NextraNet
Telecom Innovation Company
NextraNet’s AIOps-as-a-service Platform
5G and beyond networks are required to deliver services to a fast-growing array of non-human devices with varying requirements in terms of bandwidth, latency, and usage patterns. Telco operations to support such a heterogeneous set of services are therefore set to grow progressively more complex. To fulfill the demanding and complex service requirements, CSPs need to harness advantages of autonomous networks
By adopting proven cloud, automation, and intelligence best practices from the IT sector, CSPs can gain a competitive edge and dramatically accelerate their digital transformation journey. NextraNet’s AIOps-as-a -Service Platform is a holistic, AI-native framework that advances network operational capabilities using AI. An integral part of NextraNet’s solutions to enable autonomous 5G networks, from Generative AI and digital twin to deep learning – that act upon live 4G/5G network traffic in near-real-time to tangibly boost network performance and deliver an enhanced user experience.
Big Data
Machine
Learning
AIOps
We are building an AI-driven Automation Telco Platform which makes it possible to bring all main pillars of an AI ecosystem, namely, use cases, technology foundation, processes and teams via a converged operational platform together. At the core of this platform resides a technology stack which enables data readiness, AI-driven analysis and partnership (via API) features, which provides an operational suit for the Telco operators to run their internal AI workloads while having the profit of using externally developed workloads/applications through interaction with third party AI models in order to unlock the full potential of AI in different domains from B2B to IT and network.
This framework incorporates a suite of advanced machine learning algorithms which applies state of art AI/ML to telco operations, elevating problem-solving and decision-making capabilities.
Improve network operations productivity
Improve end user experience
Enforce service level agreements
Utilize network resources more efficiently
Deployment Framework and Key Characteristics
01
AI-oriented Cloud-native Architecture
An AI-oriented Cloud-native Architecture is designed to support scalable, flexible, and efficient deployment of AI solutions tailored to modern telecom networks. Built on a microservices-based framework, this architecture enables seamless integration of AI workloads and applications across various cloud environments, including public, private, and hybrid models. It leverages containerization tools like Kubernetes and Docker to ensure portability and consistency, while its serverless capabilities allow dynamic scaling based on workload demands.
This architecture supports the modular deployment of AI components, such as machine learning models, data pipelines, and analytics tools, ensuring optimized resource utilization and reduced operational overhead. Additionally, it facilitates low-latency data processing and decision-making at the network edge, empowering use cases like anomaly detection, predictive maintenance, and traffic management. With inherent flexibility, it allows telcos to incorporate emerging technologies like 5G and IoT seamlessly, adapting to evolving business needs while maintaining robust security and compliance standards.
02
Workloads Lifecycle Management
Workloads Lifecycle Management plays a pivotal role in maintaining the efficiency, reliability, and scalability of AI-driven telecom platforms. By integrating GitOps as a core methodology, this management approach ensures streamlined deployment, monitoring, and version control of AI workloads. GitOps leverages a declarative model where the desired state of the system is defined in Git repositories, serving as the single source of truth.
This methodology minimizes human intervention, reducing the risk of errors while ensuring consistency across environments. It also supports rapid iteration and rollback capabilities, enabling telecom operators to adapt quickly to evolving operational demands.
In the context of telecom AI workloads, GitOps facilitates lifecycle management by automating resource allocation, monitoring workload health, and scaling applications dynamically. It aligns with cloud-native principles, empowering operators to efficiently manage workloads across on-premise, edge, and hybrid cloud deployments while maintaining robust governance and operational transparency
Deployment Framework and Key Characteristics
03
Deployment Models
Deployment models for AI-driven telecom networks provide flexibility in adopting cloud-native architectures based on operational requirements and business goals. These models include Local Cloud (on-premise), Edge Cloud, and Hybrid Cloud, each tailored to meet specific needs in terms of latency, scalability, and control.
Local Cloud (on-premise) deployment is ideal for organizations requiring complete control over data and infrastructure. It ensures compliance with stringent data privacy regulations and provides high security by keeping sensitive information within the organization’s boundaries. This model is suitable for environments where low latency and dedicated resources are critical, such as mission-critical network management.
Edge Cloud deployments bring computation and AI workloads closer to the data source, reducing latency and enabling real-time decision-making. By processing data at the network edge, this model supports use cases like traffic prediction, anomaly detection, and autonomous network operations in highly distributed environments. Edge Cloud is particularly valuable in managing 5G and IoT applications where rapid data processing is essential.
