Our First rApps on EIAP: Smarter Networks, Greater Trust
We’re excited to announce a major milestone for our team: the launch of our first two rApps within Ericsson’s Intelligent Automation Platform (EIAP) Ecosystem.
Built to support the future of autonomous networks, these rApps mark our entry into the EIAP ecosystem with purpose and precision. They’re designed not just to showcase innovation, but to solve real operational challenges in energy management and AI trustworthiness.
Energy Demand Forecasting and Explainability rApp
Energy costs are a growing concern for operators managing increasingly dense and complex mobile networks. Our Energy Demand Forecasting and Explainability rApp takes a proactive approach to energy optimization. It doesn’t just look at what’s happening—it anticipates what’s coming.
Using advanced time series models trained on historical and real-time network data, this rApp predicts long-term energy demand across the RAN. That means operators can shift from reactive energy-saving tactics to strategic, forecast-driven planning.
But prediction alone isn’t enough. That’s why we integrated Explainable AI (XAI) into the core of the rApp.
XAI peels back the curtain on how and why the model reaches its predictions. It translates complex algorithms into human-readable insights. So instead of black-box results, operators get transparency: which variables influenced a spike in expected demand, why energy use is predicted to rise in a specific sector, or what seasonal patterns are emerging.
The result? More trust in the data, more confidence in decisions, and better alignment between business goals and network operations.
Learn more about our Energy Demand Forecasting and Explainability rApp
Prediction Integrity rApp
As AI-powered automation becomes more embedded in network operations, trust in predictions becomes critical. That’s where our Prediction Integrity rApp steps in.
This rApp monitors the predictions made by other rApps—including our own Energy Demand Forecasting tool—and checks for unexpected behavior, outliers, or patterns that deviate from expected norms. If a prediction doesn’t look right, the rApp raises a flag.
It acts as a second layer of defense, ensuring that any decisions based on AI forecasts are grounded in integrity. Whether it’s a sudden, unexplained shift in energy demand or an unusual forecast from a third-party rApp, the Prediction Integrity rApp doesn’t let it go unnoticed.
This isn’t just about catching errors—it’s about building resilient, self-aware systems that know when to slow down and question their own outputs. In doing so, it enhances the reliability of the network automation layer as a whole.
Learn more about our Prediction Integrity rApp
Why This Matters
This launch represents our vision for responsible, intelligent automation. We’re plugging into a future-ready ecosystem that fosters collaboration, innovation, and scalability. Our rApps are modular, standards-based, and built to integrate seamlessly into the EIAP framework—ensuring interoperability with other rApps and maximizing value across the network.
At the heart of this achievement is our Telco Lab, part of LAUDE’s Innovation Hub, where ideas are transformed into real-world impact. It’s here that we’re redefining what’s possible in the telecom space, blending data science, AI, and engineering excellence to build the networks of tomorrow.
