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.
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:
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
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
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
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
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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