December 2, 2019


  • EPICA has integrated more than 250 millions monthly ID’s from clients and partners to allow companies to easily acquire the data needed to build machine learning models.
  • Sophia Data Exchange (SDX) from EPICA is a modern and secure marketplace to access high quality datasets ready to use for Machine Learning.
  • Thanks to the partnership with Microsoft, all the data from SDX will be stored at Azure.

Miami, December 2nd, 2019.

EPICA, the Prediction as a service Platform (PaaS), welcomes Sophia Data Exchange (SDX) as an aid to find and use second and third-party data to activate predictions through machine learning technologies.

SDX by EPICA is an as a service marketplace platform for high-quality ecommerce/retail datasets ready to use for Machine Learning. SDX was designed to address the growing need of the amount of refined data that is required to seize the power of artificial intelligence to activate predictions. The purpose behind this new solution of EPICA is aiming to stop companies from relying solely on historical data and instead start diving into machine learning predictions in order to improve business performance immediately.

EPICA, currently runs predictions on over 250 million monthly personas (ID’s including cookies) - for more than 3 thousands dimensions at service of brands like Samsung, Unilever, CocaCola, Adidas, Avocados From Mexico and multiple Shopify merchants. EPICA is committed to supporting companies to seize the value of their data. In the last 3 years, EPICA has been delivering predictions as a service with high accuracy -an average of over 70% has been obtained under certain conditions with high quality data and specific models-. Today, EPICA announces the public launch of Sophia Data Exchange (SDX), which supports the access to high quality data that allows building better Machine Learning models, leading to better predictions for companies.

Predictions through machine learning and artificial intelligence are surprisingly powerful compared to any other model of forecasting. But they are not magic; to reach high levels of accuracy, artificial intelligence engines - like Sophia by EPICA - need to process large amounts of data. The more data the better predictions. But not all companies have the quality and quantity of data expressed in enough ID’s, data sets, and dimensions. Consequently, there is a huge demand for high quality data from companies that aim to take advantage of predictions.

“Customers have asked us for an easier way to find and integrate diverse data sets into the applications, analytics, and machine learning models they’re running on EPICA. Unfortunately, the access to high quality data is still difficult. That’s why we have built a collaborative platform that integrates our client’s approved second-party data, third-party partners and a powerful API integration with Facebook and other data providers. Now, every client from EPICA has the chance to acquire the data they need in the same as a service model they are already familiar: as a service,” explains CEO of EPICA, Hernan Rodriguez.

With SDX, companies will have the opportunity to access an immense amount of high quality data to get new ID’s (Audience Lift) or to enrich the owned ID’s with new dimensions (Audience Enrichment), both types of data sets are essential to run accurate predictions with machine learning models.

Besides activating better predictions with new access to data, SDX brings peace of mind to professionals in charge of data and takes the weight of coding multiple disparate APIs, managing sensitive billing relationships and/or licensing agreements and updates.

Sophia Data Exchange from EPICA has been designed to remove those hurdles and simplify the data sharing needed to set robust machine learning models able to deliver highly accurate predictions.

EPICA is part of the Microsoft for Startups program which means that all of the technologies developed to serve more powerful predictions are stored in Azure cloud. This ensures the safety of the data and the capacity of integration with servers at scale and the reduction of costs related to data center storage.

Press Contact: Camila Gonzalez -


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