Use Case

Test plan optimisation

Maximise learning with each new test.

Get in Touch with Monolith

Get in Touch

Request a Demo

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

Optimize resources spent on costly test rigs and facilities

Validate your designs faster with fewer prototype iterations

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.

Watch Webinar

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.

Find Out if You're Ready for AI

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

Book a Demo

Problem:

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.

When to bring in an AI solution

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.

Adopting self-learning models & AI for Automotive

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.

Read White paper

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

Watch Webinar

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.

forrester report with monolith state of AI in engineering-1

Download Study

Download Report

Watch On-Demand Webinar

Key use cases

Test Data Validation

Coming soon

Root-Cause Analysis