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- Instantly predict the performance and/or quality of new 3D designs through visual data
- Generate performance-optimized 3D designs with AI adoption
- Have the confidence to cut long transient simulations short by predicting the remaining time steps with an AI model
- Create effective test plans informed by historical data, raw data, training data, structured data or even unstructured data
- Detect if sensors aren't mounted or connected properly for data preparation
- Re-adapt test plans on the fly with custom models and enable users
- Quickly identify and correct drift in measurements with our AI models
- Know when enough data has been captured for results to be gathered from no-code AI platforms
- Identify patterns and dependencies to better understand complex engineering systems with machine learning models
- Provide customers with instant quotes for the performance of products in new configurations on a no-code AI platform
- Accurately calibrate measuring devices across a range of operating conditions
- Get your products certified quicker with accurate business data through machine learning and a no-code AI approach
- Predict readings from missing or broken sensors using AI models
- Understand how objective performance indicators will affect the subjective experience of your end customers or business users through AI 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.”
