Find & fix issues in your ML datasets

Supervise quality level & improvements to ensure low-error datasets. Simplify advanced collaboration workflows. Leverage programmatic QA. Explore your datasets and identify the data that matters.

They trust us on their data-centric journey

[1]
Focus review on data that matters
Create a communication flow between annotators and reviewers. Iterate quickly with annotators on labels to modify. Provide continuous feedback to your labeling team to avoid drift in quality.
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[2]
Quantify quality with insights from advanced quality metrics
Look at the consensus by class to know when your ontology needs to be reshuffled. Look at labelers’ disagreements to identify misunderstandings among your annotator population. Filter on data slices with low quality metrics. Compare quality between labelers or against an industry standard.
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[3]
Increase data quality with programmatic error spotting
Programmatically spot errors by building automated QA scripts in the labeling interface. Use error detection models to automatically find and fix issues in your ML datasets.
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[4]
Orchestrate all your quality strategies with automated workflows
Fully automate & build custom workflows to scale your labeling operations.
Request a demoA qualified workforce for all of your labeling needs
Data labeling takes time and resources that some organizations simply don’t have. That’s why Kili offers annotation services on premise or offshore, for adhoc missions or end-to-end projects. We’ve taken the time to source the very best so you can focus on the rest.
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