The quality and expertise to match any computer vision use case.
Our computer vision CloudWorkers are skilled at a wide array of use cases from bounding boxes and semantic segmentation to 3D point cloud and sensor fusion systems. We apply best practices developed from annotating millions of images and videos to deliver best in class computer vision projects for AI leaders around the world.
Our CloudWorkers have worked on all the leading tool platforms giving you the flexibility to choose from best of breed tools - or to bring your own.
Easily communicate new uses cases and quickly change requirements through open collaboration and feedback loops with our responsive team leads.
Vetted for skill and character, our CloudWorkers take pride in their work, which adds to their motivation to get it right the first time.
The space that I’m in is going through an evolution. If you don’t have AI capabilities, you’ll be left behind. But I can’t focus on the product if I’m swamped doing people management. CloudFactory takes that burden off of us.
Company Founder, Medical AI Company
The key to unlocking new products and features.
Our natural language processing CloudWorkers understand the nuance of your business and pick up on the subtleties in language required to accurately tag the text you need to train your natural language based applications. Whether you’re training chat bots, virtual assistants, or doing sentiment analysis we provide the high quality data that you need to make your models more accurate.
Our CloudWorkers combine business context with their native understanding of language, syntax, and structure to accurately parse and tag text according to your specifications.
Our team leads are easy to work with and responsive, keeping you in sync with your team for rapid iteration.
Our proven methodology and efficient processes ensure your NLP project ramps up and scales quickly.
We’ve been able to significantly accelerate our data science research. That’s sped up product development, especially early on where we cut out half the time it took to do some initial training of the data.