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Aviz Network Copilot (NCP)
Bring AI Assistance to Your Network Infrastructure
April 8, 2026
Overview
Modern networks generate massive volumes of operational, telemetry, and application data across data centers, cloud, and edge environments. Extracting actionable insights from this data remains a significant challenge for NetOps teams.
Aviz Network Copilot (NCP) is an agentic AI platform that leverages Large Language Models (LLMs) to transform raw network and system data into real-time, actionable intelligence. It introduces a natural language interface for interacting with infrastructure bridging the gap between human intent and network operations.
Built as a private, vendor-agnostic AI solution, NCP enables organizations to modernize NetOps with intelligent automation, deep analytics, and unified visibility across diverse environments.
Key Capabilities
01
AI-Powered Network Operations
- •Natural language interface for querying network state, performance, and inventory
- •AI-assisted troubleshooting and operational workflows
- •Context-aware responses using multi-source data correlation
- •Reduced dependency on CLI and fragmented tools
02
Unified Data Integration
- •NCP integrates and normalizes data across network, security, observability, and data platforms enabling end-to-end visibility and cross-domain intelligence.
Figure 1: Data Connectors integration with Network Copilot
Network Infrastructure
- Cisco Nexus Dashboard Controller (NDFC)
- Arista (CloudVision, EOS-based environments)
- Multi-vendor switches and routers (SONiC, Broadcom-based platforms)
Security Platforms
- Next-Generation Firewalls (NGFWs): Fortinet FortiGate, Palo Alto Networks
- Security telemetry, policy enforcement, and compliance validation
- Integration with security audit workflows
Observability & Analytics
- Elastic (ELK Stack)
- Splunk
- Logs, metrics, and event analytics platforms
Data & Cloud Sources
- Flow data (NetFlow, sFlow), SNMP, gNMI telemetry
- Data warehouses such as Google BigQuery
- Files, configurations, and offline datasets
Outcome :
Unified, correlated insights across network, security, and observability layers
03
Agentic AI Architecture
•Modular AI agents for:
- •Data querying (SQL, DataFrame agents)
- •Knowledge retrieval (KB agents)
- •Use-case-specific workflows (compliance, analytics)
•Central Manager Agent orchestrates all interactions
•Extensible SDK for custom agent development and onboarding
04
Advanced Analytics & Insights
- •Real-time and historical analytics across flows, logs, and configurations
- •Interactive dashboards for platform activity and usage trends
- •Deep offline analysis for logs, configs, and uploaded datasets
- •Correlation across structured and unstructured data sources
05
Scalable Data Connectivity
- •Flexible data connectors for ingestion and forwarding
- •Integration with external data platforms and pipelines
- •Lifecycle management and health monitoring of data connectors
- •Designed for high-scale data environments
06
Private AI Deployment
- •On-prem or air-gapped deployment using NVIDIA GPU infrastructure
- •Full control over data privacy, compliance, and governance
- •Secure integration with enterprise systems
- •Vendor-agnostic across multi-cloud and hybrid environments
07
Adaptive Workflows
- •Iterative, conversational workflows aligned with human reasoning
- •Context-aware interactions across multiple queries
- •Dynamic automation based on real-time insights
- •Personalized experience across different user roles
Use Cases
| Use Case | Description |
|---|---|
| Inventory Insights | Query devices, OS versions, ASICs, and infrastructure metadata |
| NetOps Automation | Interface checks, MTU validation, port utilization, health summaries |
| Security & Compliance | Audit configurations, enforce policies, and validate compliance posture |
| Flow & Traffic Analytics | Identify top talkers, traffic patterns, and protocol-level insights |
| Knowledge-Driven Troubleshooting | Correlate CVEs, bugs, and advisories with live network state |
| Offline Analysis | Analyze logs, configs, and uploaded datasets for deep diagnostics |
Architecture Overview
Aviz Network Copilot follows a modern agentic AI architecture that transforms raw data into actionable intelligence
Figure 2: Architecture overview of Network Copilot
01
Data Sources: Network telemetry, logs, flows, and external systems
02
Knowledge Base: CVEs, PSIRTs, bug databases, and support artifacts
03
Data Ingestion Layer: Normalizes and structures incoming data
04
Manager Agent: Central orchestration engine for user queries
05
Specialized Agents: Perform domain-specific analysis and processing
06
LLM Layer: Generates contextual, natural language responses
07
Integration Layer: Connects outputs to enterprise tools and workflows
Deployment Options
On-Prem Deployment
- Ubuntu 22.04, Docker
- NVIDIA CUDA toolkit
- GPU-enabled infrastructure (RTX / A-series / V100)
Cloud Deployment
- GPU-enabled instances (T4, A10, or equivalent)
- Same software stack as on-prem
- Elastic scalability and flexible deployment models
Benefits
01
Accelerated Troubleshooting
Reduce mean time to resolution from hours to minutes
02
Operational Efficiency
Minimize manual effort and tool fragmentation
03
Unified Visibility
Single interface across network, security, and observability domains
04
Scalable Analytics
Process and analyze large datasets efficiently
05
Secure AI Adoption
Private, controlled deployment with full data ownership
Conclusion
Aviz Network Copilot redefines network operations by combining AI-driven intelligence, unified data integration, and agentic automation into a single platform. It empowers organizations to transition from reactive operations to proactive, insight-driven NetOps.
With its ability to seamlessly integrate across network, security, and observability ecosystems, NCP provides a scalable and future-ready foundation for modern infrastructure management.