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.
Product offerings
The Lumenova AI complete solution
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
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
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
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
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
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
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
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
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
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
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
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