Exciting Announcement! In celebration of launching our AI Certification, we’re thrilled to offer a 50% discount exclusively. Seize this unique chance—don’t let it slip by!
AI is transforming networks, and networks are fueling the AI boom. At NVIDIA’s GTC event, Aviz Networks and NVIDIA met to discuss the next evolution of AI-driven infrastructure and the seamless integration of NVIDIA Spectrum-X with Aviz ONES. This panel, moderated by Matt Palmer (CEO, SDxCentral), brought together industry leaders Amit Katz (VP of Networking, NVIDIA) and Thomas Scheibe (CPO, Aviz Networks) to explore AI’s transformative impact on networking and the groundbreaking collaboration between the two companies.
AI for Networks & Networks for AI: A Paradigm Shift
The discussion kicked off with a deep dive into the symbiotic relationship between AI and networking. Amit Katz highlighted how AI Data Centers differ from traditional infrastructures, requiring a complete overhaul of validation, deployment, and management processes. AI inference, in particular, is reshaping how networks handle data movement, requiring highly optimized east-west GPU communication and north-south storage connectivity.
Thomas Scheibe reinforced this, explaining how enterprises are rethinking their network architectures to cater to AI workloads. Aviz Networks focuses on AI-driven solutions that assist network operators in migrating from legacy architectures to AI-ready data centers. AI for Networks enables automation, orchestration, and optimization of network operations, while Networks for AI ensures high-performance, low-latency infrastructure for modern AI workloads.
Aviz ONES & NVIDIA Spectrum-X: A Powerful Integration
One of the most anticipated discussions of the panel was the integration of NVIDIA Spectrum-X with Aviz ONES, a game-changer for AI networking. Amit Katz emphasized that while NVIDIA accelerates networking performance, real-world AI data centers need much more—multi-tenancy support, observability, orchestration, and enterprise integration. Aviz ONES fills this gap by providing:
1. Orchestration & Telemetry: Seamless integration with enterprise tools, ticketing systems, and observability platforms.
3. Scalability & Multi-Vendor Support: Ensuring that AI data centers can operate across different network environments, from AI clusters to traditional enterprise networks.
Thomas Scheibe further explained that Aviz ONES offers tools for network operators to validate NVIDIA Spectrum-X configurations, automate deployments, and integrate seamlessly into existing CI/CD pipelines. This results in higher quality day 0, 1, and 2 operations, reducing complexity and accelerating time-to-value for AI data centers.
AI Assistants Transforming Network Operations
As AI assistants become mainstream, the role of AI in networking is evolving rapidly. Aviz’s Network CopilotTM, powered by NVIDIA GPUs, introduces AI-driven automation for network operators. This AI-powered assistant simplifies troubleshooting, compliance auditing, and real-time alert analysis.
Amit Katz emphasized that AI for networking must be private, secure, and integrated seamlessly across different infrastructures. NVIDIA’s vision of sovereign AI ensures that organizations retain full control over their AI-driven network management solutions, aligning with Aviz’s mission to provide adaptable and future-proof AI networking tools.
The Road Ahead for AI Networking
As the panel wrapped up, both Amit and Thomas shared their vision for the future. AI is evolving at an unprecedented pace, and AI networking must keep up with the growing demands of training, inference, and storage acceleration. While AI-driven networking tools continue to improve, the key to success lies in collaboration across the ecosystem—bringing together best-in-class hardware, software, and automation.
With Aviz ONES and NVIDIA Spectrum-X, enterprises can now confidently build and operate AI data centers with best-in-class networking solutions that ensure performance, scalability, and seamless management.
Catch the panel session here and stay tuned for more updates from Aviz Networks and NVIDIA on how AI is shaping the future of networking!
AI is transforming every industry—including networking. As AI workloads scale, the infrastructure powering them must evolve. Networks must become smarter, faster, and more efficient to support the next wave of AI-driven innovation. At Aviz Networks, we believe in Networks for AI and AI for Networks—and we’re making it happen.
That’s why I’m thrilled to invite you to an exclusive panel at NVIDIA GTC, where we’ll explore NVIDIA’s role in transforming networking across these two dimensions and how Aviz Networks complements this ecosystem with innovative products.
Panel: Network Modernization in the Age of AI
AI-driven workloads demand a new era of networking—one that redefines how we design, deploy, and optimize infrastructure for peak performance. Our discussion will cover:
Networks for AI – Discover cutting-edge architectures designed to deliver high-performance, lossless networking for AI workloads
AI for Networks – See how AI-driven assistants like Network Copilot™ are redefining network operations with automation and real-time insights.
