QuickCode.ai helps companies unlock the potential of their unstructured text data at machine scale.

Quickcode.ai publicly launched their SaaS product for unstructured text, designed to help users realize the power of their data and optimize training the best machine learning models. Because great machine learning requires great training data, QuickCode.ai helps data scientists save time and train more accurate models by improving the quality of unstructured text datasets before they are labeled.

QuickCode.ai is text processing software for data scientists who want to solve complex problems with machine learning, especially domain specific problems that require expertise for curating and labeling. Unlike random sampling, keyword sampling, and pre-trained classifiers, which sacrifice data quality for the sake of convenience, QuickCode.ai combs through every word of unstructured text, using machine learning and expert insight to quickly identify the most representative training data before it’s labeled. QuickCode.ai is able to process any language, any topic, and any size text, producing domain-specific data rather than traditional generic training data. In other words, QuickCode.ai unlocks the potential of your unstructured text data.

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“We set out to develop a solution that allows data scientists to solve their complex text-based problems more efficiently and accurately,” said Shannon Hynds, CEO of QuickCode.ai. “Bad training data limits the potential of machine learning, and traditional methods of hand labeling brings its own set of challenges. QuickCode.ai integrates expert knowledge into the workflow even earlier, helping companies save time, money, and headaches down the road.”

With the public release of QuickCode.ai, users can build representative training data from across their entire dataset, providing recommendations and empowering data science teams or subject matter experts (SMEs) to validate those recommendations in minutes. QuickCode.ai gives companies complete confidence in their training data, allowing them to focus on building the best possible machine learning models.

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