Multi-domain network and satellite payload data, normalized at scale.
Continuous learning across constellations, beams and traffic patterns.
Versioning, A/B testing, safe rollout to dApps in production.
Operator dashboards, forecasting and revenue optimization tools.
NIaaS chains nine specialized ML stages. Each stage feeds the next, progressively building from raw observation to autonomous network action.
Multi-domain telemetry collection
PMs ā¢ KPIs ā¢ CMs ā¢ FMs ā¢ Trace ā¢ State
Data ādeep-fakeā completion
Fill gaps from link outages
Correlation across domains
UE ā¢ Cell ā¢ Slice ā¢ Beam ā¢ Group
Normalization + standardization
Prep features for the ML layer
KPI explainability
Random forests + SHAP-style traces
Predictive KPI alerts
LSTM / sequence models
Reinforcement-learned actions
Reward shaping over network state
Generative observability
Validate before rolling out
AI insights in the GUI
Operator dashboards + APIs
‘NIaaS trains the models. The dApp deploys them.’
