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Appen is a tool that I use in my day to day to get my data labels. That's a very critical part of the process and you guys make it very easy.

Cullen Billhartz

Machine Learning and Computer Vision Technologist, The Boeing Company

With Appen, we’re getting trust and reliability. We can have confidence that the things we’re building are functionally right, that they do what we say they do while meeting the metrics our customers care about.

Colin Sullivan

Data Science Manager, Brandwatch

Appen is so fast. Using their platform, we could do overnight what used to take us a month. Appen is wonderfully efficient.

Rick Britt

Vice President of AI, CallMiner

The fact that Appen has it all right there, with a project as subjective as emotion, I can be really confident in what we’re getting back and have those numbers to back up the work that we’re doing.

Micaela Kaplan

Data Scientist and Ethics in AI Lead, CallMiner

We were thrilled to achieve our desired level of quality at the scale that we needed on the Appen platform to train our new citation intent classification model.

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Sr. Manager – Business Operations, Allen Institute for AI

Appen’s platform is really easy to use. What makes it great is you can reach so many different channels because of its global outreach.

Kenneth Benoit

Director of the Data Science Institute, LSE

We had to buy critical data from Appen, and that data has been incorporated into our background lexicon. This helps us build out new vocabularies for our clients.

Ines Wendler

Product Manager, MediaInterface

What’s really key is the guaranteed throughput more than anything, because that means I can give customers a definitive deadline, which I could never do before we started working with Appen. And I can get back to them with quality and quantitative numbers.

Jon Morra

Chief Data Scientist

Working with Appen has made our model development process 10 times faster, allowing us to get to the next step much quicker and think about audio and video at scale.

Lane Schechter

Product Manager at GumGum

We knew we were making progress when we could rapidly rebalance our training data and integrate feedback from our ML team into the process. The iterative cycle sped up dramatically, and we could move our models from idea to production in record time.

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co-founder of Shotzr

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