rApps
Networks that see what’s coming
We apply AI to telecom network operations, turning data into the ability to predict, explain and make better-informed decisions.
Our rApps bring advanced intelligence into network automation environments, helping networks evolve from reacting to what has already happened to anticipating what comes next.
Anticipate to optimise
Automation enables networks to react. Predictive intelligence enables them to anticipate
At LAUDE, we develop rApps that combine telecom expertise, data engineering and AI to predict network behaviour, understand the factors behind it and increase confidence in automated decision-making.
The aim is not to bring AI into the network simply because we can. It is to apply it where it helps us anticipate, understand and make better decisions.
Predict
Anticipate demand, consumption and network behaviour.
Explain
Understand which factors are driving each prediction.
Validate
Ensure predictive models remain reliable as network conditions change.
Intelligence built for better network operations
EDFE
Energy Demand Forecasting & Explainability
Anticipate energy demand
EDFE predicts network energy consumption while providing insight into the traffic, configuration and operational conditions driving its evolution.
It helps move energy optimisation from a reactive approach towards proactive planning, anticipating consumption peaks and supporting better-informed decisions.
EDFE combines operational, configuration and energy data, analyses the relationship between traffic and consumption, and provides explainability supported by a private LLM. Its cloud-native architecture also enables it to be delivered as rApp-as-a-Service through APIs.
PI
Prediction Integrity
Know when to trust a prediction
Prediction Integrity continuously monitors predictive models used in network automation, comparing their outputs against actual network behaviour.
It detects drift and performance degradation, determines reliability levels and generates alerts to prevent deteriorating predictions from influencing decisions made by other rApps.
Its model-independent validation logic enables it to monitor heterogeneous predictive models. It also maintains historical records for analysis and audit purposes and incorporates LLM-assisted reasoning within a governed context through MCP.
TiSFE
Time-series Forecasting & Explainability
Anticipate network behaviour
TiSFE forecasts how key network variables will evolve, including traffic, radio resource demand, active users, SNR and energy consumption.
It combines zero-shot time-series forecasting, contextual data and explainability to turn predictions into actionable insight for network planning and optimisation.
TiSFE can also incorporate external covariates — such as weather conditions, work shifts or scheduled events — and explain which variables influenced each prediction and to what extent.
Network Intelligence
NETWORK → PREDICT → OPTIMISE → VALIDATE → BETTER-INFORMED DECISIONS
We work as an independent software vendor across leading network automation platforms, bringing specialist expertise in network intelligence and automation.
Our work with rApps extends across different network automation environments, developing solutions designed to integrate with multiple architectures and technology ecosystems.
We combine AI, telecom expertise and engineering to build new capabilities for prediction, optimisation and control across telecom networks.
Let’s talk
We look at every challenge in its real-world context to determine what’s worth building — and the right way to build it.