This Catalyst is empowering self-driving networks with graph AI and digital twins for predictive root cause analysis and Level 4 autonomy. Their solution unlocks the "zero-X" experience - zero wait, zero touch, zero trouble.
Project companies
Introduction
Same fault. Different response.
Not all network failures carry the same business risk. A performance degradation in a revenue-dense cell serving a financial district represents a materially different operational priority than one in a low-traffic residential zone. Yet today's autonomous networks have no mechanism to express or act on that difference.
Isolating contributing factors across RAN, Core, and Cloud remains a manual and siloed process. Every fault enters the same resolution queue regardless of business impact.
Business-Aware GNN-Healing Networks changes this.
The catalyst introduces a three-layer closed-loop architecture:
Business Intent Layer Sequences remediation based on infrastructure business criticality and revenue exposure.
Graph Intelligence Layer A Graph Neural Network powered by a Google Cloud Spanner digital twin traverses cross-domain topology to identify remediation branches from a single service-layer trigger. No alarm is required.
Execution Layer Vendor-agnostic remediation across any network vendor using WNR-wrapped TM Forum Open APIs.
The key innovation is the first Business Intent Extension Model contributed to the TM Forum Intent Ontology (TIO). This provides operators with a standards-based vocabulary to declare which remediation branch executes first when recovery resources are constrained, decoupling business priority from network implementation.
The result: same fault, different response.
Level-4 autonomy across RAN, Core, and Cloud, with the business layer exploring the boundary of Level-5 through declarative, intent-driven prioritization.
Validated with CSP champion data, the catalyst targets:
55% reduction in operational costs
MTTR reduced from hours to minutes
Protection of revenue at risk in critical coverage zones