow structured?

Unstructured data filtering → Consistent annotation process → Model quality assurance → Cross-team labeling collaboration → PDF processing at scale → AI products released to production.

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