Test Data Evaluation
Test engineers spend a significant amount of time in reviewing, cleaning, and understanding data, which includes audio signals, CAN-Bus data, and time series.
AI-based applications can save time by automatically finding critical events in time series and acoustic data. Additionally, AI data assistant help do democratize complex data analysis tasks to domain experts.
Why Renumics
We Combine Expertise in ML and Test Data
Our team holds comprehensive expertise in machine learning and has successfully delivered multiple projects across the engineering domain. We specialize in building AI-driven systems that focus on the specific needs of test engineers.
Our systems can handle large amounts of data, connect numerous data sources, and integrate with proprietary software. Many of the systems are currently operational in production environments.
We have shown that test engineers can create a new testing report twice as fast when working alongside an AI. The AI advantage becomes even larger for highly complex tasks such as anomaly detection and root cause analysis.
- Save time & costs
We can achieve time savings of 50-80% in evaluating audio test data with a custom-developed AI-driven system at the production stage.
- Complex queries made easy
Our natural language interface for test data analysis enables complex SQL queries in seconds without writing a single line of code.
Explore the Potential of AI in the Domain of Test Engineering
Discover more use cases for AI-driven test data evaluation and find out how Renumics can assist you. We are your ideal partner for industrial AI.
Talk With Your Data
Test Data Analysis with Natural Language Processing
Test engineers often rely on dashboards that are built by dedicated data teams. Changing the dashboard or answering ad-hoc questions can be a difficult process.
AI assistants enable engineers to directly ask questions on their data and to build custom dashboards – without specific programming skills. Get in touch with our team to learn more.
Classification of Acoustic Events
Experienced engineers use acoustic information to derive critical insights on the performance and status of complex parts. However, analyzing audio data is time consuming and heavily depends on the subjective judgement of the engineer. Pre-trained machine learning models can reliably speed up this process and deliver more consistent results.
- Trustworthy Results
We use state-of-the-art models that can accurately evaluate acoustic data and output explainable results.
- Interactive Analysis
Validate evaluation results and generate customizable reports from your data with an interactive UI.
Anomaly Detection on CAN-Bus & Sensor Data
Finding critical patterns in testing data can be a time-consuming step. Often, these patterns cannot be described by simple rules. In this case, Machine learning enables to detect and classify critical events in low and high-frequency sensor data, saving valuable time.
Our team develops AI-driven systems that provide robust automated classification, anomaly detection, and data searching capabilities for sensor data to assist test engineers.
We design and implement solutions that provide real benefits to testing workflows, and can be seamlessly integrated into existing processes. Experience our CAN-Bus data demo by signing up below.
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CAN-Bus Data Demo
Examine the potential of AI in the domain of test data evaluation. Get started with our demo and test it for yourself.
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