Blog | super.AI

Part-of-speech (POS) tagging is an essential component of teaching machines to understand human language. This article explains how AI-powered POS tagging works and what it can do for your business.

Manually transcribing nameplate data is a boring, repetitive, and time consuming task that humans are poorly suited for. Learn how AI can be used to automate data extraction for TIC services providers, leading to an estimated operational cost savings of 97%.

Learn how optical character recognition works, the ways artificial intelligence is enhancing it, and what it can do for your business.

Named entity recognition (NER) is the task of identifying and categorizing key information in text. Explore the basics of NER and how you can apply it.

The rapid expansion of unstructured data is quickly reaching crisis levels. Learn why this unprecedented growth comes with problems, and how AI can be used to overcome them.

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.