Data curation, AI-assisted labeling, model training & diagnostics, and labeling services, all in one platform, to build better AI products, remarkably fast.
Trusted by companies of all sizes — from startups to Fortune 500s
Leverage an end-to-end system that features the full set of capabilities needed to improve your model’s performance.
Quickly search, explore and manage your data in one place. Accelerate AI development with raw data, metadata, and ground truth labels at your fingertips.
Access a full suite of labeling, collaboration, and quality tools that give you complete visibility and control over data labeling operations with in-house labeling teams and labeling service vendors. Leverage automation and custom workflows to make progress as quickly as possible.
Improve your model with better data. Model is the command center for data-centric iterations, including model error analysis, mining for edge cases, finding and fixing label quality issues, and more.
Access world-class data labeling services on-demand. Get started immediately with a labeling workforce designed for your needs. Quickly scale up or down as your AI initiatives evolve. Use Labelbox Boost for everything from the smallest to the largest labeling projects.
How Blue River Technology used model-assisted labeling to reduce labeling costs by 50%
High labeling costs associated with the costs of crops vs. weeds on full image segmentation images.
Labelbox’s model-assisted labeling and collaborative annotation suite.
The company is now able to standardize how they create and manage data all in a single location and using Labelbox's automation suite, they’ve seen a reduction in labeling times by 50%, worth millions of dollars in cost savings per year.
How Burberry harnesses Labelbox and Databricks to curate their strategic marketing assets
Retail and ecommerce
How Ancestry prioritizes collaboration and training data quality to enable genealogical breakthroughs with ML
Technology and software
Improve model performance through fast and impactful data-centric iterations.
Find the data that will boost model performance using active learning and model error analysis. Save time and money by focusing resources where your specific model needs the most help.Learn more
Achieve up to 80% in labeling efficiency gains with model-assisted labeling – use models to pre-label data, and let humans focus on corrective actions to generate ground truth so they don’t need to start from scratch.Learn more