Our AI Software
No-code AI software built for engineers
Monolith is a machine learning AI platform built by engineers, for engineers. No coding or Ph.D. in statistics is required - just your engineering expertise and test data.
A commissioned study conducted by Forrester Consulting on behalf of Monolith
The State of AI in Engineering
The first-ever study on AI in product development surveys US and European automotive, aerospace, and industrial engineering leaders.
Discover our top data-driven business applications and engineering use cases for Aerospace & Defense, Automotive, and Industrial customers.
Monolith's AI afotware supports a diverse range of industries, engineering teams, and business requirements. If you work with any type of data; from data science, to data entry, data preparation, data management, or make data-led decisions—our AI software can help you do more with less.
Use Monolith’s intelligent exploration artificial intelligence tools to sort and visualise track test data, inform your engineers, and train machine learning models on manoeuvres to predict the forces on the vehicle during other manoeuvres that were not performed—saving testing time on complex tasks.
Monolith's AI software instantly determines what smart meter designs give the best performance, while self-learning models learn from new data generated along the way, indicating which designs are most promising to investigate next through machine learning.
Wind Tunnel Testing
Build 3D artificial intelligence (AI) models that directly predict wind tunnel performance from a CAD design – faster and more accurately than Computational Fluid Dynamics (CFD) simulation alone.
Use AI to predict fuel-sloshing noise, solve intractable physics
- Problem: Vehicle acoustics
- Methods tried: CFD, physics-based simulation
- Solution: Predict noise, reduce testing with self-learning models
“Monolith allowed us to understand and optimize the gas meter's behaviour for all operating conditions and optimize meter accuracy under extreme conditions, allowing us to build a superior, more accurate product in a much shorter amount of time.”
“Monolith Team understood what it means to work with genuine engineering problems in artificial intelligence: the needed flexibility and knowledge. ”
“The Monolith engineering team are unique experts in the field of solving scientific problems with machine learning. I trust them to guide our team to get the greatest ROI from AI and magnify our engineering talent and expertise. ”
“With Monolith’s machine learning method, we not only solved the challenge, we also reduced design iteration times and prototyping and testing costs. We are thrilled with the results, and we are confident we have found a way to improve future design solutions.”
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 sensors
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.
- Prepare your data for optimal AI performance
- Catch bad data now so you avoid re-engineering later
- Find outliers in your test data fast with intuitive visualisation
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 failure
Product design issues during validation risk launch delays and lost market share. Pressure on engineers is high 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 the failure
- Identify components causing sub-optimal performance
- Avoid long delays and uncertainty in the validation process
4. Calibrate for thousands of conditions
Designing highly complex, non-linear systems that must 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
Get started with Monolith, your trusted AI partner
Learn how to begin AI adoption in your engineering organization and understand the technical requirements for the applicability of artificial intelligence software to your organization.
Find out how we support the world's top engineering teams, and how you can be sure you will succeed with AI to deliver better quality products in half the time.
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.
JOTA cuts car setup time by 50% with AI
Since teaming up with Monolith, JOTA engineers can better understand and predict the aerodynamics of their cars by building AI self-learning models. As a result, they reduced the number of simulations and tests by 50%, cut car time-to-setup in half, and achieved a 66% reduction in overall costs.