Test plan optimisation
Maximise learning with each new test.
Reduce the tests you run
Test too much and you waste time confirming what you already know. Test too little and risk missing performance issues. Your schedule, the product’s quality, and ultimately your career depends on finding the balance.
Trusting test data
It's vital to understand that testing every possible scenario is not feasible. Over-testing confirms what's already known, while under-testing risks failing certification or missing issues. To optimize testing efforts, identify critical performance components and prioritize tests accordingly.
How we solve it:
Using self-learning models that get smarter with every test, Monolith identifies the input parameters, conditions, and ranges that most impact product performance so you do less testing, more learning, and get to market faster.
Identify an AI use case
3 ways to identify good AI use cases in engineeringLearn how you and your team of engineers can unlock the full potential of AI and transform your product development workflows, ultimately leading to greater success in an increasingly competitive marketplace.
Kautex engineers reduced physical tank testing with AI
- Problem: Vehicle acoustics
- Methods tried: CFD, physics-based simulation
- Solution: Predict noise, reduce testing with self-learning models