# 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.