AI Testing
The NELTA AI testing works in two directions: On the one hand, we check the quality of your AI model for accuracy, fairness, and robustness. On the other hand, we use AI tools for intelligent testing of traditional software.
AI Testing
The NELTA AI testing works in two directions: On the one hand, we check the quality of your AI model for accuracy, fairness, and robustness. On the other hand, we use AI tools for intelligent testing of traditional software.
AI Testing
The NELTA AI testing works in two directions: On the one hand, we check the quality of your AI model for accuracy, fairness, and robustness. On the other hand, we use AI tools for intelligent testing of traditional software.
Our testing not only determines whether the system works, but also how well it works. Only in this way can companies reliably harness the full potential of their AI applications.
Our testing not only determines whether the system works, but also how well it works. Only in this way can companies reliably harness the full potential of their AI applications.
AI becomes valuable when it is understandable and reliable. We ensure that.
AI becomes valuable when it is understandable and reliable. We ensure that.
Trust and Acceptance. We ensure that customers and employees can understand the decisions made by AI.
Risk Minimization. AI testing uncovers bias and unpredictable behavior before they become detrimental to business.
Enhanced AI Performance. Systematic feedback improves the precision, stability, and reliability of the models.
Objective Quality Evidence. Measurable metrics demonstrate the quality of AI, relevant for stakeholders and audits.
More Efficient Testing Process. The use of AI in testing itself allows for faster detection of errors in traditional software.
Trust and Acceptance. We ensure that customers and employees can understand the decisions made by AI.
Risk Minimization. AI testing uncovers bias and unpredictable behavior before they become detrimental to business.
Enhanced AI Performance. Systematic feedback improves the precision, stability, and reliability of the models.
Objective Quality Evidence. Measurable metrics demonstrate the quality of AI, relevant for stakeholders and audits.
More Efficient Testing Process. The use of AI in testing itself allows for faster detection of errors in traditional software.
Especially the innovative small and medium-sized enterprises need an extra boost in security right now.
Especially the innovative small and medium-sized enterprises need an extra boost in security right now.
Many companies are already using AI components – e.g., in production, e-commerce, customer service, or for data-driven forecasts. But often this is still associated with uncertainties in business operations.
Does that sound familiar? “Our AI is a black box – we don’t know exactly why it makes certain decisions. We are concerned that our AI system discriminates against customers due to biased training data. Our chatbot sometimes gives nonsensical or incorrect answers. Customers are therefore frustrated.”
Let’s have a conversation. Together, we will ensure the quality of your solutions.
Many companies are already using AI components – e.g., in production, e-commerce, customer service, or for data-driven forecasts. But often this is still associated with uncertainties in business operations.
Does that sound familiar? “Our AI is a black box – we don’t know exactly why it makes certain decisions. We are concerned that our AI system discriminates against customers due to biased training data. Our chatbot sometimes gives nonsensical or incorrect answers. Customers are therefore frustrated.”
Let’s have a conversation. Together, we will ensure the quality of your solutions.
Our approach
Our approach
The QA for intelligent systems
The QA for intelligent systems
In 4 steps, we take your AI applications to a new level.
In 4 steps, we take your AI applications to a new level.
1.
Context analysis
First, we want to understand your AI model and its purpose in detail.
2.
Data validation
We check the quality and balance of your training and test data.
3.
Model evaluation
With specialized metrics, we test the AI model for fairness, accuracy, and robustness.
4.
Monitoring concept
We are developing a strategy to monitor the performance of AI even in live operations.
5.
Continuous improvement
Upon request, we provide advice for the continuous improvement of your QA for intelligent systems.
1.
Context analysis
First, we want to understand your AI model and its purpose in detail.
2.
Data validation
We check the quality and balance of your training and test data.
3.
Model evaluation
With specialized metrics, we test the AI model for fairness, accuracy, and robustness.
4.
Monitoring concept
We are developing a strategy to monitor the performance of AI even in live operations.
5.
Continuous improvement
Upon request, we provide advice for the continuous improvement of your QA for intelligent systems.
1.
Context analysis
First, we want to understand your AI model and its purpose in detail.
2.
Data validation
We check the quality and balance of your training and test data.
3.
Model evaluation
With specialized metrics, we test the AI model for fairness, accuracy, and robustness.
4.
Monitoring concept
We are developing a strategy to monitor the performance of AI even in live operations.
5.
Continuous improvement
Upon request, we provide advice for the continuous improvement of your QA for intelligent systems.
