Silverthread leverages cutting edge technology to bring you the most accurate insights into your business. 


What is Machine Learning?

Machine learning is the process of teaching a computer program through data, rather than explicitly programming changes into the machine. Silverthread uses supervised machine learning which trains algorithms by telling them what to look for - but not how to find it. The machine recognizes patterns from complex datasets and creates a predictive model. New data is then fed into this model and a prediction is made from the output.  As new data is added to the algorithms, the predictive mechanisms become sharper and sharper - ensuring that the predictions of your dataset are more accurate than ever before. 


How does Silverthread use Machine Learning?

Silverthread’s CodeMRI® Platform uses machine learning to create predictive algorithms that assist in large scale decision making. Our systems scan proprietary codebases and determine from the design and code quality architectures the schedule and ROI of codebase refactoring. 


How can Machine Learning Insights Help You Make Better Business Decisions?

Making expensive choices, such as the choice to refactor your codebase, is daunting. The most valuable insight in making business decisions is the ability to see into the future. Using machine learning, Silverthread is able to get closer than ever before. Be able to confidently defend your proposals and changes, and commit with confidence. 

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Outdated software with poor design quality is expensive to maintain. Non-hierarchical code structures create cyclicality in codebases, which leads to difficult to trace bugs and hard to decipher dependencies. This complex code leads to long development times, confusing structures, and increasingly difficult ramp-up for new developers. Slow development is costly and often leads to missed deadlines, disappointing customers and leading to lost revenue. Silverthread saves money in faster development times and increased features and releases.



Silverthread’s technical health improvement plans reduce high code quality complexity. The removal of cyclical structures and hierarchy-breaking code leads to decreased bug releases and the removal of critical cores, areas of high complexity in a codebase that unnecessarily complicate development and runtimes. This allows developers to spend time creating features, rather than hunting down bugs.


Silverthread frees up resources to create more value out of your investments. Create new products faster, with fewer bugs and a more reliable release schedule, so your customers can rely on you to deliver their needed updates faster than competitors.

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Deidentified Consumer Data

The best comparisons are to real, working codebases, not conceptual academic models. Silverthread uses deidentified data from past customers that have opted-in to create a cutting-edge, current models. 

6000+ codebase dataset

The database that trains our machine learning model contains over 6,000 codebases in various sizes, architectural quality, and economic factors. This database trains our model to recognize patterns in a large variety of different codebases.

Statistical Model

Predictive algorithm

Through iterative retraining we refine our statistical model into a dynamic predictive algorithm. 

CodeMRI scan results

Our CodeMRI Platform scans your codebase and inputs the architectural and code quality data into our model.

Task tracking

Task tracking keeps a record of whether each code change was used to fix a bug or add a feature. This affects input such as time to create a feature, time spent fixing bugs, and developer time wasted in complex cores that could be eliminated through refactoring.

Version control

Version control systems gather data on where changes are made in the codebase during both normal development and refactoring. This information is used to further improve the algorithm to focus specifically on high-traffic lines of code.

Economic model and scenarios

Using all of these inputs, Silverthread creates detailed economic models and scenarios that provide unprecedented insight into potential business choices. Use our models to make educated decisions and provide executives with concrete schedules and costs of each option.

Technical outcomes


Economic outcomes

Refactoring and maintenance COO

Cost of ownership comparisons between refactoring a codebase, rewriting it completely, or leaving it as is. This cost of ownership projection additionally factors in the time and risk factors behind varied bug production as a result of various refactoring initiatives. 

Scheduling and ROI insights