Find out how we achieved SOTA performance with sparse, high dimensional data
Lowering barriers to effective machine learning
Better Predictions
Gain a competitive advantage by capturing complex, non-linear relationships in your data that are otherwise invisible to your model.
Faster Iteration
Streamline model training and iterate faster by giving your model a dense, machine-friendly representation of only what matters for your prediction.
Lower Costs
Cut compute costs and time, freeing up your team’s bandwidth and boosting efficiency with one simple integration.
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.
Securely installs in under 5 minutes
Dark Matter is a surprisingly lightweight solution that slots seamlessly into your data science pipeline with just a few lines of code. Integrates easily on-prem for total privacy or run via cloud API for rapid scalability.
// Import
import ensemblecore as ec
// Authentication
user = ec.User()
user.login(username='USERNAME', password='PASSWORD', token='TOKEN')
Improving predictive power across applications
Dark Matter boosts the productivity of any ML pipeline, reducing training compute and preserving valuable resources. Works regardless of industry, model type, or prediction task — even with limited data.
Example Applications
Forecasting
Price predictions
Supply and demand
Customer churn
Recommendations
Ad placement
Content suggestions
Product personalization
Optimized Training
Reduces compute
Train on limited / sparse data
Backed by:
