Turn test data into decisions. In under 20 minutes.
Are your insights getting lost between data exports, lab notes, and spreadsheet filters? Upload your test data and quickly see which parameters relate to your outcome and which experiment series deserve your attention first.
- Hardware & DeepTech startups
- Testing labs & test benches
- TÜV-related organizations
- Research & development teams
The Problem
Many tests create plenty of data, but not much clarity. Without a clean evaluation flow, your team spends time on outliers, redundant series, and gut feel instead of defensible next steps.
- Manual evaluation eats up valuable lab time every week
- Important patterns stay buried in tables and notes
- Next experiments are guessed instead of prioritized
- Insights are hard to document and share across the team
The Solution
dAIve Predict turns experiment data into a clear decision layer: make patterns visible, prioritize the biggest drivers, and compare variants more confidently.
See which factors actually drive the outcome
Correlations and model-based analysis show where the real signal sits in your test data.
Compare test variants instead of raw table rows
Evaluate configurations more systematically and spot which areas deserve deeper follow-up.
Prioritize the next worthwhile experiments
Use the results to avoid redundant tests and focus follow-up work where it matters most.
less unnecessary test effort when you prioritize experiments based on data instead of gut feel. Scattered results turn into a clearer direction much faster.
Indicative value from experience, to be verified with your data in a pilot.
How it works
Load test data
Import your experiment data, e.g. as a CSV table.
Define goals
Choose target variables and relevant drivers.
Recognize patterns
dAIve Predict reveals relationships, drivers, and unusual ranges.
Prioritize next tests
Compare variants with data and plan follow-up work more deliberately.
Every unnecessary test series costs lab time.
Start your 14-day trial and use your first dataset to see which experiments are truly worth continuing.