QuickCode.ai, a Fulton, Md.-based platform that enables organizations to unlock the insights trapped in text data to solve the data-labeling bottleneck that is inherent when deploying AI applications at scale, raised $2m in seed funding.
DataTribe made the investment.
The company intends to use the funds to expand operations and its development efforts.
Led by Shannon Hynds, CEO, QuickCode.ai provides a software platform that helps solve this problem of getting the right kind of labeled training data, resulting in more accurate and less biased machine learning models. The solution uses its machine learning method to target and create datasets for text-based machine learning algorithms, focusing users on finding the most representative data, including the hard-to-label and edge cases. Powered by technology developed and patented by Harvard University, the platform makes it efficient for experts to precisely identify the right training data, thereby improving label quality while reducing labeling time and ultimately resulting in more accurate models.