Aviz ONES + NVIDIA Spectrum™-X – Explore the next-gen AI networking solution built for Ethernet-based GPU cloud infrastructures.
Why This Matters
AI is no longer just an application—it’s the backbone of modern enterprise infrastructure. But here’s the challenge: AI workloads are hungry for bandwidth, require extreme precision, and demand real-time optimization. Aviz Networks and NVIDIA are solving this with cutting-edge AI networking innovations.
Don’t just read about AI in networking—experience it. Watch our exclusive demo and join the panel discussion at NVIDIA GTC!
We are thrilled to unveil Aviz Network Copilot™ v1.1.0, packed with innovative features and enhancements. This cutting-edge AI-driven network analysis tool is crafted to help network operators, executives, and stakeholders pinpoint performance bottlenecks and optimize resource utilization via a highly intuitive chat interface.Prepare to dive into the capabilities of Network Copilot™ and explore how its latest updates can significantly improve your network operations’ efficiency.
Data Ingestion Capabilities
Network Copilot™ has expanded its capabilities to include device telemetry ingestion through gNMI, sFlow, and API, in addition to the existing SNMP & Aviz ONES telemetry agent. These enhancements broaden its compatibility across multi-vendor NOS using SNMP, gNMI, and other protocols, facilitating effortless multi-vendor deployments with Network Copilot. The metrics gathered from on-premises networks now encompass Inventory, System Resources, Platform Health, Counters, and Flow Records
Use Cases
In this release, We have introduced several new use cases to provide more comprehensive insights and visibility into your network.
1. Network Upgrade Compliance
Network upgrades are inevitable and crucial for any network to ensure security, performance and reliability. Network Copilot can now assist the Network Administrators with the repetitive task of verifying network upgrades and ensuring the network is restored to its original state post-upgrade, even in a multi-vendor environment.
2. Security Audit Reports
Maintaining IT compliance is crucial for any organization and regular security audits play a vital role in this process. Network Copilot can assist you in ensuring your configuration and security protocols meet the required standards. With Network Copilot, you can verify various aspects like ACL, SSH, TACACS, RADIUS+ & Control plane policing etc..
3. Outbound Network Performance Monitoring
Network Copilot can help you monitor your outbound network performance by analysing network traffic, examining QOS and monitoring bandwidth, it provides insights into your SD-WAN & MPLS networks. This ensures you have a clear understanding of your network’s performance, enabling you to optimize and maintain efficient network operations.
4. Application Visibility and Real-time Network Insights
Network Copilot can analyze the network traffic to identify the applications generating the traffic and its purpose. Utilizing flow data such as sFlow and NetFlow, Network Copilot monitors and analyzes traffic patterns and application behavior, providing detailed insights into the data traffic.
5. Network Troubleshooting
Network Copilot can help Network Administrators effectively diagnose network issues by analyzing past traffic patterns and drops networks.
6. Enhanced Japanese Support
Network Copilot now has enhanced support for the Japanese language.
UI Enhancements
To enhance user-friendliness and simplify interactions with Network Copilot UI, the following improvements have been made:
Streaming responses
Exporting conversations
Import user context
Timestamps
Regenerating responses
User feedback on the responses for improvements
Dark and Light Mode
In conclusion, Aviz Network CopilotTM v1.1.0 brings a suite of exciting new features designed to revolutionize the way to monitor and analyze your network. With its advanced generative AI assistant, the latest release enhances network operations through a user-friendly chat interface, offering capabilities such as dark mode for visual comfort, expanded telemetry ingestion methods for seamless multi-vendor deployment, and new use cases to ensure network upgrade compliance, security audit standards, monitoring outbound network performance, application visibility, and real-time network insights. Network Copilot v1.1.0 is poised to set a new standard in network monitoring & analysis, empowering network operators, executives, and stakeholders to optimize and maintain efficient network operations effortlessly.
Contact us today because with Network Copilot™ v1.1, you’re not just upgrading your software — you’re transforming your network infrastructure strategy to be smarter, faster, and more reliable
FAQs
1. What new telemetry ingestion methods are supported in Network Copilot™ v1.1?
Network Copilot™ v1.1 now supports telemetry ingestion through:
gNMI
sFlow
API integrations in addition to the existing SNMP and ONES Telemetry Agent, making multi-vendor network deployments more seamless than ever.
2. How does Network Copilot™ assist with network upgrade compliance?
After a network upgrade, Copilot:
Verifies configuration restoration
Ensures network consistency
Works across multi-vendor environments This helps administrators quickly validate upgrades and minimize downtime risks.
