Understand your model’s behavior at a glance

Get AI explainability, fairness, compliance, and security in one single platform with flexible set-up and use.

Enterprise-centric Responsible AI

Lumenova automates the complete Responsible AI lifecycle

Our AI Trust Platform helps you accelerate the adoption of AI and manage AI risks.

Gain real-time insights into the reasoning behind outcomes, monitor ML performance, and leverage Responsible AI to promote transparency and accountability.

Design user-friendly procedures and policies that increase company-wide awareness of Al risk exposure and address compliance shortcomings.

Lead with trust in AI.

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Product offerings

The Lumenova AI complete solution

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Policy Frameworks

Develop, document and track progress for risk management and regulatory compliance objectives.

Save time and resources
  • Industry and regulatory frameworks
  • Risk management
  • Policy repository
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Evaluation Engine

Perform technical assessments of AI models, as specified by your defined policy frameworks.

Stay agile and maximize performance
  • Broadest technical scope
  • Model risk alerts and warnings
  • Alignment with existing data platforms
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Monitor & Improve

Automate the continuous evaluation and reporting on your AI models and get a headstart towards remediation.

Detect and mitigate ongoing AI risks
  • Monitoring configuration
  • Remediation head start
  • AI improvement platform
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Policy Frameworks

Evaluation Engine

Monitor & Improve

Develop, document and track progress for risk management and regulatory compliance objectives.

Perform technical assessments of AI models, as specified by your defined policy frameworks.

Automate the continuous evaluation and reporting on your AI models and get a headstart towards remediation.

Save time and resources
Stay agile and maximize performance
Detect and mitigate ongoing AI risks
  • Industry and regulatory frameworks
  • Broadest technical scope
  • Monitoring configuration
  • Risk management
  • Model risk alerts and warnings
  • Remediation head start
  • Policy repository
  • Alignment with existing data platforms
  • AI improvement platform

Platform capabilities

Meet all of your Responsible AI needs with one platform

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Fairness

  • Analyze your model's predictions to make sure they are not biased
  • Measure and compare a multitude of fairness metrics across the intersection of sensitive attributes
  • Evaluate fairness in a wide range of model types
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Explainability & Interpretability

  • Uncover how each individual input influences the model's decision-making process
  • Gain insights into what your AI has actually learned during training
  • Determine how consistent your model is in the way it uses features across different predictions
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Security & Resilience

  • Identify potential model issues and weak spots
  • Discover adversarial vulnerabilities which make the model unreasonably sensitive to changes in the input
  • Check if your AI relies too heavily on only a few dominant features when making predictions
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Validity & Reliability

  • Measure predictive performance with a multitude of metrics, including Accuracy, Precision, and Recall
  • Ensure your model's performance is consistent across the whole feature space without concealed weak spots
  • Analyze the extent to which your model is affected by data distribution drift
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Data Integrity

  • Uncover data quality issues, such as class unbalance, outliers, unusual distributions, missing data, data drift
  • Assess whether data is impartial to sensitive attributes or if it contains biases that can translate into model unfairness
  • Uncover mislabelled training and test data samples that can impact model performance
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Fairness

Explainability & Interpretability

  • Analyze your model's predictions to make sure they are not biased
  • Uncover how each individual input influences the model's decision-making process
  • Measure and compare a multitude of fairness metrics across the intersection of sensitive attributes
  • Gain insights into what your AI has actually learned during training
  • Evaluate fairness in a wide range of model types
  • Determine how consistent your model is in the way it uses features across different predictions
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Security & Resilience

Validity & Reliability

  • Identify potential model issues and weak spots
  • Measure predictive performance with a multitude of metrics, including Accuracy, Precision, and Recall
  • Discover adversarial vulnerabilities which make the model unreasonably sensitive to changes in the input
  • Ensure your model's performance is consistent across the whole feature space without concealed weak spots
  • Check if your AI relies too heavily on only a few dominant features when making predictions
  • Analyze the extent to which your model is affected by data distribution drift
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Visual

Data Integrity

  • Uncover data quality issues, such as class unbalance, outliers, unusual distributions, missing data, data drift
  • Assess whether data is impartial to sensitive attributes or if it contains biases that can translate into model unfairness
  • Uncover mislabelled training and test data samples that can impact model performance

Lumenova AI
The fastest route from Black Box to Trustworthy AI

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