How a global telecommunications provider built a network operational twin and improved root cause analysis
A leading communications service provider partnered with Selector to create an operational twin of its network, enabling faster root cause analysis and improved operational efficiency across a massive, multi-domain infrastructure.
At a glance
Global telecommunications provider
Telecommunications
On-premises
- Build a network operational twin
- Improve root cause analysis
- Enable what-if analysis across control and data planes
- Expand proactive observability and capacity planning
- Operational twin modeling
- Path computation and dependency mapping
- Historical replay / Network DVR
- AI-assisted analysis and summarization
- Control-plane topology visualization
- Routing-aware observability
- Faster troubleshooting
- Better operational visibility
- Safer, non-intrusive analysis
- Stronger planning for network changes and augmentation
- Foundation for predictive, service-aware operations
Challenge
This global telecommunications provider needed a better way to understand network behavior across infrastructure and services, model the impact of routing and topology changes, and investigate issues without depending on direct access to the live production network. Existing approaches made it difficult to correlate signals across the environment, understand service impact, and move from reactive troubleshooting to more proactive operational planning.
Solution
Selector was deployed on-premises to ingest large-scale telemetry, routing, and configuration data from internal systems and create an operational twin of the network. This gave the customer a unified, virtualized view of infrastructure and service behavior, along with support for path computation, historical replay, and AI-assisted analysis, helping teams investigate issues faster and with more context.
Impact
With a more complete model of the network, the organization improved troubleshooting and root cause analysis, enabled safer what-if planning, and laid the groundwork for more advanced observability use cases such as capacity awareness, threshold-based alerting, utilization forecasting, and routing anomaly detection.
Building a more complete view of network behavior
For large service provider environments, visibility alone is not enough. Teams need to understand how routing, topology, infrastructure, and services interact so they can investigate problems faster, assess operational risk more confidently, and make better planning decisions. That was the challenge facing this customer as it worked to strengthen operational insight across a complex network environment.
The customer’s goal was to build more than a monitoring layer. It needed an operational twin that could represent both the control plane and the data plane, support what-if analysis, help teams trace service impact, and make root cause analysis more effective. Over time, that vision expanded to include broader observability needs such as dynamic capacity planning, health monitoring, KPI forecasting, routing-aware triage, and more accessible insight through natural-language querying.
Key challenges
Unified visibility across infrastructure and services
The customer needed a single operational model spanning both the control plane and the data plane
Faster, more contextual root cause analysis
Teams needed stronger correlation across routing, telemetry, and configuration data to reduce time to insight.
Safe, non-intrusive analysis
The organization wanted deep operational visibility without requiring direct access to the live production environment.
A path to more proactive observability
The team wanted to extend visibility into forecasting, threshold-based alerting, anomaly detection, and capacity planning.
From fragmented visibility to operational clarity
Before Selector, the organization lacked a unified model that could connect infrastructure behavior with service impact across the network. Teams needed a way to understand routing paths, dependencies, and control-plane changes in context, rather than piecing together isolated data points from multiple systems. This made it harder to move quickly from signal to explanation, especially when troubleshooting complex issues.
The customer also wanted the ability to perform historical analysis and what-if simulations. They needed to understand how link or node failures could affect services, how prefixes moved through the network, and how control-plane changes might create downstream operational risk. Just as importantly, they wanted to generate these insights in a way that aligned with security and compliance expectations, without requiring direct interaction with the live production environment.
As requirements matured, observability and planning became a bigger part of the story. The team wanted to dynamically understand router and firewall capacity, monitor protocol and interface counts, detect deviations in BGP behavior, track prefixes of interest, and forecast utilization trends. Those needs made it clear that the customer was not just solving for better troubleshooting, but building toward a more proactive, service-aware operational model.
Building an operational twin foundation for smarter operations
Selector was deployed on-premises to ingest exported telemetry, routing, adjacency, and configuration data from internal systems and create an operational twin of the customer’s network. This gave engineering and operations teams a virtualized environment for understanding topology, dependencies, and service behavior at scale, without relying on direct access to live production systems.