3. Can Network Copilot™ generate security audit reports? What aspects does it check?
Network Copilot™ now assists in security audits by checking:
Access Control Lists (ACLs)
SSH configurations
TACACS and RADIUS+ settings
Control plane policing policies Ensuring your network meets IT compliance and security standards efficiently.
4. How does Network Copilot™ provide real-time application visibility and traffic insights?
Using flow data like sFlow and NetFlow, Copilot:
Analyzes traffic patterns
Identifies applications generating traffic
Delivers real-time insights into usage trends This allows administrators to optimize bandwidth and secure application flows proactively.
5. What UI enhancements have been introduced in Network Copilot™ v1.1?
Network Copilot™ v1.1 offers a more intuitive interface with features like:
Streaming responses
Exporting conversations
Timestamps on replies
Dark and Light Mode options
User feedback system for continuous improvement These updates make the AI experience faster, smoother, and visually comfortable.
Introduction to AI-TRiSM (Trust, Risk & Security Management)
As AI reshapes the world, its transformative power drives revolutionary innovations across every sector. The benefits are immense, offering businesses a competitive edge and optimizing operations. However, to harness this potential responsibly, we must prioritize ethical and trustworthy AI development and usage.
This is where the concept of AI-TRiSM, a framework conceptualized by Gartner, emerges as a cornerstone for responsible AI development. It emphasizes three crucial concepts: Trust, Risk, and Security Management (TRiSM) in AI systems. By focusing on these key principles, AI TRiSM aims to build user confidence and ensure ethical and responsible use of technology that impacts everyone.
The Framework of AI TRISM
By embracing AI TRISM, organizations can navigate the exciting world of AI with confidence, maximizing its benefits while ensuring responsible and ethical use.
1. AI Trust: This cornerstone emphasizes transparency and explainability. By ensuring AI models provide clear explanations for their decisions, users gain confidence and trust in the technology.
2. AI Risk: This pillar focuses on mitigating potential risks associated with AI. By implementing strict governance practices, organizations can manage risks during development, deployment, and operation stages, ensuring compliance and integrity.
3. AI Security Management: This crucial aspect focuses on safeguarding AI models from unauthorized access, manipulation, and misuse. By integrating security measures throughout the entire AI lifecycle, organizations can protect their models, maintain data privacy, and foster operational stability.
4 Pillars of AI TRiSM Framework
This framework relies on five key pillars to ensure responsible and ethical implementation of AI:
1. Explainability and Model Monitoring: This pillar combines two crucial elements. Explainability ensures transparency in how AI models arrive at their decisions, building user trust and facilitating performance improvement. Model monitoring involves continuously tracking the model's behavior, identifying any biases or issues impacting its accuracy and effectiveness.
2. ModelOps: This pillar focuses on establishing a well-defined lifecycle management for AI models. It encompasses the entire journey, from development and deployment to ongoing monitoring, maintenance, and updating. Robust ModelOps practices ensure the continued reliability and effectiveness of AI models over time.
3. AI Application Security: This pillar safeguards the integrity and functionality of AI models and their applications. It involves implementing security measures throughout the AI lifecycle to protect against unauthorized access, manipulation, and misuse. This ensures the reliability of the model's outputs and protects sensitive data.
4. Privacy: This pillar emphasizes the responsible handling of data used in AI models. It involves ensuring compliance with relevant data protection regulations and implementing appropriate security controls. By prioritizing data privacy, organizations can build user trust, minimize the risk of data breaches, and ensure responsible AI development and deployment.
Adopting AI TRiSM Methodology for Network CopilotTM
Network Copilot transcends the typical tech offering. It’s a conversational AI crafted to meet the complex demands of modern network infrastructures. Its design is LLM agnostic, ensuring seamless integration without disrupting your current systems, and doesn’t demand a PhD in data science to get started. Engineered with enterprise-grade compliance at its core, it offers not just power but also reliability and security.
Dive deeper today because with Network Copilot™, you’re getting seamless integration, enterprise-grade reliability, and enhanced security—all with ease
1. Documentation of AI Model and Monitoring:
To ensure the successful use and management of the Aviz Network Copilot, comprehensive and up-to-date user manuals, technical documentation, and training materials are created. This includes detailed documentation explaining how the AI model makes decisions. Additionally, a clear privacy statement is provided guaranteeing that Network Copilot will not access, transfer, or manipulate sensitive information such as passwords.
