AI's paradigm shift
Time series foundation models
for sensor data
Reduce the time for each new use case for all assets in your fleet from months to weeks (and soon to days), using flexible and reusable AI models.

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The Problem
Current AI solutions are built by looking at patterns represented in well-annotated historical data and using feature engineering to build specific features for specific use cases.

The model that is produced is tailored to a specific use case - and can hardly be reused for another, even a similar use case.

Models for each use case take 4 to 6 months to build.

However, there are major problems with the current approach.
  • Lack of well-annotated data
    AI relies on annotated data, which is one of the biggest bottlenecks for AI adoption - collecting data is hard, but collecting and then annotating the data is even harder.
  • Long model build time
    Currently, a model is produced for a specific use case, and can hardly be reused for another, even a similar use case. This makes comprehensive coverage of assets a very long and expensive process.
  • Blackbox AI that can't be explained
    Without interpretability, the output of AI solutions in the engineering domain is untrustworthy. And there is a mistrust of AI adoption for fleet services.
Amygda's time series foundation model
A shift towards flexible and reusable AI models. Amygda's time series foundation models can be used for multiple use cases including smart maintenance, route optimisation, or even equipment life forecasting.
Amygda's flexible and reusable AI models reduce the time for each new use case from months to weeks.
One model. Multiple use cases.

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.
Benefits of this new approach

Amygda's AI models overcome a major challenge in the lack of large sets of well-annotated data. This presents a significant advantage in real-world applications, as it eliminates the risk of equipment failure on critical assets.

Amygda's models are built using unsupervised and semi-supervised techniques, with further development focusing on self-supervised learning. This approach has reduced reliance on well-annotated datasets, paving the way to addressing novel, diverse and less understood use cases.
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.
Why work with us?
  • Get ROI across your whole fleet
    As a fleet operator, you own multiple assets and equipment from several different OEMs. Our AI platform works across any equipment in your fleet, irrespective of its type or age. So you can get whole fleet coverage for any use case.
  • Embed AI across the business
    Scaling AI improves productivity. Multiple teams managing fleets of assets can now benefit from Amygda's solutions. Our AI platform is scalable and enables business leaders to embed AI in various use cases across the business.
  • Faster time to value
    Equipped with billions of data points from 100s of different equipment types, Amygda is already demonstrating a reduction in time to value from months to weeks, and in some instances, days. So you can demonstrate business value faster.
Download our White Paper
on flexible and reusable AI models and
their benefits in the enterprise domain