Test Data Validation

Find more data errors faster with AI-guided anomaly detection

Validate your data using AI.  Find sensor and system issues while still testing.  Avoid wasted test runs.

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Automate data validation with AI. Scan hundreds of test results in minutes. Find anomalies before it's too late.

In order to prevent future problems, your engineers need to carefully inspect the test data for any errors. One failed sensor or wiring malfunction can render thousands of dollars in testing useless and set your schedule back significantly. With Monolith, you can automate the inspection of your test results using AI-driven anomaly detection tools. By finding more issues faster, you avoid costly retesting or re-engineering later.

Save your engineers from manual data inspection
error detection
Avoid costly and schedule-killing re-testing
catch bad data
Make better decisions with more confidence in your data


"If your model is in your data, Monolith will find it. Built by engineers for engineers, Monolith helps you make better models faster." 
Dr. Ted Duclos, Monolith Advisor and Former CTO at Freudenberg Sealing Technologies


Quickly inspect and validate your data with AI

With the array of tools Monolith provides for exploring and analysing your data, you can quickly identify gaps, outliers, and duplicates in your test data.

Using statistical plots, you can understand distribution of values to find duplicates or weak spots. The wide array of point plots, line plots, box and whisker, and parallel coordinate displays provide fast and intuitive tools to find data issues.   

Data Visualization (Plots)

With Monolith, choose from a variety of mathematical techniques to clean and transform your data to prepare it for training models.  You can filter data or remove missing, duplicate, or outlier values quickly; or apply more advanced transforms to resample, smooth, or derive new values in your data.

When basic transformation methods are not appropriate, you can use models to predict and replace gaps or errors in your data. 

Your data is the foundation for more accurate models, more confident predictions, and more powerful optimisations.  Getting there starts with Monolith's built-in visualisation and transformation libraries to validate your data. 

self learning models converging plot training ai modelling

With Monolith's advanced Anomaly Detector, you can apply machine-learning models to quickly inspect hundreds of channels, look for a wide variety of outliers in the data, as well as find cross-channel relationships that are abnormal. 

The Anomaly Detector produces an anomaly score and interactive heat map for an intuitive approach to rank the tests most likely to have an issue worth investigating.  You define the sensitivity and tolerance for outliers that require your attention. 

The Anomaly Detector was designed, built, and tested on real test data from hundreds of customer projects.  We continually improve and optimize the algorithms and user experience to meet the needs of engineers. 

anomaly detection machine learning monolith ai
Automate Data Inspection
Live Webinar April 9th 16:00 CET | 15:00 GMT


Introducing AI-guided anomaly detection with Monolith


Monolith has developed new tools for inspecting engineering test data in collaboration with select customers. Dr. Joël Henry, Principal Product Engineer, will showcase the new Anomaly Detector in an upcoming webinar. This technology automates error detection in data, sensors, and systems, making it easier and faster for you to identify and resolve issues.
No-Code AI Software


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 
battery health

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.

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. 

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Key use cases 

test plan optimize
Test Plan Optimisation
root-cause analyiss
Root-Cause Analysis 
System calibrtaion
*A commissioned study conducted by Forrester Consulting on behalf of Monolith

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
4 validation cases white paper battery testing
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:
Case Study 


72% test reduction using trustworthy AI models


Monolith uses self-learning models to analyse data efficiently, allowing engineers to understand and predict complex system performance quickly. This enhances their workflows and helps solve challenging physics problems. The solution not only predicted vehicle optimisation outcomes but also identified data labeling inconsistencies, leading to significant time savings on the test track.
kistler case stusy monolith
Learn More About Monolith

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