Improve ML prediction of material properties, chip design, maintenance, and more

Dark Matter is available for on-premises installation on your machine and using your compute resources, enabling you to retain complete control over your proprietary data. On-prem deployment ensures that we never see your data.

We encourage you to try Dark Matter with your data and model to compare the results with your existing pipeline. Most customers use it in a testing environment with sample data to minimize resource requirements before putting it into production. If you’d like to set up a trial, please fill out the form here and we will be in touch.

While Dark Matter does create new variables, its mechanics are fundamentally different. Traditional synthetic data recreates existing distributions from Gaussian noise, so no new information is created. This has the virtue of anonymizing data (which is essential for some regulated industries), but it has minimal impact on predictive accuracy as it mirrors the statistical properties of your data.

In contrast, Dark Matter learns how to create embeddings that have different statistical properties and distributions. Using our new machine learning algorithm, it’s able to converge on nearly orthogonal features that measurably improve predictive accuracy.

One of the primary benefits of Dark Matter is that it lowers the barrier to useful predictive performance by creating richer representations of your data. That said, there is a theoretical minimum threshold of data quality and volume that can be useful (i.e. if what you’re working with is mostly noise, it probably won’t help). Our rule of thumb is that if you have a working data science pipeline that’s generating mediocre predictions, Dark Matter can improve its performance.