Compare material data better. For material and test datasets.
When material tests, experiment series, and result data are spread across tables, lab notes, and isolated charts, dAIve Predict helps teams make drivers, differences, and next test paths more visible.
- Material development and material engineering
- Test benches and lab environments
- R&D teams working with tabular test data
- Teams that want to compare material variants more consistently
The Problem
Material data is often available, but the important relationships between parameters, test conditions, and outcomes remain difficult to compare.
- Results are spread across files, notes, and one-off charts
- Variants are reviewed side by side instead of compared systematically
- Key drivers are hard to prioritize
- Agreeing on the next step takes too much team effort
The Solution
dAIve Predict brings material data into one guided workflow: import, describe, train, compare, and derive the next useful question from the result.
Compare material variants on a shared data basis
Use consistent inputs and targets to review differences between experiment series more clearly.
Make the main drivers visible
Correlations, model metrics, and structured evaluation help teams identify relevant parameters faster.
Use predictions as an additional decision aid
Trained models help classify additional variants and plan follow-up tests more deliberately.
dAIve Predict helps teams evaluate material data more consistently, narrow down the main drivers faster, and prioritize follow-up tests with more clarity.
Indicative value from experience, to be verified with your data in a pilot.
How it works
Export data
Export material and test data from your existing tooling, e.g. as a CSV table.
Define inputs
Choose the relevant parameters and target variables.
Train model
Use dAIve Predict to surface relationships and train models.
Compare results
Use metrics, correlations, and predictions for further evaluation.
Use your material data as a better decision base.
Start with your first dataset and see which parameters and variants deserve closer review next.