2. Well-defined Life Cycle Management:
A well-defined life cycle management process is established for the Aviz Network Copilot product, encompassing all stages from its Building to Deployment. This process will involve defining clear criteria for use case identification, dataset identification, model training, Model selection, Model deploy, Monitor and re-train, a communication plan in place to effectively inform users about any product changes and updates.
3. System Checks and Bias Balancing:
To mitigate potential biases in the Aviz Network Copilot product, regular system checks are conducted such as Gaurdrails. This involves testing the product with diverse datasets and user groups to identify and address any bias that may arise. A bias mitigation strategy is developed and implemented, incorporating techniques such as data normalization, algorithm adjustments, or fairness checks. Furthermore, the performance of the Aviz Network Copilot product is continuously monitored to identify any emerging biases or fairness issues.
4. Responsible Handling of Data:
To ensure responsible data handling practices, robust security measures are implemented for the Aviz Network Copilot product. These measures include encryption, access controls, and regular security audits. Furthermore, clear guidelines are established to define how user data will be collected, stored, used, and shared. Additionally, informed consent will be obtained from users before collecting and using their data. Finally, users will be provided with clear and accessible information about their data privacy rights and how they can exercise those rights.
Conclusion
Aviz Network Copilot prioritizes responsible AI practices through its commitment to the AI TRiSM framework, emphasizing Trust, Risk, and Security Management (TRiSM) throughout its lifecycle. This ensures transparency by providing clear explanations for the AI model’s decisions, fostering user trust. The potential for bias is mitigated through regular system checks and a dedicated bias mitigation strategy. Additionally, robust security measures safeguard the model and user data, further demonstrating Aviz Network Copilot’s commitment to responsible AI development and user confidence.
1. What is AI TRiSM and why is it important for responsible AI in networking?
AI TRiSM (Trust, Risk, and Security Management) is a framework that ensures AI models are developed ethically and responsibly. It builds user trust, manages risks, and ensures security throughout the AI lifecycle—especially crucial for AI solutions like Network Copilot in critical infrastructure environments.
2. How does Aviz Network Copilot implement AI TRiSM principles?
Aviz Network Copilot follows AI TRiSM by:
Documenting AI model behavior
Monitoring model performance and bias
Managing lifecycle from training to deployment
Protecting user data with strong encryption and consent-based practices This ensures a secure, transparent, and trustworthy AI solution for network management.
3. What security measures are in place to protect user data in Network Copilot?
Network Copilot uses:
End-to-end encryption
Strict access controls
Regular security audits Users’ data is collected only with informed consent, ensuring full compliance with privacy best practices and regulatory standards.
4. How does Network Copilot address AI model bias and fairness?
Aviz Network Copilot runs:
Regular system checks
Guardrails testing with diverse datasets
Bias mitigation techniques like normalization and fairness algorithms This keeps the AI model balanced, inclusive, and accurate across different network environments and user groups.
5. Why should enterprises choose an AI-driven network assistant that follows AI TRiSM guidelines?
Choosing a TRiSM-compliant AI like Network Copilot™ means:
Greater trust in AI-driven decisions
Reduced risk of compliance breaches
Stronger data protection and security resilience
Future-proofing network operations with ethical, explainable AI innovation
AI utilized in network management involves the deployment of artificial intelligence methodologies like machine learning, deep learning, and natural language processing. Its objective is to enhance the effectiveness, dependability, and security of any network. Aviz Network Copilot 1.0 serves as an AI-driven network analysis tool designed to assist network operators in identifying performance bottlenecks and resource utilization challenges within their networks. By leveraging natural language prompts, operators can easily interact with the tool to gain insights into network performance metrics and effectively address any issues that may arise. This intuitive approach enhances the efficiency of network monitoring and troubleshooting, enabling operators to maintain optimal network performance and reliability.
Capabilities:
Data ingestion and Storage:
It entails the procedures associated with gathering and housing data from SNMP and ONES Collector, which includes metric data collection from EOS, SONiC, Cumulus and NXOS. This encompasses acquiring, transforming, and preserving data in an organized format to facilitate subsequent analysis or utilization. Standardized data collection makes Network Copilot ready for multi-vendor on-prem deployments.
Figure-1 : Device inventory
Chat Prompt:
Users will receive a sample question to start a conversation in a chat-based interaction. This question prompts participants to respond, steering the conversation. Additionally, the chat history can be exported using this prompt.
The end user can encounter a continuous stream of data or information as a response to the question via streaming.
Figure-2 : Network Copilot homepage
Figure-3 : Network Copilot Chat window
Import Context:
In the model, network compliance is defined based on user preferences. Threshold values are adjustable to meet individual customer needs. The context can be imported and controlled through the RAG in chromaDB.
