Advertising
Maximize the impact of your ML models
Whether you’re segmenting audiences, targeting and personalizing ads, or optimizing bid strategies, Dark Matter offers a more cost-effective way to enhance the performance of your ML ranking models. This means higher click-through rates, better conversion rates, and decreased costs per acquisition.
Dark Matter thrives in ad-tech’s complex ecosystem
Data Management Platforms
Enhance audience segmentation and data analysis for better-targeted ads and content.
Demand-Side Platforms
Optimize targeting and segmentation criteria for improved RTB results and increased ROI.
Ad Exchanges
Boost ad inventory matching and RTB decision-making capabilities for higher revenue and customer satisfaction.
Retain control of your data and models
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.
Our partners count on us
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.
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.
