Background is a machine-learning and artificial intelligence company specialized in advertisement technology.

They developed a software called CORE which analyzes supply/demand data, along with behavioral analytics. With the power of Predictive Bidstream, CORE helps advertisers and media-buyers overcome the industry’s overflow of data and clutter.

Founded by industry experts, Cignal’s technologies elevate the efficiency and transparency of digital advertising markets around the world.

The Challenge

Cignal’s need to supply highly valuable bidding insights in real time demanded continuous querying and processing with machine learning algorithms.

These queries included over 200 data points from multiple demand and supply resources: publishers, advertisers, DSP’s and SSP’s. While scaling-up the operations for a growing client-base, the performance demands became increasingly challenging.

The speed in which the advertising landscape evolves, and the client demands for better and quicker insights meant Cignal’s data processes had to evolve to match the demands of an extremely fast-paced industry.


The Solution

With Rivery, CORE was able to successfully transform large scale amounts of raw data from the marketplace into practical insights for media buying.

The new serverless data pipeline helped orchestrate their data sources, as well as unify their own internal data – which was filed across multiple locations.

Using Snowflake and Rivery in their BI stack, CORE was able to analyze market-wide performance and the ROI of the digital advertising ecosystem.


The Outcome

By aggregating raw data from various sources, CORE is able to analyze insights from a single data platform.

With Rivery’s off-the-shelf data integration tool, CORE was able to connect their data sources without having to develop bespoke integrations.

An automated process of collating data saves the teams precious time. With the advertising landscape changing at lightning speed, having access to performance insights in real-time is a major advantage.

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