Achieve peak model performance with the best data. Create & manage training data for any visual modality, debug models and boost performance, and make foundation models your own.
Trusted by the world's best AI teams
Get actionable insights from your models in production and iteratively create training data, all in one place.
Efficiently label any computer vision modality and manage large scale annotation teams.
Learn more about
IMAGE
VIDEO
DICOM
SAR
MODEL-ASSISTED LABELING
ECG
Expert review, QA and QC workflows help you deliver higher quality datasets to your artificial intelligence teams, helping improve model performance.
Learn more about
AUTOMATED DATA QUALITY ASSESSMENT
Connect your data and models with Encord's Python SDK and API access to create automated pipelines for continuously training ML models.
Learn more about
MICRO-MODELS
Improve model accuracy by identifying errors and biases in your data, labels and models.
Learn more about
ENCORD ACTIVE
Get Computer Vision Models
Into Production Faster
Forget fragmented workflows, annotation tools, and Notebooks for building computer vision models. Encord is an active learning platform that accelerates every stage of taking a computer vision model to production.
We reduced our false acceptance rate to just 1% from 6% using Encord’s platform since we could train our new models on much better datasets with much higher quality labels.
Jeffrey Siaw
We reduced the labeling time for CT series by 50 percent and by 25 percent for MRI series with Encord.
Dr. Hamza Guzel
We really value Encord's SDK and having the flexibility to set up new projects with unique labelling tools that don’t just limit us to bounding boxes.
Esben Thorius
Encord is transforming how businesses are getting their computer vision models into production. We can do the same for you. Talk to us to find out how.
.svg?ixlib=gatsbyFP&auto=compress%2Cformat&fit=max)
.svg?ixlib=gatsbyFP&auto=compress%2Cformat&fit=max&w=152&h=150)
.svg?ixlib=gatsbyFP&auto=compress%2Cformat&fit=max&w=150&h=150)
.png?ixlib=gatsbyFP&auto=compress%2Cformat&fit=max)
