Rivr works with the power of your team to reduce unanswered bid requests weighing down your programmatic auctions. Our machine learning models dynamically decide on multi-dimensional ways to segment traffic entering the ecosystem.
Expedite auctions, maximize QPS utilization and drive value for your partners at scale.
Getting started with Rivr
1. Request a simulation
We will run our models to create a simulation on your data. Revealing potential impacts of Rivr, such as reduction in costs and increase in revenue.
2. Review results
Form a working group of both business and tech stakeholders to align goals across the organization, this ensures all sides get the most out of Rivr's capabilities.
3. Test in a controlled environment
Take our tech to task by testing it live in production, showing the accuracy of the simulation and experimenting with ideas from the working group.
4. Go live & scale up
Finally, launch into production with 100% confidence on how best to use Rivr and scale up for the best results.
Rivr technology is adaptable
Rivr's traffic shaping and automated QPS management system is a native solution that works in any code environment. It can be tailored to optimize different goals according to geos, apps and/or type of traffic.
Each instance of Rivr is unique with its ML models learning on your infrastructure. This native in-process solution becomes part of your environment, tailored by you, maintained by our tech support team.
Analysis on the audience level
Identifiers like device characteristics, possible impressions and unique ad slots, as well as seasonality and unexpected changes in bidding behaviour per bidder are used to fit the most effective AI models to each division of traffic.
As an in-server solution with unnoticeable response time, failsafe mechanisms, and fresh predictions updated every 15min to leverage fast changing demand and supply.
With the Rivr Library, there are no 2 identical solutions, your unique data and expertise has a direct impact on the performance.
Easy data integration
Rivr trains on data sets and logs that you already have in place. No need to create unique sources for Rivr.
Dynamically manage QPS
Rivr extracts the maximum value from the demand side's QPS limits. Keeping partners happy and increasing the profile of existing inventory by only sending relevant traffic to auctions.