Natural language copilots are one of the most exciting developments in AI for network operations. They allow engineers and operators to query complex environments in plain language rather than memorizing obscure CLI commands or digging through multiple dashboards.
But here’s the truth: a copilot is only as good as the AI behind it. Without a purpose-built network LLM, a copilot can’t deliver the accuracy, context, and speed that real-world IT operations demand. As we discussed in the first post of this series, What Makes a Good Network LLM?, the proper foundation is essential for AI to deliver meaningful results.
In this post, we’ll look at how natural language copilots are already transforming network operations, and why these use cases depend on having a domain-specific network LLM at their core.
What is a Natural Language Copilot in Networking?
A natural language copilot is an AI-driven assistant that lets you interrogate network data through conversational queries. Instead of issuing structured commands like:
show interface status | include Gi0/1… you can ask:
“ˆWhat’s the status of the uplink interface on switch SW-01 in Dallas?”
The copilot then retrieves the relevant telemetry, correlates it with recent events or logs, and responds with a clear, context-aware answer — often with visualizations or suggested next steps.
Selector’s Copilot does this through its Collaboration Service, integrating directly into Slack, Microsoft Teams, or via API. Engineers can ask questions and troubleshoot issues without leaving their workflow.
Use Case 1: Using a Natural Language Copilot for Incident Triage and Root Cause Analysis
Scenario: A branch office is reporting slow application performance
“What’s causing slow application performance in the Denver branch?”
The copilot queries live telemetry, syslogs, and historical events. Selector’s Knowledge Service then correlates:
- SNMP metrics showing rising interface errors on a WAN router
- Syslogs indicating repeated interface flaps
- A configuration change was detected in the last hour
The result: a root cause report with time stamps, affected services, and recommended remediation steps — all surfaced in seconds.
Use Case 2: Using a Natural Language Copilot for Change Validation
Scenario: A planned firmware upgrade is scheduled for a core router.
Instead of manually combing through metrics, an engineer can ask:
“Show me the impact of last night’s firmware update on Router R1.”
The copilot checks pre- and post-change baselines using Selector’s time-series anomaly detection, comparing KPIs like latency, packet loss, and CPU utilization. If anomalies are detected, they’re flagged with associated topology data so the team can see downstream service impact.
Use Case 3: Using a Natural Language Copilot for Performance Troubleshooting
Scenario: A hybrid cloud application is showing inconsistent latency for end users.
Query:
“Where is the latency coming from for our HR portal users in APAC?”
The copilot pulls:
- ThousandEyes measurements for external paths
- NetFlow records to see traffic distribution
- Wireless controller metrics for local office access points
It then combines this with topology-aware correlation to pinpoint whether the issue is the WAN, LAN, or cloud segment — and provides recommended next steps.
Use Case 4: Using a Natural Language Copilot for Proactive Health Checks
Scenario: Instead of waiting for tickets, the NOC runs daily environment checks
Query:
“Summarize today’s network anomalies and their potential impact.”
The copilot uses ML-driven baselines to detect deviations, cluster anomalies by device or site, and rank them by business impact. The result is a digestible daily health summary perfect for morning stand-ups or shift handovers.
Why These Use Cases Require a Network LLM
In each of these examples, the value of the copilot comes from more than just natural language processing. The real magic is in the network LLM’s ability to:
- Ingest and normalize data from hundreds of sources (metrics, logs, events, configs, CMDB)
- Enrich telemetry with context like device roles, dependencies, and service impact
- Correlate across time and topology to identify causes, not just symptoms
A generic AI model might parse the question, but it won’t have the operational knowledge or integrated data needed to give a precise, actionable answer.
The Bottom Line
Natural language copilots are transforming how teams interact with network data, but only when they’re powered by an AI model that understands networking. In the next post of our How AI Changes Network Operations series, we’ll explore why your IT copilot needs context, not just data — and how context separates quick fixes from meaningful, lasting solutions.
Learn more about how Selector’s AIOps platform can transform your IT operations.
To stay up-to-date with the latest news and blog posts from Selector, follow us on LinkedIn or X and subscribe to our YouTube channel.