Product Introduction
Definition: Net AI is a specialized network intelligence platform that provides cloud-native, plug-in Artificial Intelligence (AI) software designed for the telecommunications industry. Categorized as an AI-driven Network Traffic Analytics and Automation solution, it leverages deep learning and proprietary algorithms to transform how mobile network operators (MNOs) manage, monitor, and optimize their infrastructure.
Core Value Proposition: Net AI exists to empower telcos to transition from traditional "always on" network management to intelligent, demand-aware operations. By integrating its AI software into existing stacks, operators can achieve up to a 50% reduction in energy consumption, significantly lower Operational Expenditure (OPEX) and Capital Expenditure (CAPEX), and enhance service quality through predictive maintenance and granular traffic decomposition.
Main Features
EnergAIze: AI-Driven Network Energy Efficiency: This module utilizes advanced forecasting technology to control the activation of radio and computing resources in real-time. By predicting traffic demand with high precision, EnergAIze deactivates redundant capacity during low-demand periods without degrading user experience, effectively balancing power consumption against network performance.
Microscope: Mobile Network Traffic Decomposition: Microscope is a proprietary technology that disentangles aggregate network metadata into individual service flows. It provides a geotemporal understanding of specific application demands (e.g., streaming vs. gaming) without deep packet inspection, allowing for highly accurate resource allocation and granular traffic analysis.
ForesAIght: Uncertainty and Cost-Aware Forecasting: A highly-scalable forecasting engine designed to predict the evolution of multiple Key Performance Indicators (KPIs) simultaneously. It supports configurable time horizons and geographical granularities, enabling operators to anticipate capacity needs across entire network deployments before bottlenecks occur.
IdentifAI: Operational Anomaly Prediction: Utilizing unsupervised data labeling and recommendation logic, IdentifAI predicts anomalous patterns across time series of various network KPIs. It identifies potential incidents minutes or hours before they impact the end-user, streamlining network management and reducing the need for manual intervention.
xUPscaler: O-RAN-Compliant Resource Autoscaling: Specifically designed for Open RAN (O-RAN) frameworks, this feature produces relative capacity values to load-balance traffic between different computing elements. It automates resource allocation to guarantee Service Level Agreements (SLAs) while minimizing the compute footprint.
DeepQoE: Quality of Experience Prediction: This feature provides short-term, accurate predictions of the individual user’s Quality of Experience (QoE). By monitoring ongoing performance, it triggers dynamic capacity allocation or handovers to ensure customer satisfaction in a cost-effective manner.
AIDA: AI-Driven Business Analytics: A business intelligence platform that translates technical network data into actionable commercial insights. AIDA identifies service-level deviations from historical patterns to help shape service offerings, pricing strategies, and targeted marketing campaigns.
Problems Solved
Pain Point: Excessive Energy Costs and Climate Impact: Traditional networks remain "always on," wasting immense amounts of electricity during low-traffic periods. Net AI addresses this by aligning resource consumption with real-time demand, slashing utility bills and carbon footprints.
Pain Point: High Engineering-Related OPEX: Network engineers often spend significant time on labor-intensive manual re-configurations and reactive troubleshooting. Net AI’s automation logic allows these teams to shift focus toward long-term strategic decisions and proactive network planning.
Pain Point: Service Quality Instability: Unpredicted network failures and traffic congestion lead to customer churn. IdentifAI and DeepQoE solve this by predicting anomalies and user experience drops before they manifest as service outages.
Target Audience: Chief Technology Officers (CTOs) of telecommunications firms, Network Operations Center (NOC) managers, O-RAN infrastructure developers, mobile network engineers, and data analysts within the MNO ecosystem.
Use Cases: Automating radio resource management in 5G networks, optimizing cloud-native network functions (CNFs) in O-RAN environments, and conducting granular market analysis based on service-specific traffic trends.
Unique Advantages
Differentiation: Unlike traditional network management tools that rely on static thresholds, Net AI uses dynamic, real-time AI control logic. It is one of the few solutions capable of maintaining an optimal balance between energy savings and network quality (QoS) simultaneously, as validated by partners like A1 Telekom Austria Group.
Key Innovation: The "Microscope" technology represents a significant technical breakthrough. It allows for the analysis of traffic flowing on top of shared infrastructure with service-level granularity using only metadata, providing insights that were previously impossible to obtain without privacy-invasive and compute-heavy deep packet inspection.
Frequently Asked Questions (FAQ)
How does Net AI reduce telecommunications energy consumption? Net AI uses its EnergAIze module to forecast traffic demand. It then dynamically adjusts the number of active radio and computing resources to match that demand, preventing energy waste from "always on" components and achieving up to 50% energy savings.
Is Net AI compatible with O-RAN (Open RAN) standards? Yes, Net AI’s xUPscaler and Microscope technologies are designed to be O-RAN-compliant. They provide granular insights into service-wise resource demand and enable automated load balancing between different computing elements within an O-RAN framework.
What is the benefit of traffic decomposition in mobile networks? Traffic decomposition, provided by Net AI’s Microscope, allows operators to see exactly which services (like YouTube, Zoom, or gaming) are driving traffic at specific locations and times. This helps in more accurate capacity planning, better placement of network functions, and more fair revenue-sharing schemes on shared infrastructure.
Can Net AI predict network failures before they happen? Yes, the IdentifAI engine uses unsupervised machine learning to analyze KPI patterns and predict anomalies with a horizon of tens of minutes to hours. This allows network operators to address potential issues before they result in service degradation or downtime.
