There has been a major shift in the use of AI across sectors and companies. But the process of embedding AI is complex and time-consuming, especially in transport industries like rail and aerospace.
Amygda’s time series modelling approach provides flexible and reusable AI solutions for multiple use cases across an enterprise’s whole data domain. This means companies don’t have to wait for large amounts of data to be annotated in order for them to start seeing returns on their investment in AI.
By not relying on well-annotated historical information or labels, we remove the major blocker in scaling AI across your whole fleet of multiple use cases, enabling full fleet coverage for any equipment.
Why now?
By 2025, enterprise AI will be entirely run on AI models that can solve multiple use cases. These models will be general-purpose, meaning that they can be trained on data to learn how to represent it and then fine-tuned for specific tasks. The infrastructure necessary to operationalize these models for multiple data volumes, velocities and veracities is a major innovation.
