Since graduating from 500 Startups, the MoBagel team recruited top talents from Stanford, UC Berkeley, Oxford University and National Taiwan University to further the technical development and business operations.

Through Softbank’s Innovation Program and Nokia’s Open Innovation Challenge, MoBagel worked on multiple data science projects with different enterprises to enable prediction across industries such as IoT, retail, manufacturing, and banking.

MoBagel learned from its own experience building predictive solutions for enterprises that a lot of the data science work could be automated and that many companies have trouble building their data science solutions due to high costs associated with it.

To solve this problem, MoBagel since developed Decanter AI, a fully automated machine learning (AutoML) engine, to build predictive solutions that directly integrate with clients’ systems to convert data into actionable insights that promote business growth.

MoBagel’s Decanter is made for businesses that want to utilize data science methods to make better data-driven decisions and extract deeper insights from their valuable data. This usually requires hiring a full-fledged data science team, but MoBagel significantly lowers the barriers by making its data science tool simple enough for anyone to use.

Decanter allows anyone to automatically build predictive models from raw data, without requiring a background in data science or programming. By automating the data science workflow with Automated Data Cleaning, Automated Feature Engineering, and Automated Data Modeling, Decanter can build accurate models at a fraction of the time and cost of other existing solutions.