Learned data enrichment for ML

Distill signal from noise to improve model performance with our net-new machine learning algorithm, Dark Matter.
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See you at ICML!

We’re excited to see other cutting edge ML research in Vienna this July. Come say hi!

Dark Matter improves model performance,
longevity in production, and compute efficiency.

By revealing the unseen relationships within your data, our proprietary algorithm delivers a more cost-effective way to train more accurate, longer-lasting models. 
Want to dive into the technical details? Check out our research.

Enhance your pipeline, no matter your model or domain.

Slots in Seamlessly

Surprisingly lightweight, Dark Matter represents a transformative new step in the data science pipeline that doesn’t alter your existing processes.

Domain and Model Agnostic

We make any model in any domain better simply by creating richer representations of the relationships in your existing data.

Secure Integration

Integration is available on-premises or via cloud API. Retain total control of your pipeline, keeping the privacy and integrity of your data intact.

Dark Matter improves model performance across prediction tasks


Price predictions
Supply and demand
Customer churn


Ad placement
Content suggestions
Product personalization

Optimized Training

Reduces compute
Train on limited / sparse data

Example Industries

Seamlessly slots into your existing ML pipeline.

Dark Matter is a surprisingly lightweight solution that integrates seamlessly into your pipeline — either on-prem or via cloud API. So you maintain end-to-end control of your ML process, data, and models.

Securely installs in under 5 minutes

					// Import
import ensemblecore as ec

// Authentication
user = ec.User()
user.login(username='USERNAME', password='PASSWORD', token='TOKEN')

Ready for better model performance?

Get in touch to learn more and book a demo. 

Backed by:


Mark Nelson
Venture Partner at Madrona
Former CEO of Tableau
“Creating the best model most efficiently is what every data scientist strives for. Ensemble offers a novel method of achieving this goal by finding features in your data you didn’t know existed — making more accurate modeling faster and easier.”


Dive into our cutting-edge research.

Feature Enhancement: A New Approach for Representation Learning (Whitepaper)

Discover a novel approach to representing complex, non-linear relationships inherent in real-world data.

Feature Programming for Multivariate Time Series Prediction (ICML)

Learn about a new framework for automated feature engineering from noisy time series data.


Op-eds and thoughts on the state of machine learning and AI
Developer support, API docs, quick-start guide
Published Research
Ensemble research, papers, and conferences

Frequently Asked Questions

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. 

Join the Waitlist

Early Access Form