On top of that foundation, Selector enabled full path computation across infrastructure and services, control-plane topology rendering, and historical replay for incident forensics and service-impact analysis. These capabilities helped the customer move from isolated troubleshooting toward a more complete and contextual understanding of network behavior.
Selector also supported AI-assisted workflows such as configuration summarization, anomaly detection, and dashboard insight generation. That helped teams work more efficiently from large-scale data and made it easier to turn operational signals into actionable understanding.
As the use case expanded, the same foundation supported a border observability roadmap. The customer could build toward service-aware capacity insight, threshold-based alerting, utilization forecasting, routing-aware issue triage, and natural-language access to network intelligence across planning, engineering, and operations teams.
What Selector enabled
Operational twin modeling
A unified, virtualized representation of the network across infrastructure, services, and routing domains.
Path analysis and dependency mapping
Teams could understand service paths and assess downstream impact more effectively.
Historical replay for investigation
Network DVR capabilities supported incident forensics and service-impact analysis.
AI-assisted operational insight
LLM-enabled analysis supported summarization, anomaly detection, and faster interpretation of large-scale data.
Control-plane visibility
Topology rendering made it easier to visualize routing behavior and network dependencies.
Expansion into observability and planning
The deployment created a foundation for forecasting, capacity awareness, alerting, and routing-aware triage.
Delivering insight without directly touching the live network
One of the most important elements of this story is the operating model behind it. Rather than interacting directly with the live production network, the customer exported data from internal systems to create a high-value operational model. That approach supported internal security and compliance expectations while still enabling meaningful, large-scale analysis.
This mattered because it changed the quality of visibility available to the team. Instead of viewing events in isolation, engineers could understand how telemetry, routing, topology, and services related to one another. That broader context made faster troubleshooting, stronger root cause analysis, and safer planning possible.
It also created a practical path forward. By starting with an operational twin and contextual analysis, the customer established a foundation that it could extend into more proactive observability workflows over time, without needing to rebuild its approach from scratch.
From reactive troubleshooting to proactive operations
The deployment helped the customer improve troubleshooting by giving teams a more complete and contextual view of the environment. Digital Twin modeling, path analysis, and AI-assisted correlation supported faster diagnosis and more effective root cause analysis, particularly when understanding service dependencies and historical behavior was critical.
The solution also improved planning. With what-if simulations and broader visibility into control-plane and service behavior, the organization was better positioned to evaluate the impact of failures, anticipate augmentation needs, and make more informed operational decisions.
Just as importantly, the work established a base for the next phase of operational maturity. The customer now had a platform capable of supporting more advanced observability use cases, including threshold-based alerting, dynamic capacity awareness, utilization forecasting, routing anomaly detection, and natural-language access to operational insight.
Results snapshot
Large-scale environment modeled
Tens of thousands of devices were represented across the operational twin environment.
Enterprise-scale data processing
The platform supported terabytes of recurring data and millions of time-series records.
Near real-time data processing
Teams gained insight quickly enough to support active troubleshooting and planning workflows.
Safe, non-intrusive
analysis
Operational understanding was improved without requiring direct access to the live network.
A foundation for broader observability
The customer created a path toward more predictive, service-aware, and accessible operations
Extending the foundation for capacity planning and service-aware observability
What makes this story especially compelling is that it does not stop with operational twin visibility. The same foundation can be extended into more proactive capabilities, including dynamic capacity awareness for routers and firewalls, threshold-based alerting, forecasting of utilization trends, routing-aware issue triage, and identification of BGP behavior that deviates from expected patterns.
That progression gives the customer a clear operational path: first build a trustworthy model of the network, then use it to improve troubleshooting and planning, and finally extend it into more predictive and accessible forms of observability. For an organization operating at scale, that is the difference between simply collecting data and turning data into operational confidence.