Figure-4 : Network Compliance modification
The model offers a content template for upload. Users can download it, make edits as needed, save the changes, and then upload the modified file.
Multi-Language support:
Aviz Network Copilot 1.0 will facilitate user interaction with the model in both English and Japanese languages initially. However, the capability to support additional languages can be improved based on specific requirements as needed.
Figure-5 : Conversation in English
Figure-6 : Conversation in Japanese
Analytics
Graphical representation of data using Pie Charts, Bar Charts, and Timeline Graphs to convey insights, trends, and patterns more effectively. The model also facilitates summarizing, describing, and analyzing the gathered data, while also performing computations to determine averages, counts, percentages, and more. This aids in achieving greater clarity and comprehension of your network.
Figure-7 : Pie Chart representation of inventory
Figure-8 : Line Chart representing network utilization over time
Figure-9 : Example shows average cpu utilization for past 3 months
Security:
Network Copilot 1.0 ensures the security of the customer data through secure certificates(HTTPS) and the Secure API.
The secure APIs facilitate the interaction between the Large language model and Database, allowing data exchange, task execution, and information retrieval.
LLMs are empowered to operate autonomously, utilizing custom-built tools to perform designated functions efficiently and effectively.
Network Copilot - Use case:
Inventory and Accounting:
Capturing comprehensive network device information, including hostname, HWSKU, operating system version, interface details, its overall capacity and also the device uptime. This helps administrators to maintain inventory records and account for network assets effectively.
Figure-10: Snapshot of model responding the device details from inventory
Capacity Planning:
This simplifies the administrator’s task by forecasting network capacity details through a comparison of available bandwidth with utilized bandwidth. It also assists network operators in designing the infrastructure required to support current and future network demands effectively.
Figure-11 : Model projecting the overall capacity and utilization of past
Anomaly Detection:
Network Copilot 1.0 is additionally trained to identify network failures resulting from sudden increases in traffic. Such spikes in CPU and memory utilization can lead to failures in various components, including control planes or links within the network infrastructure. By recognizing these patterns, Network Copilot 1.0 can help mitigate potential disruptions and proactively address issues before they escalate, thereby enhancing network stability and performance.
Figure-12 : Network Copilot detecting the peak usage of traffic
Figure-13 : Model responded with HWSKU details which has peak network usage on last 3 months
Network Compliance:
The model comes pre-configured with default compliance thresholds that establish limits for all relevant metrics captured. Network Copilot is equipped to assess whether CPU, memory, bandwidth utilization, and network packet drops comply with these thresholds by comparing observed values with the predefined limits. These thresholds are customizable by users, allowing them to be adjusted as needed.
Figure-14 : Compliance supported on Network Copilot
Conclusion:
Aviz Network Copilot 1.0 leverages cutting-edge AI capabilities facilitated by large language models. Rather than relying on conventional methods for network access, users can engage with Network Copilot to assess various aspects of their network. This includes planning network capacity, identifying instances of network failure, and verifying compliance with predefined configurations and standards. Network Copilot offers an intuitive and efficient alternative to traditional network management approaches, empowering users to gain insights and make informed decisions regarding their network infrastructure.
Unlock the Network Copilot 1.0 experience—schedule a demo on your preferred date, and let us show you how it’s done!
FAQs
1. What is Aviz Network Copilot 1.0 and how does it assist in network management?
Aviz Network Copilot 1.0 is an AI-powered network assistant that uses natural language prompts and machine learning to help operators monitor, analyze, and optimize network performance. It assists in tasks like anomaly detection, capacity planning, inventory tracking, and network compliance, making network management more intuitive and efficient.
2. Which network data sources does Network Copilot 1.0 ingest and analyze?
Network Copilot 1.0 ingests metric data collected via SNMP and ONES Collector from multiple NOS types, including Arista EOS, SONiC, Cumulus, and Cisco NX-OS. The data is standardized for multi-vendor on-prem deployments, enabling comprehensive network analysis across diverse infrastructures.
3. How does Network Copilot help with capacity planning and resource forecasting?
Network Copilot 1.0 compares available versus utilized bandwidth and analyzes traffic trends using graphical tools like pie charts and timeline graphs. This helps administrators forecast network capacity needs, plan infrastructure upgrades, and optimize resource utilization to meet future demand.
4. Can Network Copilot 1.0 detect and alert on network anomalies or failures?
Yes, Network Copilot is trained to detect anomalies such as sudden CPU spikes, memory overutilization, and traffic surges that could lead to link or control plane failures. It enables proactive troubleshooting by identifying network risks early and providing insights to prevent service disruptions.
