Automatically find & resolve errors in your labels & data

Our label quality evaluation tool helps you to automatically find classification and geometric errors in your training data, ensuring that your labels are of the highest possible quality before they go into production.

Manual computer vision quality control

The problem with existing methods of quality control

Human supervision can be highly unreliable, biased, and redundant. It also adds severe constraints on throughput where high expertise is necessary.


Current rates of data growth means that we will rapidly run out people to review data.


    Human perspectives differ, which can create disagreements on what is ground truth.


      Humans may be reviewing data that might not be value-add to your model.

        Automated computer vision quality control

        Augment quality control with automation

        Encord's novel quality assessment tool helps scale your quality control processes by spotting hidden errors in your training dataset.


        Let algorithms do the grunt work - deploy humans only when necessary


        Easy to use interface allows you to assess multiple label types.


        Finally a tool that helps you discover and visualise errant labels.
        Quality report workflow

        How it works

        A new approach to quality

        Encord has developed the first truly automated quality assessment tool - powered by micro-models. Instantly discover errors in your labels and make the most of human review.

        Features 01

        Model validation

        Import model predictions through our APIs and SDK to find errors and biases in your model.

        Features 02

        Predict label quality

        Use our automated quality control features to ensure only the best ground truth is delivered to your models.

        Features 01

        Expert review

        Encord supports purpose-built workflows for domain experts to drive specialised quality control.

        Get started today