List of Labelbox Customers
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United States
Since 2010, our global team of researchers has been studying Labelbox customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased Labelbox for AI infrastructure from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using Labelbox for AI infrastructure include: John Deere, a United States based Manufacturing organisation with 73100 employees and revenues of $44.67 billion, SAP, a Germany based Professional Services organisation with 26944 employees and revenues of $23.17 billion, Pathware, a United States based Life Sciences organisation with 15 employees and revenues of $1.0 million and many others.
Contact us if you need a completed and verified list of companies using Labelbox, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
The Labelbox customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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John Deere | Manufacturing | 73100 | $44.7B | United States | LabelBox | Labelbox | AI infrastructure | 2020 | n/a |
In 2020, John Deere implemented Labelbox to automate data curation and large scale labeling for computer vision model training that supported See & Spray precision agriculture products. The Labelbox deployment served as AI infrastructure for Blue River Technology within John Deere R&D in the United States, centralizing more than 1 billion assets of AI and ML training data.
The implementation leveraged Labelbox Catalog and labeling and workflow tools to standardize annotation pipelines, enforce dataset versioning, and orchestrate high throughput labeling operations. Configuration emphasized dataset indexing, metadata management, annotation quality review loops, and task routing to accelerate iterative model development for computer vision workloads.
Labelbox was integrated into model training pipelines for See & Spray so curated datasets could flow into computer vision development workflows, with operational coverage focused on R&D teams and precision agriculture engineering in the United States. The deployment consolidated image and video asset management, enabling controlled dataset curation and repeatable training dataset assemblies for downstream model training.
Governance centered on centralized cataloging, workflow controls, and annotation standards to manage labeling consistency and data stewardship across John Deere and Blue River Technology. The case study reports reduced iteration time and a centralized training data repository, reflecting outcomes tied to the Labelbox implementation.
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Pathware | Life Sciences | 15 | $1M | United States | LabelBox | Labelbox | AI infrastructure | 2022 | n/a |
In 2022, Pathware used Labelbox as its AI infrastructure provider, deploying Labelbox's annotation and training-data management SaaS to label thousands of pathology image tiles. The implementation emphasized pixel and object level annotation, producing bounding boxes and masks to create structured ground truth for supervised learning workflows in medical imaging and pathology.
Labelbox's ontology-driven labeling and external labeling workflows were implemented to enforce annotation semantics and support distributed annotators, feeding curated datasets into Pathware's Bioptic system training pipeline. Operational scope centered on pathology and clinical AI model development in the United States, and the labeled datasets enabled the Bioptic system to deliver near real time biopsy quality assessments with over 90% assessment certainty.
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SAP | Professional Services | 26944 | $23.2B | Germany | LabelBox | Labelbox | AI infrastructure | 2021 | n/a |
In 2021, SAP used Labelbox in an academic and industry research project to manage labeled data for a supervised machine learning classifier to detect feature requests in the SAP Community. The study involved researchers from Technical University of Munich, University of Innsbruck and SAP Deutschland, and focused on requirements engineering and developer community analysis in Germany; the labeled corpus contained 1,500 community questions used for training and evaluation.
Labelbox was employed as AI infrastructure to provide dataset management and the annotation interface required for supervised learning workflows. The implementation supported label schema definition, batch annotation, dataset versioning and human-in-the-loop quality review processes to maintain label consistency across iterations. These category-aligned capabilities were used to organize annotation rounds and produce a training dataset suitable for model development.
No external system integrations are specified in the source study, the operational coverage centered on SAP Deutschland and the collaborating academic teams working with SAP Community data. Governance relied on annotation guidelines and consensus review cycles to align labels with requirements engineering taxonomies, and the labeling output fed the classifier training pipeline. The reported outcome from the research was a trained classifier achieving approximately 0.819 accuracy on the feature request detection task.
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Buyer Intent: Companies Evaluating Labelbox
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