For years, network operations have relied on complex query languages that demand specialized knowledge. Extracting insights from network data often meant writing intricate commands in formats like SQL, a skill reserved for seasoned IT professionals. But what if anyone, regardless of expertise, could ask a simple question and get immediate, accurate answers from their network?
That’s precisely what Selector Copilot makes possible. With natural language querying (NLQ), teams can interact with their network the same way they interact with AI-powered chat assistants like ChatGPT or Google Gemini. Instead of struggling with syntax, they can simply ask, “What configuration changes were made in the last 24 hours?” and get a clear, actionable response.
This shift is more than just a convenience. It fundamentally changes how teams troubleshoot, analyze, and manage network operations. It’s the culmination of the layered intelligence we’ve explored throughout this series, starting with harmonized, enriched data, then correlation and context, and finally LLM-powered insights and AI agents. With that foundation in place, Copilot brings it all together in the most human way possible: conversation.
Complex Queries Are Hurting Your IT Efficiency
Network operations generate massive amounts of data, but accessing that data traditionally requires deep technical skill. Traditional query languages like SQL force teams to spend valuable time crafting the right commands instead of focusing on solving problems.
This complexity creates bottlenecks for non-technical stakeholders. Suppose an operations manager needs to check system performance trends or verify whether a config change introduced instability. In that case, they must wait for a network engineer to write the proper query. That delay slows down decision-making and response time. Even within technical teams, only a few choice experts typically know how to extract insights from the data effectively.
Copilot addresses this head-on by making insights universally accessible.
Network Troubleshooting Shouldn’t Require an SQL Expert
Selector Copilot eliminates these barriers by automatically translating natural language into S2QL (Selector Software Query Language) queries. Instead of memorizing syntax, users can type their queries in plain text English, and Selector Copilot converts them into the appropriate commands.
This capability is powered by Natural Language Translation (NLT), which ensures that questions are understood and contextualized based on your network telemetry. Whether troubleshooting a performance issue or analyzing trends, users can get the necessary information without requiring expertise in query languages.
And it’s not just passive responses. Selector’s AI can also explain why something is happening.
Behind the AI: How Selector Copilot Makes Network Data Instantly Accessible
Selector’s Copilot uses a hybrid AI architecture combining Local and Cloud-based Large Language Models (LLMs) to ensure accuracy, performance, and security. Here’s how a natural language query flows through the system:
- A user enters a plain-language query: “Show all failed login attempts in the last 48 hours.”
- Selector’s local LLM converts it to S2QL, ensuring the query follows network-specific syntax.
- The query retrieves relevant data, pulling logs, metrics, and events, and enriched telemetry across the environment.
- The local LLM processes the raw data and organizes it into structured insights.
- An Enterprise-Grade Cloud LLM refines and summarizes the response, providing clear visualizations and recommendations.
This hybrid AI approach ensures that users receive accurate, human-readable insights in seconds, transforming complex network analysis into a seamless, intuitive experience.
Why AI-Powered Cloud Models Deliver Deeper Network Intelligence
While Local LLMs handle query translation and processing, Cloud LLMs take network insights to the next level by refining and visualizing data. These advanced AI models summarize key findings, identify patterns, and recommend next steps.
Beyond intelligence, security remains a top priority. Customer-specific vector stores isolate sensitive network data, while enterprise-grade security measures prevent unauthorized access. Unlike public AI models, enterprise-grade Cloud LLMs like Google Gemini 1.5 Pro do not train on customer queries or responses, eliminating concerns about data leakage.
By combining the speed of local AI processing with the depth of cloud-powered insights, Selector Copilot delivers a best-in-class network intelligence experience.
Faster Answers, Smarter Decisions: The Impact of AI in Network Ops
The ability to interact with network data using natural language improves efficiency and democratizes access to information across entire organizations.
Network engineers can troubleshoot issues faster without spending time on query syntax. IT operations teams can easily identify performance trends and diagnose network slowdowns. Even executives and operations managers can retrieve key performance insights in real time without relying on IT experts.
For example, instead of writing a technical query like:
access_events as log-table where username=michael.hutt group-by device_nameA user can simply ask:
“What devices did Michael Hutt access?”
The result? Faster insights, improved collaboration, and a more efficient approach to network operations.
The Future of Network Operations with AI and Natural Language
AI-driven network management is evolving rapidly, transforming how teams monitor, troubleshoot, and optimize their infrastructure. As networks become more complex, organizations need solutions that reduce operational friction and streamline decision-making. Natural language querying is at the center of this shift.
Soon, AI-powered assistants like Selector Copilot will go beyond answering queries, instead predicting issues before they occur, automating resolutions, and providing proactive insights. Enterprises will move from reactive troubleshooting to AI-assisted network intelligence, where problems are identified and addressed before they impact performance.
Scaling natural language querying across an enterprise requires seamless integration into existing workflows. By embedding AI-powered network intelligence into collaboration tools like Slack, Microsoft Teams, and ITSM platforms, organizations can break down silos and make real-time network insights accessible across teams. The shift toward self-service analytics will empower technical and non-technical users, ensuring that everyone, from engineers to executives, can make informed decisions without relying on a handful of internal experts.
For enterprises looking to stay ahead of the curve, adopting AI-driven natural language querying today is more than a convenience. It is a strategic advantage that will define the next era of network operations.
Your Network Has All The Answers – Now You Can Finally Hear Them
With Selector Copilot, speaking to your network is now a reality. Teams can retrieve insights, diagnose issues, and analyze trends using natural language, eliminating the need for specialized query skills.
This isn’t just an incremental improvement. It’s a fundamental shift in how organizations manage their networks, enabling faster decision-making, better collaboration, and a future-proof approach to network intelligence.
Now is the time to embrace AI-powered network intelligence so your team can focus on solving problems instead of writing queries.
Request a demo today to see how Selector Copilot can simplify network queries and supercharge your network 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. Keep an eye out for this series’s final blog, exploring how today’s AI foundations will power tomorrow’s self-healing, self-optimizing infrastructure.