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EnergAIze solution

EnergAIze:  AI-driven network energy efficiency solution

  • 50% reduction in energy consumption and 99.3% accessibility demonstrated in an urban deployment with 270 antennas
  • Integration demonstrated with VMware in Deutsche Telekom’s i14y Lab, and suitable for deployment in any type of fixed/mobile network

Microscope:  AI-driven traffic decomposition

  • Real-time service demand estimation
  • Breaks large traffic aggregates into the service streams active at different locations
  • Lightweight metadata-based, non-intrusive data processing
  • Traffic volume predictions (Netflix, Spotify, etc.) accurate to within 1% of measured data
  • Adaptive solution for intensive traffic analysis
Microscope solution
ForesAIght solution

ForesAIght:  AI-assisted uncertainty and cost-aware forecasting

  • Dedicated neural models learn complex spatiotemporal dependencies
  • Anticipates the capacity needed to accommodate each service-level flow
  • Predicts multiple KPI reflective of user quality of experience
  • Provides operators with supplementary information about the confidence of neural networks' output
  • Configurable forecasting horizons, temporal and geographical granularities
  • 100x lower model complexity than state-of-the art
  • Fast training, superior forecasting accuracy

AIDA:  AI-Driven business Analytics

  • Business intelligence platform for geo-temporal network analysis
  • Identify congested locations
  • Understand service-level deviations from historical patterns
  • Helps shape service offerings and pricing
  • Underpins targeted marketing
AIDA solution
IdentifAI solution

IdentifAI:  AI-driven operational anomaly prediction

  • ML pipeline for predicting anomalous patterns across time series of multiple KPIs specific to communications networks
  • Operates with minute-level granularity on multiple input signals in parallel and produces forecasts with horizons of tens of minutes to hours
  • Unsupervised data labelling that limits time-consuming human expert input for model training
  • Proactive incident resolution improving customer experience
  • Recommendation logic that streamlines network management and reduces OPEX

DeepQoE:  AI-assisted quality of experience prediction

  • Harnesses Net AI‘s DeepQoE technology to deliver the anticipated evolution of KPIs reflective of user quality of experience (QoE)
  • Operates over configurable time horizons with high accuracy
  • Consumes only metadata and does not interfere with user traffic, carrying low complexity and high scalability
  • Provides network operators with supplementary information about the confidence of the neural networks‘ output
DeepQoE solution
xUPscaler solution

xUPscaler:  O-RAN-compliant AI-driven traffic forecasting and resource autoscaling xApp

  • Embeds Net AI‘s proprietary AI models and uses historic and real-time network traffic data to forecast the upcoming traffic volumes at different network elements
  • Provides actionable analytics to the near-RT RIC via an O-RAN-compliant interface
  • AI-driven autoscaling logic produces relative capacity values for gNB Centralized Unit User Plane (gNB-CU-UP) entities, which are used to load-balance traffic between gNB-CU-UPs
  • Enables CSP to easily allocate resources to guarantee SLAs and improve customer quality of experience, while reducing operational costs