Use Cases

Reduce validation time and effort with AI.

Get products to market faster with self-learning models from Monolith


BAE Systems

Transform test data into powerful self-learning models for a smarter approach to design validation and test.

Monolith is built specifically to solve engineering test and validation challenges.  Our tools are custom-tailored based on hundreds of projects with top engineering teams to address specific testing challenges like optimising complex test plans, inspecting hundreds of test channels for anomalies, calibrating complex systems to meet specific requirements, or finding the root-cause of head-scratching failures.  Built by engineers for engineers, we put your test data to work to solve real engineering challenges.  

Reduce cost
Reduce expensive & labour-intensive testing.
Data Warning
Decrease risks to product performance & quality.
Shorten Product Development
Shorten product development duration significantly.
4 validation cases white paper battery testing
AI for simplifying validation testing 

4 applications for AI in validation test


AI has a significant impact on validation testing in engineering product development. You can reduce testing by up to 73% based on battery test research from Stanford, MIT, and Toyota Research Institute. Learn more with Monolith:

Four ways to use AI to cut validation costs

Build shorter test plans, create fewer prototypes, find errors faster, and validate designs more quickly using AI-based self-learning models. 


1. Automatically detect faulty measurements

To avoid downstream issues, your engineers must tediously inspect test data for errors. One failed sensor or wiring malfunction can render thousands of dollars in testing useless and set your schedule back significantly.

  • Find outliers in your test data fast with intuitive visualisation
  • Prepare your data for optimal AI performance
  • Catch bad data now to avoid re-engineering later

avoid wasted tests_Dashboard Kistler
Reduce Tests to Run_Kautex_laptop

2. Predict the critical tests to run

Test too much and you waste time confirming what you already know. Test too little and risk missing performance issues. Schedule, quality and your career depend on finding the balance. 

  • Run the most important tests and skip the rest
  • Optimize resources spent on costly test rigs and facilities
  • Validate your designs faster with fewer prototype iterations 

3. Determine the cause of system failures faster


Product design issues during validation risk launch delays and lost market share. Engineers are under pressure to identify critical parameters causing failure, quickly analyze the root cause, and predict how the product will perform in changing conditions.

  • Predict what design changes will most likely fix failures
  • Identify components causing sub-optimal performance
  • Avoid long delays and uncertainty in the validation process
Know Critical Parameters to Test-jota_tyre_degradation_1
Test for Thousands of conditions_Honeywell_3

4. Calibrate for thousands of conditions


Designing highly complex, non-linear systems to meet stringent performance standards is challenging. Predicting which combination of inputs will deliver the optimal output, in all operating conditions, is next to impossible.

  • Calibrate complex dynamic systems
  • Ensure your system performs to spec in all conditions
  • Find best-fit values across 1000's of inputs and conditions
*A commissioned study conducted by Forrester Consulting on behalf of Monolith

It is imperative to get to market faster.

You need to get to market faster with revolutionary new EV batteries but you can't rely on your current methods of physical testing and simulation. 

In our 2024 study, 58% say AI is crucial to stay competitive. Here are other highlights:


  • 64% believe it's urgent to reduce battery validation  
  • 66% say it's imperative to reduce physical testing
  • 62% indicate virtual tools don't fully ensure designs meet validation requirements
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.

ai use cases white paper monolith

Key use cases 

test plan optimize
Test Plan Optimisation
test Data Validation
Test Data Validation
root-cause analyiss
Root-Cause Analysis 
System calibrtaion


Discover more AI resources 

Are you ready for AI?
3 Ways To Identify Good AI Use Cases in Engineering White Paper-2
White Paper
3 Ways To Identify Good AI Use Cases in Engineering 
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3 Ways AI Leaders Can Benefit From Self-Learning Models

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