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Deploy AI Systems You Can Trust

Deploy AI Systems You Can Trust

Get real value from artificial intelligence and machine learning (AI/ML) systems by being able to trust their outputs and hence enable true autonomy. Build automated systems that take decisions with confidence by quantifying how reliable model outputs really are. Achieve all of these goals through automated uncertainty quantification of your pre-trained AI/ML models by running them on Signaloid's hardware modules or on the Signaloid Cloud Compute Engine.

Get real value from artificial intelligence and machine learning (AI/ML) systems by being able to trust their outputs and hence enable true autonomy. Build automated systems that take decisions with confidence by quantifying how reliable model outputs really are. Achieve all of these goals through automated uncertainty quantification of your pre-trained AI/ML models by running them on Signaloid's hardware modules or on the Signaloid Cloud Compute Engine.

Run your pre-trained ONNX-format models on the Signaloid Compute Engine and see how uncertainties in model inputs affect the spread of possible model outputs. Direct computation of uncertainty with Signaloid's UxHw technology is easier to achieve and faster than traditional approaches based on Monte Carlo methods.

Run your pre-trained ONNX-format models on the Signaloid Compute Engine and see how uncertainties in model inputs affect the spread of possible model outputs. Direct computation of uncertainty with Signaloid's UxHw technology is easier to achieve and faster than traditional approaches based on Monte Carlo methods.

Run your pre-trained ONNX-format models on the Signaloid Compute Engine and see how uncertainties in model inputs affect the spread of possible model outputs. Direct computation of uncertainty with Signaloid's UxHw technology is easier to achieve and faster than traditional approaches based on Monte Carlo methods.

For mission-critical applications, such as medicine and finance, it is vital to know how uncertain a result is before choosing to act on it. By running your AI/ML models over Signaloid’s compute engine, you can get automated uncertainty quantification, giving you clear insight into whether a prediction has a narrow or wide set of alternative possibilities. Easily integrate the Signaloid Cloud Compute Engine or Signaloid's hardware compute modules with your current infrastructure.

Built for AI Teams

Run your pre-trained ONNX-format models on the Signaloid cloud, on-premises, and hardware modules.

Built for AI Teams

Run your pre-trained ONNX-format models on the Signaloid cloud, on-premises, and hardware modules.

Built for AI Teams

Run your pre-trained ONNX-format models on the Signaloid cloud, on-premises, and hardware modules.

Simple Integration

Your engineering teams can easily add uncertainty quantification without disrupting their existing stacks.

Simple Integration

Your engineering teams can easily add uncertainty quantification without disrupting their existing stacks.

Simple Integration

Your engineering teams can easily add uncertainty quantification without disrupting their existing stacks.

Cloud or On-Premises

Supports cloud and edge environments to fit diverse workflows and operational requirements.

Cloud or On-Premises

Supports cloud and edge environments to fit diverse workflows and operational requirements.

Cloud or On-Premises

Supports cloud and edge environments to fit diverse workflows and operational requirements.

Up to

0x

speedup

Up to

0%

reduction in software implementation cost for compute-intensive applications*

*Factor reduction in non-recurring engineering (NRE) software implementation cost time based on Constructive Cost Model (COCOMO) cost estimation methodology.

*Factor reduction in non-recurring engineering (NRE) software implementation cost time based on Constructive Cost Model (COCOMO) cost estimation methodology.

Solutions

Choose between running computations on our cloud-based compute engine, or your current on-premises infrastructure.

Signaloid Cloud Compute Engine

Our geographically-redundant deployment and auto-scaling infrastructure ensure high reliability and low latency for AI/ML applications including automated trading systems, autonomous vehicles, and human-in-the-loop medical diagnosis systems.

Contact Sales

Signaloid Cloud Compute Engine

Our geographically-redundant deployment and auto-scaling infrastructure ensure high reliability and low latency for AI/ML applications including automated trading systems, autonomous vehicles, and human-in-the-loop medical diagnosis systems.

Contact Sales

Signaloid Cloud Compute Engine

Our geographically-redundant deployment and auto-scaling infrastructure ensure high reliability and low latency for AI/ML applications including automated trading systems, autonomous vehicles, and human-in-the-loop medical diagnosis systems.

Contact Sales

AWS Outpost On-Premises Solution

Build on your existing on-premises CPU and GPU deployments. Achieve orders of magnitude speedup and reduced infrastructure and operational costs for AI/ML applications including automated trading systems, autonomous vehicles, and human-in-the-loop medical diagnosis systems.

Contact Sales

AWS Outpost On-Premises Solution

Build on your existing on-premises CPU and GPU deployments. Achieve orders of magnitude speedup and reduced infrastructure and operational costs for AI/ML applications including automated trading systems, autonomous vehicles, and human-in-the-loop medical diagnosis systems.

Contact Sales

AWS Outpost On-Premises Solution

Build on your existing on-premises CPU and GPU deployments. Achieve orders of magnitude speedup and reduced infrastructure and operational costs for AI/ML applications including automated trading systems, autonomous vehicles, and human-in-the-loop medical diagnosis systems.

Contact Sales

Technology Explainers

Signaloid's technology provides deterministic uncertainty tracking methods by utilising efficient arithmetic on probability distributions while integrating with existing edge hardware. These technology explainer articles provide further details on how this technology benefits AI and Machine Learning Models, from the underlying mathematics to the engineering implementations.

General

Implementing Gaussian Processes with Uncertain Inputs Using UxHw

General

Implementing Gaussian Processes with Uncertain Inputs Using UxHw

General

Implementing Gaussian Processes with Uncertain Inputs Using UxHw

General

Using UxHw Technology to Quantify Uncertainty of AI/ML Model Outputs

General

Using UxHw Technology to Quantify Uncertainty of AI/ML Model Outputs

General

Using UxHw Technology to Quantify Uncertainty of AI/ML Model Outputs

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Schedule a Demo Call

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