The fastest way to get high quality annotation data for machine learning

Encord's flexible APIs & SDK will allow you to stream annotations directly to your data loaders with a few lines of code.

How it works

Unlock data-centric AI for computer vision

Encord offers the most sophisticated automated data annotation & evaluation solution in the market.


The most sophisticated labeling automation features in the market.

  • Build ensembles of micro-models
  • Utilise automation feature suite
  • Incorporate problem-specific heuristics


Automated data quality assessments discover errors in your ground truth.

  • Version your label sets
  • Validate model performance
  • Discover classification & geometric errors


Seamlessly integrate our APIs & SDK with your model data loaders.

  • Import model predictions
  • Facilitate active learning pipelines
  • Prototype new models and ontologies

Manage, version, experiment

We built Encord to support you as your company adopt and scales computer vision AI applications.

Flexible tools

Our software support a wide variety of computer vision modalities. Multi-faceted data loaders and parsers fits your use case.

Automated labeling

Use our object tracking & interpolation features to reduce costs. Use micro-models to accelerate your active learning workflows and get to production faster.

Configurable taxonomy

Create arbitrarily rich nested labeling structures accommodating all label modalities in one place.


Iterate and experiment with label structures by branching out projects and adding custom filters.


Programmatically access, monitoring, and deployment of labels and data assets into your machine learning infrastructure.


Access automated assessment and visualisation tools to get precise estimations of your label quality and model performance.
IOU Grid

Debug label quality

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

Branch project

Branch projects

Our labelset management capabilities allow you to experiment fluidly with versioned data and labels, and set up custom pipelines and filters.

An Active Learning Pipeline for MLOps