AI is about quality data. Oversee quality level to ensure low-error datasets. Fix identified quality, bias or imbalance issues in your dataset. Simplify advanced collaboration workflows. Leverage programmatic QA and active learning to master quality while scaling the volumes.
They trust us on their data-centric journey
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.
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.
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.
A 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.Learn more
"Kili's customer support is best in-class. We solve issues much faster and it has a direct impact on our performance."
"Great companies like Kili Technology, [...] have already adopted this data-centric AI approach."
"Kili is bringing added value in the management of our projects and this is quality."
"Kili enables us to improve our models’ performance and scale our AI projects as fast as our business needs."
"We are very satisfied with our collaboration with Kili. We saw a performance improvement of our model of 3.5%"
Kili at work
Discover how Kili is helping companies in different sectors build responsible, effective AI on a foundation of good data.