Instantly efficient training & inference for multimodal AI
Best-in-class multimodal embedding models for models built to reduce model size & cost
Massively reduce parameter count for any AI model
By learning how to extract and represent complex relationships in your existing data, Dark Matter improves model performance and speeds up training without extensive feature engineering or resource-intensive deep learning — enabling data scientists to spend less time on data and more time solving hard problems.
Case Studies
Customer Conversion
Dark Matter significantly improved model precision and f1 scores in predicting customer conversion in the online retail space.
Cancer Kinase Inhibitors
Model performance metrics improved across the board when trained on an optimized embedding learned from a sparse, high dimensional data set.Customer Churn
Training XGBoost on a better representation of the data improved predictions of customer churn in the banking industry.
Massively reduce parameter count for any AI model
'Model Shrinking' as a Service
Upload any AI model, and Dark Matter will generate a reduced-parameter version with the same benchmark performance—at a fraction of the size.
Fully Multimodal
Dark Matter is compatible with any model and any data modality — even multimodal approaches.
Preserve Performance
Model accuracy and performance remain uncompromised—we guarantee that our smaller models match the original’s performance.
Optimize training and inference across tasks
Forecasting
Price predictions
Supply and demand
Customer churn
Recommendations
Ad placement
Content suggestions
Product personalization
Specialized Tasks
Chemical discovery
Sensor data
Virus-host interactions
Backed by:
Mark Nelson
Former CEO of Tableau
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.