Use cases

Reduce validation time and effort with AI

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

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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 expensive & labour-intensive testing

Decrease risks to product performance & quality

Shorten product development duration significantly

There are many potential use cases for AI to speed up the battery test and validation process. After an extensive evaluation, we found Monolith to be an excellent option for scaling AI across our R&D.

-Markus Meiler, VP Research & Development, Webasto

4 validation cases white paper battery testing

AI for simplifying validation testing

Four 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 70% based on battery test research from Stanford, MIT, and Toyota Research Institute.

Learn more with Monolith:

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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

Engineers reduced test time by 70% by using AI to detect data errors.

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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

Kautex engineers reduced physical tank testing with AI

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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

Jota reduced track tests by 80% and fine-tuned performance with AI

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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

Honeywell reduced product development time

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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

*A commissioned study conducted by Forrester Consulting on behalf of Monolith

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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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3 ways to identify good AI use cases in engineering

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Use cases

Reduce validation time and effort with AI

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

Reduce expensive & labour-intensive testing

Decrease risks to product performance & quality

Shorten product development duration significantly

AI for simplifying validation testing

Four applications for AI in validation test