5. How does Network Copilot 1.0 ensure data security during AI-based network operations?
Network Copilot ensures secure interactions using HTTPS certificates and Secure APIs. It safeguards customer data during ingestion, processing, and retrieval, ensuring encrypted communication between the AI models and network telemetry databases while maintaining user-controlled compliance settings.
Envision a world where network infrastructures stack and its management is AI-driven, data-centric, flexible, cost effective and transformative as the most ambitious technological endeavors of our time. That’s precisely the future Aviz is steering the networking toward with the launch of Network Copilot. This isn’t merely a product launch; it represents a fundamental reimagining, propelling us into what we’re dubbing the Networking 3.0 era.
The Rise of Network Copilot
Network Copilot transcends the typical tech offering. It’s a conversational AI crafted to meet the complex demands of modern network infrastructures. Its design is LLM agnostic, ensuring seamless integration without disrupting your current systems, and doesn’t demand a PhD in data science to get started. Engineered with enterprise-grade compliance at its core, it offers not just power but also reliability and security.
It begins its learning process with the customer’s expectations – their unique challenges, context and thresholds. Through ongoing network data training, Network Copilot reshapes network management, providing innovative solutions in compliance, capacity planning, troubleshooting, and more. The core of its innovation is the “Prompt Engineer as a Service” feature, which allows users to customize use cases to their specific needs. This ensures that Network Copilot not only meets but also anticipates the evolving needs of its users, setting a new standard for customization and efficiency in network infrastructure management.
Redefining the Status Quo
The landscape of network operations has long been mired in outdated methodologies. Despite the potential for AI and automation to revolutionize industries, network management has remained entrenched in inflexible, template-driven solutions.
Recent attempts at innovation through Network AI Operations (AIOps) platforms have fallen short, offering costly, proprietary systems that constrain rather than liberate.
Network Copilot shatters these old paradigms, embracing open-source technologies and the spirit of community-driven innovation. This approach not only champions data privacy and breaks free from vendor lock-ins but also ignites a rapid pace of innovation, delivering a solution that’s both cost-effective and built to last.
Beyond the Horizon: Networking 3.0
Networking 1.0
Networking 2.0
Networking 3.0
Vendor Dependency
High
Medium
Low
Network Management
Vendor Tools Only
Template Driven IAC Or Observability
AI Driven Copilot while enabling templates for pragmatic transition
Support
Vendor Dependent
Vendor Siloed
Vendor Agnostic single pane of support
Cost to Customers
$$$$$
$$$
$
The Evolution of Networks – How they are Made and Managed
Network Copilot doesn’t just aim to be different; it seeks to redefine the playing field by embracing the Networking 3.0 Stack. This innovative framework simplifies the complexities of networking components like ASICs, NOS, and switches; fostering a universal approach to network management. This vision is supported by Aviz’s strategic partnerships throughout the technology ecosystem, enabling networks to not just react to the future but actively shape it.
Ushering in the New Era of Networking
So, what does this shift to Networking 3.0 look like in practice? It’s a ready-to-deploy recipe where networks are standardized yet infinitely customizable, thanks to vendor-agnostic operational layers integral to the Networking 3.0 Stack. It’s an AI reality where GenAI not only automates tasks but also anticipates needs, creating insights and solutions on the fly. Finally, it’s a service model that guarantees you’re never alone, providing 24/7 support from the brightest minds in the field, ready to address any challenge that arises.
The Future Is Now
With Network Copilot, we’re not merely introducing a new product. We’re kickstarting a movement towards an open, cloud, and AI first network infrastructure. This is about laying the groundwork for not only immediate gains in efficiency and cost savings but also establishing a robust foundation for whatever innovations the future holds.
Welcome to the era of Network 3.0, where your network’s potential is boundless, driven by open source innovation, defined by flexibility, and distinguished by the empowerment it offers through choice, control, and cost efficiency. The future of networking isn’t on the horizon; it’s here, and with Network Copilot, it’s more vibrant—and promising—than ever.
1. What is Aviz Network Copilot and how does it redefine network infrastructure management?
Aviz Network Copilot is a vendor-agnostic, LLM-powered conversational AI designed to revolutionize network management. It empowers enterprises with AI-driven compliance, troubleshooting, capacity planning, and operational customization, ushering in the Networking 3.0 era — where flexibility, cost-efficiency, and proactive innovation drive network infrastructure.