Hybrid Cloud combines the strengths of on-premise and cloud-based deployments, offering a balanced approach to scalability, cost-effectiveness, and data control. Organizations can leverage the public cloud for resource-intensive AI workloads while maintaining sensitive operations on-premise. This model supports seamless integration between different environments, enabling telcos to scale AI-driven solutions dynamically while ensuring business continuity and operational efficiency.
These deployment models empower telecom operators to adapt their infrastructure to diverse business needs, ensuring efficient and secure implementation of AI-powered solutions across varying scales and locations.
Supported Features and Capabilities
Converged Services and Integrated Solutions
Our Open AI Telco Platform unlocks all main pillars of an AI ecosystem, namely, use cases, technology foundation, processes and teams via a converged operational platform. At the core of this platform resides a technology stack which enables data readiness, AI-driven analysis and partnership (via service layer) features, which provides an operational suit to serve external AI workloads and interaction with third party AI models. Moreover, the service layer confers the possibility of designing workflows to realize AI-driven decision-making processes, while building the collaboration structure based on the policy-oriented guidelines of the deployment framework.
Flexibility and Extensibility
Flexibility and extensibility are achieved through an open API gateway, which enables seamless integration of new services and tools into existing telecom ecosystems. Open APIs foster interoperability by allowing external applications and third-party AI models to interact with the platform. This extensibility supports rapid innovation by enabling the addition of use case-specific workloads, advanced analytics tools, and AI-driven decision-making capabilities. Open architecture ensures that telecom operators can customize their platforms to meet evolving business requirements and adopt emerging technologies like machine learning frameworks, visualization tools, and network automation software.
Security
Security in AI-powered telecom platforms is a critical component that ensures the protection of data, infrastructure, and operations. By implementing advanced encryption methods, multi-factor authentication, and access control policies, these platforms safeguard sensitive information from cyber threats. Additionally, AI-enhanced threat detection systems continuously monitor network activities to identify anomalies and potential breaches in real-time. This proactive approach not only prevents unauthorized access but also ensures compliance with industry standards and regulations, providing telecom operators with a robust foundation for secure, reliable AI implementations.
Supported Features and Capabilities
Automation Workflow Design
Low/no-code workflow design simplifies the creation and deployment of AI-driven processes, enabling telecom operators to innovate without extensive technical expertise. By utilizing intuitive drag-and-drop interfaces and pre-built AI components, non-technical users can design workflows that address specific operational needs, such as automating routine tasks or optimizing resource allocation. This approach democratizes access to AI technologies, accelerating implementation cycles while reducing development costs. Furthermore, it fosters collaboration across teams, empowering business units to create and manage workflows tailored to their unique objectives.
Central Governance and Access Management
Experience and governance are centralized, but scaling is decentralized across business units. A democratized model is adopted with an admin leading the AI enablement group with a democratized approach guiding business units to scale AI use cases.
Scalability
Scalability, enabled by a cloud-native architecture, ensures that telecom AI platforms can adapt to increasing workloads and growing operational complexity without compromising performance. By leveraging containerized environments and orchestration tools like Kubernetes, these platforms can dynamically allocate resources to meet real-time demands. This approach allows telecom operators to scale AI models and data processing pipelines seamlessly across on-premise, edge, and hybrid cloud environments. Moreover, the modular design of cloud-native architecture ensures that new services and functionalities can be integrated without disrupting existing operations, supporting the rapid growth of 5G networks and IoT deployments.
Driving Value for CSPs
NextraNet delivers tailored, scalable open AI service platform solution that integrate advanced technologies to enhance operational efficiency, reduce costs and provide data-driven insights for better decision-making. Complementing these benefits, this solution drives digital transformation, manage risks and ensure compliance, empowering CSPs to navigate the evolving telecom landscape.
Tailored solutions for complex needs: NextraNet address specific pain points, helping CSPs meet their operational and business goals.
Innovative technology integration: AI, machine learning, and automation are incorporated to boost network performance, predict and resolve issues proactively, and optimize service delivery.
Scalability and flexibility: NextraNet solution grows with the network, ensuring CSPs can handle increasing complexity without sacrificing service quality or efficiency, especially as they expand services and adopt new technologies like 5G and IoT.
Operational efficiency and cost reduction: NextraNet’s platform reduces manual efforts, lower costs and minimize errors, allowing CSPs to focus on strategic growth instead of day-to-day operational challenges.
Data-driven insights and decision support: Advanced analytics from NextrsNet’s embedded services provide real-time insights, empowering CSPs to make informed decisions and optimize resource allocation.
Support for digital transformation: NextraNet assists CSPs in adopting new business models, improving customer engagement and driving innovation.
Risk management and compliance: NextraNet’s solution ensures secure, resilient networks that comply with industry standards, reducing the risk of penalties and enhancing network reliability.
