Use Case

Test plan optimisation

Maximise learning with each new test. 

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BAE Systems

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. 

Run the most important tests and skip the rest
error detection
Optimize resources spent on costly test rigs and facilities
catch bad data
Validate your designs faster with fewer prototype iterations
HubSpot Video
New Feature

Next Test Recommender (NTR): AI-Powered Test Plan Optimisation

Learn how our AI software's latest feature enables users to train and assess machine learning models. It offers valuable recommendations for optimal test conditions to apply in the next round of testing. NTR assesses previously gathered data to suggest the most effective new tests to conduct. 

Are you ready for AI?

AI Readiness Scorecard

Complete Monolith's 3-minute assessment to develop an understanding of your organisation's readiness for AI, and which areas can benefit from the implementation of AI through an in-depth report sent directly to you. 

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No code software

AI built by engineers for engineers

  • Avoid wasted tests due to faulty data
  • Build just the right test plan - no more, no less 
  • Understand what drives product performance and failure
  • Calibrate non-linear systems for any condition 
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.

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self learning models for AI
How we solve it:
Revolutionised testing 

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 engineering 

Learn 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.
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HubSpot Video
Kautex-Textron webinar

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
A commissioned study conducted by Forrester Consulting on behalf of Monolith

The State of AI in Engineering  

First-ever study on AI in product development surveys US and European automotive, aerospace and industrial engineering leaders.

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Key use cases 

Test Data Validation
Root-Cause Analysis 

Ready to get started?

BAE Systems