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Introducing scalable AI across distributed IoT systems.

Creating a new paradigm for ML.

Enabling the creation of scalable networks and continuously & collaboratively learning devices.

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Deploy up to 500 devices in 30 mins and reduce the number of learning cycles by 90% and training time x10. Purus in massa tempor nec feugiat nisl pretium.

Lorem ipsum dolor sit amet

Deploy up to 500 devices in 30 mins and reduce the number of learning cycles by 90% and training time x10. Purus in massa tempor nec feugiat nisl pretium.

Lorem ipsum dolor sit amet

Deploy up to 500 devices in 30 mins and reduce the number of learning cycles by 90% and training time x10. Purus in massa tempor nec feugiat nisl pretium.

Lorem ipsum dolor sit amet

Deploy up to 500 devices in 30 mins and reduce the number of learning cycles by 90% and training time x10. Purus in massa tempor nec feugiat nisl pretium.

Lorem ipsum dolor sit amet

Deploy up to 500 devices in 30 mins and reduce the number of learning cycles by 90% and training time x10. Purus in massa tempor nec feugiat nisl pretium.

Lorem ipsum dolor sit amet

Deploy up to 500 devices in 30 mins and reduce the number of learning cycles by 90% and training time x10. Purus in massa tempor nec feugiat nisl pretium.

Features out of the box.

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The benefits

Greater accuracy

x2

Reduction in communication overhead costs

x50

Reduction in wall to wall learning times

x10

Fixed per hour CPU cost

£0.60

Fixed OP infrastructure cost

£150

Reduction in cloud CPU/GPU cost

x100

AI team overhead saving

x3-4

TCO reduction

x10

Secure AI

Only model parameters are shared from edge devices on the network, which are aggregated and encrypted. This ensures data confidentiality and avoids any risk involved with the transfer or large volumes of sensitive data.

Primary Secondary

SSO User Access
Secure Communication
Development Environment
SSO Integration
Token Handling
Managed Identity
Device Authentication

Unparalleled edge hardware integration.

Enabling the creation of scalable networks and continuously & collaboratively learning devices.

Constrained device edge

Microcontroller-based, highly distributed in the physical world

Smart Device edge

Includes IoT (headless) and end user client compute in accessible locations

On-prem data centre edge

Server based compute in secure locations

Access edge

Server-based compute at Telco Network and Edge Exchange sites

Regional edge

Server-based compute at regional Telco and Direct Peering sites

Centralised data centers

Server-based compute in traditional Cloud Data Centers

Supported models

Only model parameters are shared from edge devices on the network, which are aggregated and encrypted. This ensures data confidentiality and avoids any risk involved with the transfer or large volumes of sensitive data.

Primary Secondary

Regressor compatible

Linear
Ridge
Elastic net
Random forest
Tweedie
Quantile
Support vector
Stochastic gradient descent
Baysesian ridge
Ard
Gradient boosting
Extreme gradient boosting

Classification

Linear
Ridge
Elastic net
Random forest
Tweedie
Quantile
Support vector
Stochastic gradient descent
Baysesian ridge
Ard
Gradient boosting
Extreme gradient boosting

Cluster and smoothing

Linear
Ridge
Elastic net
Random forest
Tweedie
Quantile
Support vector
Stochastic gradient descent
Baysesian ridge
Ard
Gradient boosting
Extreme gradient boosting

Model management

Linear
Ridge
Elastic net
Random forest
Tweedie
Quantile
Support vector
Stochastic gradient descent
Baysesian ridge
Ard
Gradient boosting
Extreme gradient boosting

Model versioning

Linear
Ridge
Elastic net
Random forest
Tweedie
Quantile
Support vector
Stochastic gradient descent
Baysesian ridge
Ard
Gradient boosting
Extreme gradient boosting