Project Name
AI-Powered Network Telemetry Dashboards From Plain English
Our client is a large network operations organisation responsible for monitoring, analysing, and maintaining the performance of a complex, high-scale network infrastructure. The organisation’s Network Operations Centre, VP of Engineering, and SRE leadership teams require continuous real-time visibility into network performance, including throughput trends, latency behaviour, anomaly detection, protocol-level metrics, and SLA compliance status, to maintain operational continuity and respond to performance degradations before they affect end users.
Despite operating a sophisticated network telemetry data infrastructure capable of capturing and storing high-resolution performance metrics in real time, the analytical layer was structurally dependent on specialist Python engineers to translate operational questions into executable analysis code. This introduced delays that were incompatible with the real-time response demands of NOC operations.
The client faced multiple operational and technical barriers that slowed network analysis, delayed decision-making, and reduced incident response efficiency.
- Operational Leaders Lacked Direct Access to Network Insights: NOC Directors, VP Engineering, and SRE Leaders relied on specialist engineers to access and analyze telemetry data, limiting their ability to make timely operational decisions.
- Network Analysis Required Advanced Programming Expertise: Extracting insights from telemetry data required Python expertise, along with deep knowledge of proprietary schemas, libraries, and processing frameworks.
- Specialist Engineer Dependency Delayed Incident Response: Every analytical request had to pass through engineering teams, creating delays in detecting, investigating, and resolving network incidents.
- Complex Schemas Increased Analysis Errors: Domain-specific telemetry schemas and APIs made manual query development error-prone, often resulting in inaccurate visualizations and additional debugging.
- No Self-Service Dashboarding for Live Operations: Operational teams could not create or modify dashboards independently, making it difficult to gain real-time visibility during active incidents.
- High-Volume Telemetry Demanded Optimized Queries: Large-scale telemetry streams required highly efficient query logic, as poorly optimized code caused slow responses, timeouts, and inaccurate results.
- Slow Analytical Iteration, Limited Investigation Depth: Every follow-up question required another engineering request, preventing the rapid, exploratory analysis needed for effective incident management.
Ksolves developed an AI-Powered Network Telemetry Analytics Platform that enables NOC Directors, VP Engineering, and SRE Leaders to ask analytical questions in natural language and instantly receive real-time dashboards.
- Natural Language to Optimized Python Generation: An LLM converts plain-English operational queries into schema-aware, performance-optimized Python code tailored to the client's telemetry data structures and processing framework.
- AI-Powered Code Validation and Safety: Every generated Python script is validated for syntax, schema accuracy, API compatibility, and execution safety before running against production telemetry data.
- Real-Time Analysis Across Live and Historical Data: Validated code executes on both live telemetry streams and historical datasets, enabling real-time monitoring, trend analysis, and incident investigations.
- Automated Dashboard Generation: Query results are automatically transformed into the most suitable visualizations, including trend charts, heat maps, protocol breakdowns, and SLA monitoring dashboards.
- Self-Service Exploratory Analytics: NOC and SRE teams can ask follow-up questions conversationally, enabling rapid drill-down analysis and dashboard updates without engineering assistance.
Technology Stack
| Category | Technology |
|---|---|
| AI / NLP | LLM-Powered NL-to-Python Code Engine |
| Processing | Python Analytics Execution Engine |
| Visualization | Real-Time Performance Dashboard Layer |
| Architecture | Network Telemetry Schema Mapper |
| Infrastructure | High-Scale Telemetry Stream Handler |
| Methodology | AI Code Validation & Safety Layer |
From engineer-dependent network analysis taking hours to AI-powered, self-service dashboards generated in seconds.
- Self-Service Analytics for NOC and SRE Teams: NOC Directors, VP Engineering, and SRE Leaders can independently generate real-time network dashboards without Python expertise, schema knowledge, or engineering support.
- Real-Time Operational Visibility: Performance dashboards are generated in seconds from natural language queries, enabling faster monitoring and decision-making during live network incidents.
- Accurate, Reliable Analytical Output: AI-generated, schema-aware Python code eliminates errors caused by incorrect queries, improving confidence in operational insights and visualizations.
- High-Performance Analytics at Scale: Optimized code delivers fast, dashboard-ready insights across high-volume telemetry streams, ensuring responsive analysis even under heavy workloads.
- Faster Iterative Root Cause Analysis: Teams can ask follow-up questions conversationally and receive updated dashboards in seconds, enabling rapid, end-to-end incident investigations without engineering handoffs.
Ksolves, an AI-first technology company offering AI and ML consulting services, transformed the client’s network analytics workflow by replacing engineer-dependent analysis with an AI-powered, self-service platform. Operational leaders can now generate real-time dashboards, investigate anomalies, and perform root cause analysis using natural language, without Python expertise or engineering support. The result is faster decision-making, improved operational efficiency, and engineering teams freed to focus on innovation instead of routine analytical requests.
Is Your NOC Still Waiting for a Python Engineer to Answer a Question the Data Could Respond to in Seconds?
