neurocat Solutions

neurocat focuses on assessing and mitigating risks in Machine Learning (ML) applications 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 robustness before deployment.

Our Expertise

Research

Our expert research team supports customers in the fields of robustness, explainability, and risk evaluation in order to improve the quality of your AI systems. Our work has led to five patent applications and six scientific publications.

Consulting

We help customers develop and integrate quality assurance processes through systematic risk analysis that evaluates and mitigates risk before the deployment of AI systems. We have completed 25+ service and consulting projects since our inception.

Analysis

We enable customers to structure an appropriate threat model and develop a test strategy that is in line with the safety goals and requirements of the use-case. Understand and identify the risks to your AI systems with bespoke solutions.

Optimization

We support customers through novel and lightweight approaches that optimize AI systems. Protect your AI system against risks and vulnerabilities without sacrificing performance.

Governance

We support in the generation of evidence for Systematic Safety Argumentation through reproducible safety artefacts. Meet the quality and compliance requirements of both internal and external stakeholders.

MLOps

neurocat seamlessly integrates test strategy and quality assurance processes into your MLOps lifecycle and existing tech-stack. Enable automated quality assessment though customized solutions or an out-of-box solution through our SaaS offering – aidkit.

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

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Our Approach

Use Case Analysis

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

1

Threat Modeling

We build the foundation for the integration of new threats into mitigation strategies by developing a taxonomy in line with the potential vulnerabilities identified for the use-case.

2

Test Strategy

A testing strategy through equivalence test classes operationalizes the taxonomy in order to classify your risks and identify mitigation strategies.

3

Test Methods

We work with your team, our industry experts, and our product aidkit to put the test strategy into practice by identifying and aggregating suitable test methods to address your safety and security concerns.

4

Risk Evaluation

In line with use-case objectives we help you set thresholds for the Quality Gate and systematically evaluate risk through artifact generation. Together we then create validated development hypotheses 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. We focus on delivering optimized 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.