2. What is the Networking 3.0 era and how is it different from Networking 1.0 and 2.0?
Networking 3.0 represents the next evolution of network management moving from vendor-dependent tools (Networking 1.0) and template-driven solutions (Networking 2.0) to AI-driven, vendor-agnostic, highly customizable infrastructures. It reduces operational costs, eliminates lock-ins, and enables real-time intelligent automation using open technologies.
3. How does Network Copilot enable AI-driven customization without vendor lock-in?
Network Copilot uses open-source technologies and offers a “Prompt Engineer as a Service” feature, allowing enterprises to customize AI models, use cases, and operational workflows. This flexibility ensures companies can tailor the system to their needs without being tied to a single vendor’s roadmap or ecosystem.
4. How does Network Copilot improve cost efficiency compared to traditional AIOps platforms?
Unlike costly, proprietary AIOps platforms, Network Copilot leverages open-source frameworks and LLM-agnostic architecture, reducing operational costs significantly. It offers a low-cost, high-impact solution with enterprise-grade compliance and support, democratizing advanced AI-driven networking for organizations of all sizes.
5. What key components make up the Networking 3.0 stack enabled by Network Copilot?
The Networking 3.0 stack simplifies the management of complex networking layers like ASICs, NOS, and switches through a universal, vendor-neutral operational layer. Powered by AI and open standards, it integrates real-time observability, proactive automation, and multi-vendor support into a seamless, future-ready framework.
The rise of generative AI frameworks based on large language models is transforming the dynamics of human-computer interactions within data-driven applications. Across various industry verticals, Copilots are being introduced to facilitate collaborative relationships between operators, practitioners, and decision-makers through interactive conversational prompts with intelligent systems. Networking is undergoing a similar transformation, given the evolution of data centers to meet the demands of advancing technology and the increased reliance on AI-driven computing. Enterprise and cloud data centers generate extensive operational and application data, offering significant visibility. Aviz envisions leveraging the capabilities of Large Language Models (LLMs) to distill this vast amount of data from Edge, Data Center, and Cloud Network Infrastructure into actionable business-driven summaries.
Network Copilot
Aviz Network Copilot stands as the industry’s pioneering vendor-agnostic Generative AI Solution, harnessing the capabilities of open-source Large Language Models (LLMs) to efficiently process, correlate, and simplify the intricate demands of networks. It is tailored for decision-makers, network research engineers, and data center operators. Network Copilot is driven by our ONES (Open Network Enterprise Suite) multi-vendor, multi-NOS data mobility platform, serving as the backend infrastructure for data ingestion, aggregation, and enrichment across diverse datasets, including Network State, Performance, and Application data.
Life Cycle: Bring AI to your Networks
Network Copilot brings a practical understanding of generative AI to every networking professional, fostering innovation in defining and customizing Large Language Models (LLMs) for their specific use cases within a multi-vendor ecosystem. This process ensures a tailored approach without compromising on operational efficiency and network security. The journey begins by outlining use cases, selecting the appropriate model, followed by training and tuning to align with contextual expectations, ultimately deploying with confidence.
Focussed Use Cases
With Network data sets varying from hardware inventory to application visibility, our focus is geared towards the following critical use cases.
Network Compliance: Providing insights and recommendations for compliance management tasks, the Copilot ensures that network devices align with business expectations. This covers aspects such as security compliance, resource utilization, and end-of-life hardware inventory. Administrators are promptly alerted to any deviations from best practices.
Forecasting & Capacity Planning: Support the network architecture team in strategically forecasting and overseeing the resources needed to meet current and future demands on the network infrastructure. This ensures the data center network operates optimally, delivering performance, scalability, and efficiency.
On-Demand Business Intelligence Analytics: Unlike traditional applications that come with pre-packaged analytics use cases, Network Copilot helps deliver ad-hoc business intelligence analytics that suit the spectrum of end users ranging from network practitioners on one hand to decision makers on the other hand.
Insights on Network State Anomalies: Leveraging its generative AI capabilities, the copilot excels in identifying anomalous behaviour within the network. This includes providing answers on unusual traffic spikes, deviations from normal usage, or potential security breaches.
Troubleshooting & Optimization Assistance: Assist NetOps and support teams in streamlining troubleshooting and day-2 operations by providing extensive visibility into network state information and traffic flows. Identify areas of concern and suggest remedial actions to ensure compliance with established standards.
Network Copilot is Ready
Network Copilot offers a vendor-agnostic data platform, allowing customers to store and process network-generated and network-related data through standard interfaces. This establishes an optimal foundation for Copilot to seamlessly integrate Gen-AI into your networks.
