Updates and perspectives from our team

With super.AI you can use our in-house labelers or you can choose to label data yourself or with team members you invite. The option you choose depends on your project’s requirements.

We take a look at the main quality assurance techniques our platform uses to understand better how the complex process of labeling data can come with a quality guarantee.

Super.AI enables a four-step data lifecyle that allows for continuous improvement to your project.

The AI compiler is the super.AI execution system. It ensures that all inputs are split into smaller tasks that are efficiently and accurately labeled before being recombined into a coherent output.

Data programming is a powerful and flexible tool that makes it easy to produce high quality labeled data for your specific use case with minimum effort.

Automation is the most valuable time-saving device. Every automated manual process is time made available to pursue creative and product-defining ideas. At super.AI, we specialize in breaking down complexity and making difficult tasks seem simple. Read about how we've been helping several software companies reduce complexity in their business with the help of our solution.

The POC (Proof of Concept) is an important and widespread way of qualifying technology providers. In this article we outline how Sun Tzu, a military strategist, would approach this modern day technique.

What guarantees the quality of output when labeling data? The answer is ground truth data. Ground truth is a pair of input and output data used as a proxy for real truth. The closer the ground truth to the real truth, the higher the upper bound quality of the generated output. Read on for what it is

Last week we attended AI in the City, an event for AI insurance thought leaders and executives. I wanted to share with you a quick recap of the event and some of the thoughts that I took away at the end of the day.

The world of machine learning (ML) is complicated. If you’re thinking about integrating ML into your project, there are hundreds of questions you will quickly find yourself asking. How to store, version, and process your data? What development framework and hyperparameter optimization and resource management solutions should you use during the development and training of your model?