Case Study: RapidAI accelerates sports video annotation and builds flexible ontologies with Encord

Reducing MRI and CT Annotation Time by 70% for Rapid AI

RapidAI, a leading sports analytics company, faced challenges scaling its machine learning development due to the limitations of its single-purpose in-house annotation tool. The tool, built for speed, lacked flexibility, restricting the ML team from iterating on new models and building new features. This made their strategy of building a new tool for each task unsustainable, hindering their ability to innovate.

Encord provided a solution with its API access and a feature for rapid multi-frame object detection, allowing annotators to label objects with a single click per frame. This enabled RapidAI to build new ontologies in minutes instead of months. The result was a transformative increase in speed and flexibility, allowing the ML team to be more adventurous in developing new products and features without the previous high cost of pursuing new ideas.

RapidAI

Ryan Mason

Neuroradiologist Overseeing Annotations