Network Copilot is specialized in providing conversational insights into the operational state and compliance metrics derived from a diverse array of network switches within the organization’s network infrastructure. The real-time information gathered by the application at scale encompasses:
Inventory: Static information about network devices and servers, including uptime, platform-specific or vendor-specific data, software versions (Network Operating System, Linux Distribution), number of operational ports and speed.
Network Health: Time-series-based operational data, including the operational status of devices and links, platform or system resource utilization (CPU, memory, etc.).
Network Traffic: Periodic percentage utilization of network bandwidth versus available bandwidth, traffic rate (packets/sec), drops due to network errors, congestion, and unintentional discards.
Copilot offers a Chat-GPT-like experience, delivering responses based on its extensive training. Users are empowered to save and export new questions tailored to specific customer use cases, fostering collaboration with the Aviz team to refine responses. Operating on a hybrid approach, combining fine-tuned and retrieval augmented generation (RAG), Copilot requires some supervision in context delivery. Users can dynamically load customer-specific context or business expectations into the application to enhance responses related to compliance. Network Copilot is adaptable and can be deployed as a standalone application on On-Prem, supporting commodity servers with NVIDIA GPUs for efficient responses, or as a service in the Cloud. See below for hardware and software requirements
Upcoming…
Expand on Data Sets
Network Performance: Network Copilot will enhance existing functionalities related to network performance metrics, offering conversational insights into the efficiency and reliability of networks in alignment with service level agreements (Network SLA). This encompasses datasets related to network latency, packet loss, and the reachability of services across the network.
Application Compliance: Automated insights into application metrics will assist network practitioners in identifying primary application consumers and top talkers. It enables an understanding of the predictability of application-specific network usage, facilitating the detection of anomalies and security risks associated with application data. The datasets encompass application identification for both data center and mobile core networks.
Cloud Ready: The Network Copilot model will undergo improvements to accommodate cloud network datasets, with a focus on application compliance, performance, and usage. This enhancement involves gathering diverse data through standardized APIs, logs, and importing from cloud storage.
Improvise Model for Customers
As Network Copilot receives feedback from customers, the content—comprising network state, performance and application datasets, along with contextual feedback collected from various environments—will facilitate the refinement of pre-trained large language models and the persistence of custom models. This process enables domain adaptation and adjustment to the network supportability domain. Exploration of Unsupervised RAG Approaches at Scale: Initiatives will focus on delving into unsupervised, purely data-driven, multimodal retrieval of contextually relevant information from both internal and external sources, encompassing both static and dynamic data.
FAQs
1. What is Aviz Network Copilot and how does it use AI for network operations?
Aviz Network Copilot is a vendor-agnostic Generative AI platform designed to optimize network operations. It uses open-source Large Language Models (LLMs) to process, correlate, and summarize complex network data providing actionable insights into network compliance, performance, anomalies, and business intelligence for enterprises and cloud data centers.
2. How does Network Copilot enhance network compliance and operational security?
Network Copilot offers AI-driven monitoring that alerts administrators to deviations from compliance best practices, security vulnerabilities, end-of-life hardware issues, and inefficient resource usage. It proactively identifies network risks and provides recommendations to maintain alignment with business and regulatory standards.
3. What are the key use cases Network Copilot addresses in enterprise and cloud environments?
Network Copilot focuses on several critical use cases:
Network compliance management
Forecasting and capacity planning
On-demand business intelligence analytics
Real-time anomaly detection in network state
Troubleshooting and optimization of network performance and operations.
This helps organizations improve efficiency, resilience, and scalability of their networks
4. Can Aviz Network Copilot be deployed on-premises and in the cloud?
Yes, Network Copilot is highly flexible. It can be deployed as a standalone application on-premises using commodity servers with NVIDIA GPUs, or offered as a cloud-native service. This allows enterprises to choose the deployment model that best fits their operational and security requirements.
5. How does Network Copilot integrate real-time network data for AI-driven insights?
Network Copilot uses the Aviz ONES platform for multi-vendor, multi-NOS data ingestion. It aggregates operational data such as device inventory, network health, traffic utilization, and compliance metrics. Using a hybrid fine-tuned LLM and RAG approach, it delivers real-time, conversational insights tailored to customer-specific network environments.
Network Copilot: The Gen-AI Gateway for Transforming Your Networks
The rise of generative AI frameworks based on large language models is transforming the dynamics of human-computer interactions within data-driven applications. Across various industry verticals, Copilots are being introduced to facilitate collaborative relationships between operators, practitioners, and decision-makers through interactive conversational prompts with intelligent systems. Networking is undergoing a similar transformation, given the evolution of […]
