Appen

About

When your goal is to launch world-class AI, our reliable training data gives you the confidence to deploy

Appen collects and labels images, text, speech, audio, video, and other data used to build and continuously improve the world’s most innovative artificial intelligence systems. Our expertise includes having a global crowd of over one million skilled contractors who speak over 180 languages and dialects, in over 70,000 locations and 130 countries, and the industry’s most advanced AI-assisted data annotation platform. Our reliable training data gives leaders in technology, automotive, financial services, retail, healthcare, and governments the confidence to deploy world-class AI products. Founded in 1996, Appen has customers and offices globally.

Confidently Deploy Machine Learning Products With Our Platform

The most advanced AI-assisted data annotation platform
The Appen platform combines human intelligence from over one million people all over the world with cutting-edge models to create the highest-quality training data for your ML projects. Upload your data to our platform and we provide the annotations, judgments, and labels you need to create accurate ground truth for your models.

Company Details

https://appen.com/
Year Founded 1996
Company Size 501-1,000 employees
Specialties Technology & Internet, Machine Learning, Deep Learning, Data Science, Data Resources & Management , Product Development

How Appen uses AI in their company

High-quality data annotation is key for training any AI/ML model successfully. After all, this is how your model learns what judgments it should be making. Our platform combines human intelligence at scale with cutting-edge models to annotate all sorts of raw data, from text, to video, to images, to audio, to create the accurate ground truth needed for your models.

A large majority of our annotation tools have machine learning assistance (MLA) built in to enhance the speed and accuracy of our annotators. Machine learning assistance combines machine predictions with human annotations so that instead of having to create a judgement from scratch, a machine learning model will suggest a judgment, and the human contributor can simply review and edit the suggestion saving time and effort.

With human contributors reviewing machine predictions instead of starting a judgement from scratch, the data annotation time drastically reduces. This enables fast, scalable model deployment along with the peace of mind in knowing that our teams are there to confirm and correct your model predictions if needed.