neurocat Solutions

neurocat focuses on assessing and mitigating risks in Machine Learning (ML) applicatinos and helps companies across all industries to identify issues related to the safety and security of ML products.

Our expertise is embedded into existing MLOps workflows, with a focus on managing the risks by testing against robustness concerns before deployment.

Our Expertise

Research

Our expert research team supports you in the fields of robustness, explainability and risk evaluation aimed at improving the quality of AI Systems. Our work has resulted in the accomplishment of 5 patent applications and 6 scientific publications.

Consulting

Helping customers to develop and integrate quality process through systematic risk analysis to evaluate and mitigate risk before deployment of AI Systems. Completed 25+ service and consulting projects since our inception.

Analysis

Enabling customers to structure the threat model and develop a test strategy that is in line with the safety goals and requirements of the use-case.

Optimization

Support customers through novel and lightweight approaches to optimize AI Systems for protection against risks and vulnerabilities without sacrificing performance.

Governance

Support in generation of evidence for Systematic Safety Argumentation through reproducible safety artefacts to meet quality requirements of both internal and external stakeholders.

MLOps

Finally, seamlessly integrate test strategy and quality process into MLOps lifecycle and existing tech-stack. Enable automating quality assessment though customized solutions and out-of-box solutions through our SaaS offering – aidkit.

Looking for a Solution to Improve the Quality of Your ML Applications?

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Our Research Approach

Use Case Analysis

Understanding the use case and the application scope is important to build right Continuous Testing (CT) strategies. We apply our vast research and industry know-how to break down use-cases to examine potential vulnerabilities that need to be addressed.

1

Threat Modeling

Taxonomy in line with the potential vulnerabilities identified for the use-case, to build a foundation for integration of new threats and mitigation strategies.

2

Test Strategy

Classification of risk with the help of taxonomy and identify mitigation approaches through equivalence test classes.

3

Test Methods

Identifying and aggregate suitable test methods for safety and security concerns in combination with your team, our industry experts, and our product aidkit.

4

Risk Evaluation

Setting thresholds for the Quality Gate in line with use-case objectives and systematic evaluation through artifact generation to create validated development hypothesis and safety argumentation.

5

Risk Mitigation

Consult and support you with tooling to improve the respective quality metrics and build a custom use-case specific approach to continuously test and mitigate risks. Focus on delivering satisfactory ML products for you and your users.

6

1

Use Case Analysis

Understanding the use case and the application scope is important to build right Continuous Testing (CT) strategies. We apply our vast research and industry know-how to break down use-case to examine potential vulnerabilities that need to be addressed.

2

Threat Modeling

Taxonomy in line with the potential vulnerabilities identified for the use-case, to build a foundation for integration of new threats and mitigation strategies.

3

Test Strategy

Classification of risk with the help of taxonomy and identify mitigation approaches through equivalence test classes.

4

Test Methods

Identifying and aggregate suitable test methods for safety and security concerns in combination with your team, our industry experts, and our product aidkit.

5

Risk Evaluation

Setting thresholds for the Quality Gate in line with use-case objectives and systematic evaluation through artifact generation to create validated development hypothesis and safety argumentation.

6

Risk Mitigation

Consult and support you with tooling to improve the respective quality metrics and build a custom use-case specific approach to continuously test and mitigate risks. Focus on delivering satisfactory ML products for you and your users.